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Am J Hum Genet. Jun 2006; 78(6): 1011–1025.
Published online Apr 25, 2006. doi:  10.1086/504300
PMCID: PMC1474084

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Abstract

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

The completion of various genome-sequencing projects and large-scale genomic studies has led to a wealth of available biological data. It is anticipated that this information will revolutionize our insight into the molecular basis of most common diseases by making it easier and quicker to identify genes with variants that predispose to disease (i.e., disease genes). At the moment, we are faced with many disease susceptibility loci, resulting from linkage or cytogenetic analyses, that cover extensive genomic regions. Usually, when the genes in these loci are assessed, positional candidate genes become apparent that can be linked to the phenotype being studied on the basis of their biological function.

However, the most obvious functional candidate gene from a disease locus does not always prove to be involved in the disease.e.g.,15 Often, genes that would not have been predicted to be disease causing prove to be the true disease gene—for example, the BRCA1 gene in early-onset breast cancer.6 Moreover, although these disease genes might have been assigned biological functions, it is not always evident how these functions relate to disease. Finally, genes with unknown functions are often overlooked, as attention is paid only to well-studied genes for which functions and interactions have been identified or implicated, some of which can be related to the disease pathogenesis. For example, in Fanconi anemia, at least 10 disease genes were identified,7 but only a few had a known function. However, follow-up research810 revealed that five of those genes function in the same protein complex. Another example is limb-girdle muscular dystrophy, in which many of the disease genes encode for proteins that are part of the dystrophin complex.11 This emphasizes the importance of taking an unbiased approach to assessing positional candidate genes.

Faced with the absence of complete functional information for the majority of genes in susceptibility loci, it is difficult to prioritize the positional candidate genes correctly for further sequence or association analysis. However, high-throughput genomic work has now yielded relatively unbiased genomewide data sets1215 that comprise known metabolic, regulatory, functional, and physical interactions. There is, however, little integration of these diverse data sets into a coherent view of possible gene and protein interactions that can be used to investigate relationships between genes in different genetic loci. We have tried to address this problem by developing a functional human gene network that comprises known interactions derived from the Biomolecular Interaction Network Database (BIND),12 the Human Protein Reference Database (HPRD),13 Reactome,15 and the Kyoto Encyclopedia of Genes and Genomes (KEGG).14

Since these data sets contain a limited number of known interactions, we implemented a Bayesian framework to complement these relationships with a large number of predicted interactions by relying on evidence for putative gene relationships based on biological process and molecular function annotations from the Gene Ontology database (GO).16 We further incorporated experimental data—namely, coexpression data derived from ~450 microarray hybridizations from the Stanford Microarray Database (SMD)17 and the NCBI Gene Expression Omnibus (GEO),18 along with human yeast two-hybrid (Y2H) interactions19 and interactions based on orthologous high-throughput protein-protein interactions from lower eukaryotes.20

Our interaction network was then used to test whether we could rank the best positional candidates in susceptibility loci on the basis of their interactions, assuming that the causative genes for any one disorder will be involved in only a few different biological pathways. This would be apparent in our network as a clustering of genes from different susceptibility loci, resulting in shorter gene-gene connections between disease genes than one would expect by chance (fig. 1). Our method (called “Prioritizer”) analyzes susceptibility loci and investigates whether genes from different loci can be linked to each other directly21 or indirectly.22 When we constructed artificial loci of varying size around susceptibility loci from 96 different genetic disorders (each containing at least three loci) and used Prioritizer in our most comprehensive gene network to rank the positional candidate genes for each locus, we were able to significantly increase the chance of detecting disease genes.

Figure  1
Basic principles of the prioritization method for positional candidate genes with the use of a functional human gene network. The method integrates different gene-gene interaction data sources in a Bayesian way (left panel). Subsequently, this gene network ...

Material and Methods

Functional Gene Network Reconstruction

As a basis for the gene network, we used annotations from Ensembl,23 version 32.35, resulting in 20,334 known genes that physically map within the autosomes or chromosome X or Y. This yielded 206,725,611 potential gene-gene interactions.

On the basis of this set of genes, a comprehensive “gold standard” set of validated direct gene-gene relationships (true positives) was determined using both BIND (September 15, 2005) and HRPD (September 15, 2005) to extract human, curated protein-protein interactions, the proteins of which were mapped to Ensembl gene identifiers. In addition, all human pathways from Reactome (September 15, 2005) and KEGG (September 15, 2005) were used to derive direct interactions that were of transcriptional, physical, or metabolic origin, since pathways are usually composed of genes and proteins that interact with each other in various ways. We chose to allow interactions of physical, metabolic, and regulatory origin to be included within our network, because, for instance, mutations in either one of two genes encoding proteins in the same metabolic pathway or protein complex could lead to the same disease phenotype.

Because the true-positive gold standard only describes a limited number of relationships between a limited number of genes, we also used data from GO, coexpression data derived from microarray experiments, conserved protein-protein high-throughput data, and human Y2H interaction data to predict interactions of the remaining gene pairs. We used a Bayesian classifier, because these four types of data were of varying reliability and only contained information about a subset of the data. The classifier allows for combining dissimilar data sets, can deal with missing data, and uses conditional probabilities that can be well interpreted and that control for the varying reliability of the data sets.2429

Training of Bayesian Classifier on Gold Standard

For the prediction of interactions, we used a Bayesian classifier type that assumed all data sets had been binned. This operation was performed for each gene pair, and it determined, for each data set, to which bin the pair belongs. Because the number of bins per data set was limited, each bin contained many gene pairs. Subsequently, for each bin, we determined the likelihood ratio between the proportion of gene pairs known to interact and the proportion of gene pairs known not to interact. This measure indicates whether there is an over- or an underrepresentation of truly interacting gene pairs in the bin, which specifies the conditional probability estimates of the Bayesian classifier; thus, training of the classifier is straightforward.

However, to be able to train the classifier by determining likelihood ratios of sets of gene pairs, it was crucial that the gold standard, containing the aforementioned well-defined set of curated true-positive gene pairs, be complemented with a set of gene pairs for which there is strong evidence that they, or the proteins they encode, do not functionally interact (true negatives). As has been discussed by others,30 the construction of this true-negative reference set is problematic, because it is impossible to be certain that two genes (i.e., their protein products) do not interact. However, by assuming that genes encoding for proteins localized within different cellular compartments are, in general, unrelated, it is possible to make a list of gene pairs that are unlikely to interact. The GO Cellular Component annotations were used to yield groups of gene pairs that have exclusive cellular component annotations. To overcome a strong selection bias in the classifier toward well-annotated genes (details provided in appendix A [online only]), only the 5,105 genes that were part of a true-positive gene pair at least three times were allowed to form true-negative gene pairs. We chose combinations of cellular organelles that were highly underrepresented (χ2=2,490; P<10-10) within the true-positive set, which resulted in gene pairs for the following combinations: nucleus and extracellular matrix, protein complex and Golgi apparatus, protein complex and Golgi stack, non–membrane-bound organelle and Golgi stack, non–membrane-bound organelle and extracellular space, non–membrane-bound organelle and Golgi apparatus, extracellular region and organelle membrane, mitochondrion and extracellular matrix, extracellular space and organelle membrane, extracellular space and Golgi stack, organelle membrane and extracellular matrix, extracellular matrix and Golgi stack, extracellular matrix and ubiquitin ligase complex, and ubiquitin ligase complex and Golgi stack.

Preprocessing and Binning of Data Sets

To allow for Bayesian integration, the GO data, microarray coexpression data, and orthologous and human protein-protein interactions data were preprocessed and binned. Biological Process and Molecular Function GO annotations were derived from Ensembl, and two measures of relatedness for each of the two data sets were determined, resulting in a total of four different GO measures of relatedness. First, we determined, for each Biological Process GO term, how many of the genes had been assigned this term. Then, we determined which Biological Process GO terms were shared between the two components of each gene pair, for all the pairs. This led to the shared GO term that was annotated in the least number of genes, and its frequency of occurrence was used as a measure. GO terms GO:0000004 (biological process unknown) and GO:0005554 (molecular function unknown) were discarded, since genes that shared either of these highly unspecific terms should not be related to each other on the basis of this information. The same procedure was performed to generate the first measure of Molecular Function GO relatedness.

The second measure determined the maximal hierarchical depth at which a gene pair shared a Biological Process GO term. This hierarchical depth was defined as the shortest number of branches necessary to go from one Biological Process GO term back to the GO root. The same method was used to generate the maximum hierarchical depth of the Molecular Function GO sharing measure.

Coexpression between genes was determined in microarray data sets from GEO and SMD. Individual data sets comprised an experiment that contained at least 10 hybridizations. To ensure that the quality of the intensity measurements was reliable, various filtering steps were performed to exclude spots with low signal-to-noise ratios.31 Within the SMD data sets, intensity spots were filtered out that were either missing or contaminated, and the mean intensity of spots had to be at least 2.5 times higher than the average background signal of the microarray. Since GEO contains both ratiometric and Affymetrix single-spot intensity microarray data sets, we used different filtering strategies. The 5% of genes with the lowest maximal intensity were removed from the Affymetrix data sets. For both SMD and GEO, expression ratios were log2 transformed. Microarray features missing [gt-or-equal, slanted]25% of expression measurements in a data set after filtering were excluded. All features were assigned Ensembl gene identifiers by comparing their sequences to Ensembl transcripts with the use of SSAHA.32

To determine which gene pairs showed coexpression, the mutual information was calculated between all the genes represented within each data set33 if there were at least 10 nonmissing data points. As a preprocessing step, expression levels were ranked; this invertible reparameterization did not affect the mutual information. Next, for each pair of genes, the joint distribution of expression levels was estimated by calculating a histogram with overlapping windows. The range was divided into six windows, where each window extends to the center of the next window. The number of windows was chosen by optimizing the error rate for the mutual information derived from analytical probability densities.33 In this way, each data point contributes to two windows, except at the extremities. Finally, on the basis of the resulting distribution, the mutual information (MI) between each pair of genes was calculated as MI(A,B)=H(A)+H(B)-H(A,B), where H(X) is the information-theoretic Shannon entropy.34 For each microarray data set, the MI score was binned. This allowed the subsequent Bayesian classifier to determine the likelihood ratio, indicating whether gene pairs within each bin contained an overrepresentation of truly interacting gene pairs. Once the likelihood ratios had been determined for each data set, a receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated. Data sets that had a minimal AUC of 0.59 were combined in a naive way—for each gene pair, the likelihood ratios were multiplied by each other, resulting in a final microarray coexpression likelihood ratio for each gene pair.

Two orthologous protein-protein interaction data sets from Lehner and Fraser20 were used to supplement the GO and microarray coexpression data. One data set contained computationally predicted human protein interactions that had been physically mapped within Ensembl genes. The second data set contained a subset of these protein pairs, to which Lehner et al. had assigned a higher confidence. Three bins were constructed: one containing the higher-confidence gene pairs, one containing the remaining lower-confidence pairs, and a third containing all the other unobserved gene pairs.

A human Y2H protein-protein interaction data set from Stelzl et al.19 was integrated by mapping the HUGO identifiers to Ensembl genes. Two bins were constructed: one containing the gene pairs for which a Y2H interaction was reported, and one containing all the other unobserved gene pairs.

Network Integration

The Bayesian classifier was employed to integrate the various binned types of data. We chose not to learn the Bayesian network structure from the data but to use a predefined Bayesian network structure, for which the conditional probabilities were determined by benchmarking the various data sets against the gold standard (fig. 2) (details provided in appendix A). We subsequently generated four gene networks. One network contained evidence for interaction based on the GO data (GO network). Another network contained evidence for interaction derived from integrating the microarray coexpression and predicted protein-protein interaction data in a naive way (MA+PPI network). A third network combined, in a naive way, the GO and MA+PPI networks (GO+MA+PPI network), and this was complemented with all known true-positive interactions in a final network (GO+MA+PPI+TP network). To relate interacting genes directly or indirectly, an all-pairs shortest path was calculated for each gene network.35 This measure of the minimal path length between pairs of genes was used in the subsequent method to associate disease genes with each other.

Figure  2
Integration of data sets in four gene networks. a, Data sets were benchmarked against a set of 55,606 known true-positive gene pairs derived from BIND, KEGG, HPRD, and Reactome and 800,608 true-negative gene pairs derived from GO. The Venn diagram indicates ...

Disease Analysis and Positional Candidate-Gene Prioritization

Prioritizer assesses whether genes residing within different susceptibility loci are close together within the gene network. This indicates that this method could also work with diseases for which only two loci have been identified. However, in such a case, there is a considerable probability that two genes, each residing in a different locus, would interact by chance. We therefore restricted the analysis to diseases for which at least three contributing disease genes had been identified. These diseases and disease genes were derived from the Online Mendelian Inheritance in Man (OMIM) database,36 by text mining the first paragraphs of all OMIM disease entries as of March 1, 2005, and extracting the OMIM gene numbers contained within these paragraphs (table A1 in appendix A). The HUGO gene name was later extracted from these OMIM entries and was mapped to an Ensembl gene name. If, for any one disease, there were two disease genes situated at the same chromosome and positionally <200 genes apart, one of the two genes was randomly removed to ensure that no loci would overlap.

The diseases for which at least three disease genes remained after filtering were analyzed by artificially generating susceptibility loci around the disease genes, in a range from 50 to 150 genes, in steps of 50. All 20,334 genes were assigned an initial effect score of zero, and, subsequently, all loci were traversed. Using each gene network for all positional candidate genes residing in a particular locus, we determined whether any of these genes were functionally closely related to genes physically residing inside another susceptibility locus. If this was the case, the effect score of the related gene that was functionally close but physically in another locus increased (fig. 1), by use of the following Gaussian kernel scoring function:

equation image

where “distance” is defined as the all-pairs shortest path between the two genes. The kernel function width was chosen arbitrarily, but a sensitivity analysis showed that different widths did not influence the results much (data not shown). By applying this function, positional candidate genes that resided in different loci but that were functionally closely related in the gene network were assigned higher scores than positional candidate genes that were functionally far apart from each other. To correct for differences in topology of the gene network, an empiric P value was determined for each positional candidate gene through permutation of the other loci 500 times by reshuffling them across the genome and recalculating the effect scores. This permitted a probability density function to be determined per positional candidate gene, for which the empiric P value could be looked up. For each locus, the positional candidate genes were prioritized on the basis of this P value.

Results

Construction of a Functional Gene Network

The basis for our human gene network was a gold standard of validated gene-gene interactions (true positives) and a further set of gene-gene pairs that were deemed highly unlikely to interact (true negatives). To construct the set of true-positive gene pairs, 2,788 confirmed, direct, physical protein-protein interactions were derived from BIND; 18,176 confirmed human protein interactions were derived from HPRD; 22,012 direct functional interactions were derived from KEGG; and 16,295 interactions were derived from Reactome. This resulted in 55,606 unique true-positive gene relationships (fig. 2a). For the true-negative set, gene pairs were selected that encode for proteins localized in different cellular compartments. The combinations of cellular compartments were selected from their underrepresentation in the set of true positives (see the “Material and Methods” section). This resulted in 801,108 pairs, of which 500 were known to be true-positive gene pairs, and these were therefore removed from the set of true-negative gene pairs.

We trained the classifier on this gold standard and constructed functional human gene networks on the basis of GO data, microarray coexpression data, and inferred protein-protein interactions, as well as combinations of these. First, for each gene pair, we assessed whether the genes shared GO annotations, which were derived for 15,045 genes from Ensembl. Sharing of GO terms was based on the frequency of the least-common GO term shared between two genes and the maximal depth in the GO hierarchy at which two shared terms lay. Gene coexpression was calculated in 186 microarray data sets derived from GEO and 75 data sets from SMD. However, most of these data sets were not highly informative, as judged by their ability to identify true-positive gene interactions with a low false-positive rate. Because it is known that many classifiers perform best when a subset of features are used,37,38 we used only four informative microarray coexpression data sets for classification,3942 each showing a minimal AUC of 0.59. In total, these data sets contained 461 microarray hybridizations. Finally, protein-protein interactions were derived from the Lehner and Fraser20 data set containing human protein interactions predicted by mapping physical protein interactions from various Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans interaction data sets to orthologous human gene pairs. Of the 71,806 predicted gene pairs, we were able to physically map 62,635 gene pairs with both genes in the pair mapping to known Ensembl genes. A subset was defined by Lehner and Fraser20 that contained 10,652 gene pairs deemed to be of higher confidence, of which 10,139 gene pairs could be mapped. In addition, we used 3,186 human protein-protein interactions identified by automated Y2H interaction mating by Stelzl et al.,19 of which 1,751 could be mapped to different Ensembl gene pairs.

We assessed the performance of our classifier on the basis of these various data sources in three different gene networks generated on the basis of a Bayesian framework, after preprocessing and binning of the data sets. As mentioned above, one network was generated solely on the basis of GO data (GO network), one network was based on both microarray coexpression and predicted protein-protein interaction data (MA+PPI network), and an overall network contained all three types of data (GO+MA+PPI network). ROC curves (fig. 3) show the performance of the reconstructed GO, MA+PPI, and GO+MA+PPI gene networks, which were constructed by cross-validating all data sets 10 times against the gold standard set, to mitigate overfitting (details provided in appendix A). When we compared the performance of the various gene networks, it became evident that the GO data set provided the most accurate evidence for interaction. The AUC was 88%, compared with 50% for an uninformative classifier. The ROC for the MA+PPI network shows that coexpression data derived from microarray expression, in conjunction with the orthologous protein-protein interaction data, correctly inferred functional interactions (AUC=68%), but to a lesser extent than the GO network. Nevertheless, as can be deduced from the GO+MA+PPI network, addition of the microarray coexpression and the orthologous protein-protein interaction data to the GO network improved slightly the accuracy of the network (AUC=90%). In accordance with most networks described in the literature thus far,43 our reconstructed networks have a connectivity that follows a scale-free power-law distribution, which has also been demonstrated for other organisms.4446 This is most apparent when the topology of the MA+PPI network is assessed (see appendix A).

Figure  3
ROC curve of the GO network, the MA+PPI network, and the combined GO+MA+PPI network. The baseline (solid gray line) indicates the performance of a classifier that would be totally uninformative.

To validate our network, we used a list of 2,574 Y2H interactions that recently became available47 to assess whether our gene network had predicted an interaction for these gene pairs. We first mapped the set to Ensembl pairs and then removed all pairs that were in our gold standard true-positive set, to ensure that we only assessed newly identified interactions. This resulted in a set of 1,318 novel gene pairs.

We then assessed whether our gene network had predicted an interaction for these pairs. While Y2H interactions are known to regularly yield false-positive results,48 we decided to test whether the distribution of likelihood ratios for these gene pairs was significantly different from a null distribution of 10,000 gene pairs sampled by generation of random gene pairs by selecting two genes at a time from the set of all individual genes that made up the Y2H gene pairs. The results show that the 1,318 Y2H gene pairs have a significantly higher likelihood ratio than the null distribution (P=.0003, by Wilcoxon Mann-Whitney test), which indicates that our gene network is capable of inferring as-yet-unknown interactions.

To allow researchers to look up known and predicted interactions and to identify the shortest routes between genes and susceptibility loci, we developed a Web tool, which is publicly available at the GeneNetwork Web site. The known and predicted interactions can be shown for each gene of interest, along with information about the source of evidence from which they were derived and how strong this evidence was. In addition, there are interactive graphs to visually explore how multiple genes interact with each other. All the data files (including the sets of true-positive and true-negative gene pairs) can be downloaded, along with a Java application programming interface, which can facilitate the development of new methods that use this gene network. Every 2 mo, we will update the gene network, on the basis of the most recent releases of the various repositories used in its construction.

Increased Functional Interactions Shown by Genes Associated with a Particular Disease

We first examined our hypothesis that genes associated with genetic disorders frequently share functional links, by assessing whether, for a disease, these causative genes were functionally more closely related to each other than a set of genes of equal size that were randomly selected from the full set of 345 unique disease genes of the 409 disease genes that were extracted from OMIM entries on disorders for which at least three causative genes were known. This set of disease genes was used as a background distribution to prevent bias, since the disease genes are generally better characterized than the complete set of genes in the network. We generated one extra network (GO+MA+PPI+TP network) that complemented the GO+MA+PPI network with all known true-positive gene pairs, and we calculated the shortest direct or indirect distance between all pairs of genes. In 76 (79%) of the 96 diseases, the total distance between all combinations of disease genes in one disease was, on average, lower than the total distance between all combinations of randomly selected disease genes in 10,000 permutations. This confirms our hypothesis that, in the majority of diseases, the causative genes are indeed closely related functionally.

Genes implicated in disease processes tend to be studied more than those not implicated, which could result in a bias in the gene network based on GO annotations, since these represent known functional annotations. To assess the degree to which this possible bias affected our gene network, we looked at network connectivity. The average number of direct interactions involving disease genes was 199, compared with an average of 203 for the other 11,875 genes that interacted with at least one other gene. This indicates that other genes are equally represented in the gene network, despite the fact that disease genes may have been studied more.

Increased Power to Detect Disease Genes Provided by a Functional Gene Network

Usually, researchers pick a limited number of candidate genes in susceptibility loci to follow-up, because it is too costly and labor intensive to analyze all the genes residing in these loci. As a result, these studies have a limited chance of finding disease-related variants, largely depending on the size of the loci and the number of genes selected. Using a test set of known disorders in a similar setup, we evaluated the ability of our reconstructed network to correctly prioritize positional candidate genes in a set of top-ranked candidate genes of typical size (5–10 genes). The test set consisted of 96 different disorders, for which a total of 409 disease genes (345 unique genes) had been identified. These were obtained from OMIM, with 3–10 disease genes per disease (average 4.3 genes per disease) (table A1 in appendix A). Of the diseases, 59 are of Mendelian origin, 17 have complex inheritance, and 20 are various types of cancer (table 1).

Table 1
Overview of the 96 Diseases Studied with Prioritizer and the Number of Disease Genes per Disorder That Ranked in the Top 10 Genes per Susceptibility Locus, With Locus Widths of 100 and 150 Genes

The ability of the functional human gene network to correctly prioritize known disease genes was assessed by creating artificial, nonoverlapping susceptibility loci around these disease genes. Since many genes in these loci have no known or predicted interactions in our network, we only assessed those genes for which interactions were predicted, to prevent a bias toward genes that were better represented in the underlying high-throughput data sets. This resulted in susceptibility loci of varying widths, containing 50, 100, or 150 genes, which were predicted to interact with at least one other gene. If, for any particular disease, two disease genes residing in the same chromosome yielded loci that were partly overlapping, one of the two loci was randomly removed.

For each locus, the genes were traversed, and, for each gene, we assessed whether there was another gene residing in a different locus that was nearby within the gene network. The effect scores (see the “Material and Methods” section) of each gene were affected by the gene in the other locus that had the shortest path to that gene. This procedure has the potential to preferentially identify genes with many interacting partners over genes that are less well connected, because a highly connected gene has a higher chance of interacting with a gene residing in another locus than a gene for which only a few interactions have been predicted. To overcome bias in the method toward genes that are highly connected, we corrected for differences in the network topology by permuting the susceptibility loci for each disease 500 times across the genome.

After all positional candidate genes were ranked on the basis of this permuted score, the results (see fig. 4 and table 1) indicated that this method was able to identify many of the disease genes in the top 5 or top 10 genes per locus. As expected from the ROC curves of the various gene networks (fig. 3), the performance of the MA+PPI network proved to be the least powerful. Nevertheless, the number of correctly ranked genes was higher than would be expected to occur by chance (fig. 4a and and44b; indicated by baseline) for many of the susceptibility loci widths. When assessing susceptibility loci that contained, on average, 100 genes, we found 8% and 12% of the disease genes were contained within the top 5 and top 10 per locus, respectively, compared with the 5% and 10% we would expect to find by chance. A lack of predictive performance of the MA+PPI network explains why the ranking did not improve considerably when this network was used, as is evident from inspection of the ROC curves (fig. 4c), which show the proportion of disease genes and nondisease genes that are returned when different sizes of sets of top-ranked genes per locus are assessed. For 86 of the 345 unique disease genes within the MA+PPI network, no interactions were predicted. Hence, they were ranked low, the more so because the 49, 99, or 149 other genes, residing together with each disease gene in the constructed susceptibility loci, had been selected on the premise that they interacted with at least one other gene. The GO network performed considerably better; when we used it to assess susceptibility loci that contained, on average, 100 genes, we found 16% and 24% of the disease genes were contained within the top 5 and top 10 genes per locus, respectively. The performance of the disease analysis was best when the inferred GO+MA+PPI network was complemented with the known true-positive interactions (GO+MA+PPI+TP network); with this network and an average susceptibility locus width of 100 genes, 27% and 34% of the disease genes were contained within the top 5 and top 10 per locus, respectively.

Figure  4
Accuracy of positional candidate-gene prioritization. a and b, Percentage of the 409 disease genes that was ranked among the top 5 (a) or top 10 (b) genes per locus, after artificial susceptibility loci of varying widths around these genes were constructed ...

We also assessed the probability of detecting at least one disease gene when only a fixed number of top-ranked genes per locus is followed up (fig. 5). When we employed the most comprehensive GO+MA+PPI+TP network and followed up all the top 5 or top 10 positional candidate genes for each disorder, using locus widths of 100 and 150 genes, we found at least one disease gene from these top sets of genes in 54% and 64% of the diseases, respectively, compared with 19% and 35% expected by chance. When we confined our analysis to diseases for which at least four or five disease genes were known, the performance of our method increased slightly (data not shown), because the true disease genes now interacted with more of the other true disease genes, increasing their overall scores.

Figure  5
Probability of detecting at least one disease gene when a fixed number of top-ranked positional candidate genes—as ranked by Prioritizer—are followed up for each locus. Each locus contains either 100 or 150 genes, and the GO+MA+PPI+TP ...

Breast Cancer as an Example

We selected breast cancer as an example of how the various gene networks perform in a complex disease for which multiple disease genes have been identified. Artificial susceptibility loci, each comprising 100 genes, were constructed around 10 putative breast cancer genes described in OMIM (as of March 1, 2005). For each of the four networks, we then determined how many of the disease genes were ranked within the top 10 per locus. The MA+PPI network ranked two disease genes (PIK3CA and CHEK2) in the top 10, whereas the GO network ranked three (BRCA2, NCOA3, and CHEK2), and the GO+MA+PPI network ranked four (BARD1, PIK3CA, TP53, and CHEK) (fig. 6). However, the GO+MA+PPI+TP network, which integrates the most information, performed the worst; of the 10 disease genes now known, only 2 (BARD1 and BRCA1) were ranked in the top 10. This can be explained by the observation that the true-positive set contained many known interactions for these 10 breast cancer genes. As the ranking procedure corrects for the topology of the network, these disease genes, with a marked increase in the number of relationships with other genes in this most comprehensive network, were suddenly no longer ranked as high. This became evident when the genes were ranked using the GO+MA+PPI+TP network but the differences in topology were not corrected for: 9 of the 10 breast cancer genes were then in the top 10 per locus.

Figure  6
Prioritizer analysis of breast cancer. Susceptibility loci, each containing 100 genes, were defined around 10 known breast cancer genes. The 10 highest-ranked genes for each locus are shown in the graph, with colors indicating the locus in which they ...

Prioritizer Availability

To allow researchers to analyze susceptibility loci of interest, we developed a Java application that can be downloaded, along with regularly updated gene network definition files and source code from the Prioritizer Web site. After a set of susceptibility loci has been entered, Prioritizer ranks the positional candidate genes in each locus by using the method described above in conjunction with one of the four gene networks. It can generate two- and three-dimensional graphs of the top-ranked positional candidate genes, which allows the user to visually inspect how the genes within the different loci interact with each other.

Discussion

In this study, we describe the construction of a functional human gene network of considerable accuracy (fig. 3; AUC=90%). As such, it can be used to assess interactions for a gene of interest through the bioinformatics tools that we have made available online. We have shown that, in cases where multiple genes underlie a disorder, these genes tend to have more functional interactions. When these functional interactions are employed to prioritize known disease genes in artificial susceptibility loci, the chance of detecting disease genes is increased considerably (2.8 fold).

In breast cancer, 4 of the 10 disease genes were ranked in the top 10 when the GO+MA+PPI network was applied, a fourfold enrichment over the single disease gene that would be picked up by chance. As has been discussed earlier, the correction for differences in topology is needed to prevent bias toward highly connected genes. However, this puts diseases in which underlying genes have a high degree of connectivity at a disadvantage, which was apparent in the analysis of breast cancer by use of the GO+MA+PPI+TP network. When this topology correction was omitted for breast cancer, the ranking of the disease genes improved considerably, to include 9 of the 10 genes. The availability of new high-throughput data sets will alleviate this problem in the future, by providing novel interactions for genes that currently have a low degree of connectivity, which will reduce the penalty on highly connected genes.

We noticed that the performance of Prioritizer was lower for complex disorders than for Mendelian disorders. This is likely caused by the fact that the etiology of complex diseases is more subtle and involves multiple pathways, so that most of the disease genes only confer a modest increased risk. Greater coverage of the gene network, leading to identification of relationships between genes that bridge the various pathways, could probably help to alleviate this problem.

When the accuracy of the various gene networks was assessed by investigation of their respective ROCs, it was envisaged that the GO+MA+PPI network would perform at least at a similar level in prioritizing disease genes as the GO network, because its AUC was greater. However, contrary to our expectation, when the positional candidate genes were prioritized, the disease genes in some diseases were ranked lower with the GO+MA+PPI network than with the GO network. One explanation could be that, within the microarray coexpression data sets (the main contributor to the MA+PPI network), we did not distinguish between coexpression and coregulation. As such, many direct interactions between genes were inferred, but a large proportion of these interactions were actually indirect. Methods have recently appeared33,49 that could help remove some of these incorrectly inferred interactions.

In a somewhat comparable method by Turner et al.,21 positional candidate genes are prioritized by determining which genes share InterPro50 domains and GO terms, as a measure to relate genes in susceptibility loci with each other. Our method extends this approach by also allowing for indirect relationships between individual disease genes, since Prioritizer uses the graph-theoretic distance between genes to relate them. Both approaches still rely largely on manual annotation, which is detrimental for genes that have not been investigated extensively. When no experimental evidence for interaction is available, there is only a small chance that these potential disease genes, residing in one specific susceptibility locus, will be associated with disease genes in other loci, since the sharing of GO or InterPro terms between these genes will be minimal. Although GO contributes the most to the performance of the Bayesian classifier, we should not depend entirely on a prediction if there is substantial evidence only from GO, while the evidence from the other data sets is lacking, for a specific gene pair, because the GO evidence has been inferred from the sharing of predominantly manually annotated terms, whereas the other sources rely more on direct biological measurements. It is expected that, when additional high-throughput data sets become available and their coverage of all possible functional interactions increases, GO evidence will be supplemented by experimental data, resulting in better predictions.

As such, an extensive and reliable functional gene network is crucial for good performance of our method. If this network is inaccurate or biased toward known genes, the ranking of true disease genes in the susceptibility loci will deteriorate. Several rapidly expanding data repositories are now becoming available that should help to improve our network. They include text mining methods,51,52 which extract functional relationships from the literature, and methods that integrate results from high-throughput proteomic approaches.53

Our gene network, which, in its current form, has been applied to genetic linkage analysis, can also be used for other applications. Recently, efforts have been made to prioritize positional candidate genes on the basis of their expression,54 with the assumption that differences in expression behavior in comparisons of patients with controls may be due to cis-acting variants in the underlying genes. However, it has turned out that, in most genes, differences in expression are determined by genetic variation in genes located elsewhere.55,56 The reconstructed functional gene network can help to relate the observed differences in gene expression to the underlying causative genetic variants in other genes, which might help in identifying the disease genes.

Prioritizer might also be well suited for genomewide SNP association studies. Technical improvements in conjunction with decreasing costs now allow researchers to perform these studies in complex diseases, thereby considerably increasing the resolution at which one can assess genetic variation. However, as the number of tested SNPs increases, the number of tested individuals required to achieve sufficient power will also rise. To help overcome this problem, a new statistical method has recently been developed57 that combines evidence from the most-significant tests, under the assumption that there are multiple true associations in the disease under investigation. However, within this confined set, the majority of genes will still be false positives because of power issues. Our positional candidate-gene prioritization method can easily be adapted to help distinguish true disease-associated genes and false-positive genes, by assuming that the true disease genes are mostly functionally related and will therefore be closer to each other in the gene network than to the false-positive genes that have been randomly selected.

We have demonstrated that it is feasible to use gene networks to prioritize positional candidate genes in various heritable disorders with multiple associated genes, even when the susceptibility loci are fairly large. As such, this article and the proposed methods show that the integration of gene networks with various genetic studies can be useful in identifying disease genes. We envisage that improvements both in the quality of the data sets making up these gene networks and in the statistical methods incorporating the networks will result in new, genetically testable hypotheses.

Acknowledgments

We thank Jackie Senior and members of the Complex Genetics Section and the Department of Human Genetics for critically reading the manuscript. This study was supported by Netherlands Organization for Scientific Research grant 901-04-219 and by a grant from the Celiac Disease Consortium, an innovative cluster approved by the Netherlands Genomics Initiative and partially funded by a Dutch government grant (BSIK03009).

Appendix A

Table A1

All Disease Genes for the 96 Diseases Tested and How They Rank When a Locus Width of 50, 100, or 150 Genes Is Used

MA+PPIGOGO+MA+PPIGO+MA+PPI+TP
Locus Width and Disease Gene (Ensembl)HUGODisease Name; AbbreviationOMIMNumberRankPRankPRankPRankP
50 Genes:
 ENSG00000091513TFAlzheimer disease; AD10430042.2427.4526.462.07
 ENSG00000175899A2MAlzheimer disease; AD10430047.8921.4034.591.02
 ENSG00000143801PSEN2Alzheimer disease; AD1043009.023.0520.5327.58
 ENSG00000123384LRP1Alzheimer disease; AD104300501.0016.2311.175.04
 ENSG00000142192APPAlzheimer disease; AD10430025.0933.4825.496.15
 ENSG00000130203APOEAlzheimer disease; AD10430026.0725.7144.841.00
 ENSG00000010704HFEAlzheimer disease; AD10430040.1913.5731.571.00
 ENSG00000080815PSEN1Alzheimer disease; AD1043004.021.0626.5544.78
 ENSG00000101439CST3Amyloidosis VI1051503.1040.8131.7234.82
 ENSG00000165029ABCA1Amyloidosis VI105150501.0047.9436.8124.62
 ENSG00000136156ITM2BAmyloidosis VI10515022.7447.8311.4045.99
 ENSG00000171560FGAAmyloidosis, familial visceral105200501.0043.7928.5725.54
 ENSG00000090382LYZAmyloidosis, familial visceral10520023.4335.5246.9523.56
 ENSG00000118137APOA1Amyloidosis, familial visceral1052005.0624.3246.9513.45
 ENSG00000124164VAPBAmyotrophic lateral sclerosis 1; ALS110540012.0921.3534.422.03
 ENSG00000003393ALS2Amyotrophic lateral sclerosis 1; ALS1105400501.0024.3246.6239.58
 ENSG00000142168SOD1Amyotrophic lateral sclerosis 1; ALS110540021.101.0027.2336.51
 ENSG00000100285NEFHAmyotrophic lateral sclerosis 1; ALS110540018.117.0829.2128.32
 ENSG00000135406PRPHAmyotrophic lateral sclerosis 1; ALS110540020.08501.009.0429.21
 ENSG00000112112COL11A2Stickler syndrome, type I; STL110830014.5833.7846.772.04
 ENSG00000139219COL2A1Stickler syndrome, type I; STL1108300501.0022.5046.7712.32
 ENSG00000060718COL11A1Stickler syndrome, type I; STL1108300501.0040.7824.622.04
 ENSG00000139687RB1Bladder cancer10980037.6637.5811.2528.46
 ENSG00000068078FGFR3Bladder cancer10980032.7112.2330.8516.35
 ENSG00000174775HRASBladder cancer1098009.253.0630.4838.88
 ENSG00000139618BRCA2Breast cancer11448027.065.019.052.00
 ENSG00000138376BARD1Breast cancer11448012.0337.322.016.01
 ENSG00000124151NCOA3Breast cancer11448031.175.015.018.02
 ENSG00000121879PIK3CABreast cancer11448010.0232.352.0125.16
 ENSG00000012048BRCA1Breast cancer11448011.0113.086.021.01
 ENSG00000170836PPM1DBreast cancer11448023.0722.1416.069.03
 ENSG00000141510TP53Breast cancer11448011.0224.074.0119.13
 ENSG00000023287RB1CC1Breast cancer11448020.0343.6048.7226.17
 ENSG00000183765CHEK2Breast cancer1144803.018.016.0217.11
 ENSG00000169083ARBreast cancer114480501.0023.1522.1416.14
 ENSG00000105976METHepatocellular carcinoma11455033.5115.227.1410.16
 ENSG00000168036CTNNB1Hepatocellular carcinoma11455016.2344.8911.2423.48
 ENSG00000141510TP53Hepatocellular carcinoma11455021.5115.3650.9349.99
 ENSG00000138109CYP2C9Coumarin resistance12270010.3041.8436.7110.16
 ENSG00000167397VKOR1_HUMANCoumarin resistance12270016.1821.4839.472.02
 ENSG00000198470CYP2A6Coumarin resistance12270032.766.0940.9020.33
 ENSG00000101981F9Coumarin resistance122700501.0019.2916.506.15
 ENSG00000162992NEUROD1Diabetes mellitus, non–insulin-dependent; NIDDM1258538.024.0149.9244.53
 ENSG00000121653MAPK8IP1Diabetes mellitus, non–insulin-dependent; NIDDM12585348.4120.2936.523.02
 ENSG00000163581SLC2A2Diabetes mellitus, non–insulin-dependent; NIDDM12585328.1410.084.0238.30
 ENSG00000181856SLC2A4Diabetes mellitus, non–insulin-dependent; NIDDM12585321.0815.095.031.00
 ENSG00000105221AKT2Diabetes mellitus, non–insulin-dependent; NIDDM12585324.0931.5045.5823.15
 ENSG00000101076HNF4ADiabetes mellitus, non–insulin-dependent; NIDDM1258534.0042.5216.155.04
 ENSG00000142330CAPN10Diabetes mellitus, non–insulin-dependent; NIDDM125853501.0040.5027.363.00
 ENSG00000135100TCF1Diabetes mellitus, non–insulin-dependent; NIDDM125853501.0042.4338.597.04
 ENSG00000104918RETNDiabetes mellitus, non–insulin-dependent; NIDDM12585335.1824.27501.0020.14
 ENSG00000171867PRNPDementia, Lewy body; DLB12775023.34501.0027.6422.65
 ENSG00000145335SNCADementia, Lewy body; DLB12775010.1047.9412.4031.60
 ENSG00000130203APOEDementia, Lewy body; DLB12775040.6947.9522.6131.74
 ENSG00000070182SPTBElliptocytosis, Rhesus-unlinked type130600501.002.0535.991.00
 ENSG00000163554SPTA1Elliptocytosis, Rhesus-unlinked type13060023.223.074.141.00
 ENSG00000004939SLC4A1Elliptocytosis, Rhesus-unlinked type13060012.147.1622.862.03
 ENSG00000186847KRT14Epidermolysis bullosa of hands and feet131800501.0012.031.019.01
 ENSG00000186081KRT5Epidermolysis bullosa of hands and feet131800501.0010.039.023.00
 ENSG00000132470ITGB4Epidermolysis bullosa of hands and feet131800501.0019.3317.2243.61
 ENSG00000114270COL7A1Epidermolysis bullosa of hands and feet13180013.1320.3624.3013.24
 ENSG00000127870RNF6Esophageal cancer13323938.6529.2420.2124.27
 ENSG00000134982APCEsophageal cancer1332391.0033.3847.5529.25
 ENSG00000147889CDKN2AEsophageal cancer133239501.0022.2214.1422.46
 ENSG00000008226DLEC1Esophageal cancer133239501.0016.216.1310.15
 ENSG00000061337LZTS1Esophageal cancer133239501.0045.7320.1716.12
 ENSG00000141510TP53Esophageal cancer13323921.3742.7017.302.03
 ENSG00000187323DCCEsophageal cancer133239501.0012.114.085.03
 ENSG00000186153NP_570606.1Esophageal cancer13323922.3127.3630.412.01
 ENSG00000140522RLBP1Fundus albipunctatus13688037.3815.3543.791.01
 ENSG00000112619RDSFundus albipunctatus136880501.0023.4825.5012.31
 ENSG00000135437RDH5Fundus albipunctatus136880501.0023.3931.771.02
 ENSG00000130203APOEFundus albipunctatus13688029.5849.9645.8323.49
 ENSG00000063169GLTSCR1Glioma of brain, familial13780041.39501.00501.00501.00
 ENSG00000111087GLI1_HUMANGlioma of brain, familial13780043.579.046.0322.22
 ENSG00000108231LGI1Glioma of brain, familial13780022.17501.00501.00501.00
 ENSG00000147889CDKN2AGlioma of brain, familial137800501.0036.738.0820.29
 ENSG00000146648EGFRGlioma of brain, familial1378005.0415.1932.4143.77
 ENSG00000132170PPARGGlioma of brain, familial13780013.1132.4110.0729.53
 ENSG00000117298ECE1Hirschsprung disease14262328.3049.7126.3221.30
 ENSG00000136160EDNRBHirschsprung disease142623501.0041.5247.795.05
 ENSG00000169554ZFHX1BHirschsprung disease1426233.0110.127.055.05
 ENSG00000165731RETHirschsprung disease1426239.0623.2724.2630.30
 ENSG00000124205EDN3Hirschsprung disease142623501.0010.1221.213.05
 ENSG00000168621GDNFHirschsprung disease142623501.0046.7915.1312.16
 ENSG00000130164LDLRHypercholesterolemia, familial14389010.292.024.031.00
 ENSG00000055955ITIH4Hypercholesterolemia, familial14389040.7145.7136.4449.97
 ENSG00000158874APOA2Hypercholesterolemia, familial143890501.0023.3417.233.04
 ENSG00000169174PCSK9Hypercholesterolemia, familial1438903.0427.3036.5612.07
 ENSG00000120915EPHX2Hypercholesterolemia, familial14389027.2741.5333.4010.10
 ENSG00000084674APOBHypercholesterolemia, familial14389015.283.125.107.18
 ENSG00000111664GNB3Hypertension, essential1455003.0214.1140.4220.25
 ENSG00000124212PTGISHypertension, essential14550031.2614.2232.3033.41
 ENSG00000087274NP_789771.1Hypertension, essential14550043.3626.3028.3347.81
 ENSG00000106258CYP3A5Hypertension, essential14550037.468.1342.6110.10
 ENSG00000028137TNFRSF1BHypertension, essential1455006.0319.2023.2128.43
 ENSG00000144891AGTR1Hypertension, essential14550047.4312.0933.2334.30
 ENSG00000135744AGTHypertension, essential14550036.2211.1045.538.11
 ENSG00000105227PRXHypertrophic neuropathy of Dejerine-Sottas145900501.001.001.0119.32
 ENSG00000109099PMP22Hypertrophic neuropathy of Dejerine-Sottas14590045.6632.5238.378.13
 ENSG00000122877EGR2Hypertrophic neuropathy of Dejerine-Sottas1459005.0634.6827.6532.78
 ENSG00000158887MPZHypertrophic neuropathy of Dejerine-Sottas145900501.001.021.031.03
 ENSG00000169083ARHypospadias146450501.0036.8337.8340.95
 ENSG00000101871MID1Hypospadias146450501.004.0630.632.01
 ENSG00000049319SRD5A2Hypospadias14645014.2223.5121.4820.56
 ENSG00000005471ABCB4Cholestasis, intrahepatic, of pregnancy; ICP147480501.0029.5119.2718.36
 ENSG00000073734ABCB11Cholestasis, intrahepatic, of pregnancy; ICP147480501.0042.8647.9213.21
 ENSG00000081923ATP8B1Cholestasis, intrahepatic, of pregnancy; ICP14748019.416.1935.6134.85
 ENSG00000167768KRT1Keratosis palmoplantaris striata I14870048.8718.2933.873.01
 ENSG00000134760DSG1Keratosis palmoplantaris striata I148700501.005.1318.502.01
 ENSG00000096696DSPKeratosis palmoplantaris striata I14870044.731.011.011.00
 ENSG00000111275ALDH2Leiomyoma, uterine15069937.4112.2146.819.28
 ENSG00000143196DPTLeiomyoma, uterine15069947.61501.00501.008.08
 ENSG00000182185RAD51L1Leiomyoma, uterine150699501.0031.5115.2231.64
 ENSG00000164919COX6CLeiomyoma, uterine15069934.3517.2232.4214.17
 ENSG00000087237CETPLongevity15243017.1523.3111.1943.91
 ENSG00000108599AKAP10Longevity15243048.677.126.0816.36
 ENSG00000136869TLR4Longevity1524308.064.0746.908.24
 ENSG00000005421PON1Longevity152430501.005.062.0310.19
 ENSG00000126594DNASE1Lupus erythematosus, systemic; SLE15270045.7116.2737.6627.57
 ENSG00000163599CTLA4Lupus erythematosus, systemic; SLE152700501.0045.85501.0014.18
 ENSG00000134242PTPN22Lupus erythematosus, systemic; SLE15270015.1912.198.1235.75
 ENSG00000143226FCGR2ALupus erythematosus, systemic; SLE15270014.0932.4737.5234.50
 ENSG00000168036CTNNB1Medulloblastoma1552554.1318.482.091.02
 ENSG00000134982APCMedulloblastoma1552551.0038.631.001.01
 ENSG00000117425PTCH2Medulloblastoma155255501.0029.6512.419.18
 ENSG00000107882SUFUMedulloblastoma15525519.4619.4013.4315.30
 ENSG00000107815PEO1Progressive external ophthalmoplegia with mtDNA deletions; PEO15764029.95501.0035.9226.63
 ENSG00000151729SLC25A4Progressive external ophthalmoplegia with mtDNA deletions; PEO1576404.3819.5040.8229.74
 ENSG00000140521POLGProgressive external ophthalmoplegia with mtDNA deletions; PEO15764038.933.0125.6736.79
 ENSG00000136827TOR1AMyoclonic dystonia15990023.3924.3430.5926.63
 ENSG00000127990SGCEMyoclonic dystonia15990049.8735.6128.5626.62
 ENSG00000149295DRD2Myoclonic dystonia15990022.4814.2513.2411.19
 ENSG00000143632ACTA1Nemaline myopathy 1, autosomal dominant; NEM116180045.8920.678.142.06
 ENSG00000143549TPM3Nemaline myopathy 1, autosomal dominant; NEM11618005.1924.605.192.05
 ENSG00000198467TPM2Nemaline myopathy 1, autosomal dominant; NEM116180027.3424.471.0216.41
 ENSG00000179142CYP11B2IgA nephropathy161950501.007.1733.8830.72
 ENSG00000135744AGTIgA nephropathy16195024.558.1140.9616.41
 ENSG00000159640ACEIgA nephropathy16195029.5929.5517.779.23
 ENSG00000007908SELEIgA nephropathy161950501.009.10501.001.04
 ENSG00000133256PDE6BNight blindness, congenital stationary; CSNB3163500501.0044.957.0527.38
 ENSG00000114349GNAT1Night blindness, congenital stationary; CSNB316350042.698.1436.3716.21
 ENSG00000163914RHONight blindness, congenital stationary; CSNB3163500501.0040.8548.8634.68
 ENSG00000108576SLC6A4Obsessive-compulsive disorder 1; OCD11642303.0210.3048.9426.68
 ENSG00000102468HTR2AObsessive-compulsive disorder 1; OCD116423040.4126.6444.8134.73
 ENSG00000176697BDNFObsessive-compulsive disorder 1; OCD1164230501.0030.5823.2548.95
 ENSG00000004948CALCROsteoporosis, involutional166710501.0017.2832.5420.26
 ENSG00000136244IL6Osteoporosis, involutional1667107.1015.2627.5225.48
 ENSG00000162337LRP5Osteoporosis, involutional16671013.118.1511.173.02
 ENSG00000108821COL1A1Osteoporosis, involutional1667102.023.021.0015.14
 ENSG00000111424VDROsteoporosis, involutional16671032.3819.4730.4714.13
 ENSG00000145335SNCAParkinson disease; PD16860011.2020.2322.591.00
 ENSG00000154277UCHL1Parkinson disease; PD1686009.1133.5712.252.02
 ENSG00000185345PARK2Parkinson disease; PD16860023.5234.4216.541.01
 ENSG00000064692SNCAIPParkinson disease; PD16860048.79501.0041.881.00
 ENSG00000081248CACNA1SHypokalemic periodic paralysis; HOKPP170400501.0036.8245.952.05
 ENSG00000175538KCNE3Hypokalemic periodic paralysis; HOKPP170400501.007.141.172.11
 ENSG00000007314SCN4AHypokalemic periodic paralysis; HOKPP17040037.7413.4019.678.38
 ENSG00000165731RETPheochromocytoma17130044.7012.2620.3817.52
 ENSG00000117118SDHBPheochromocytoma17130035.7630.6810.2316.39
 ENSG00000196712NF1Pheochromocytoma17130027.6848.9536.7411.26
 ENSG00000183765CHEK2Prostate cancer1768076.0820.2439.7538.58
 ENSG00000085117CD82Prostate cancer17680711.09501.0017.3220.29
 ENSG00000067082KLF6Prostate cancer17680722.2139.6311.1712.11
 ENSG00000133216EPHB2Prostate cancer17680726.3016.1645.9226.38
 ENSG00000135828RNASELProstate cancer176807501.0014.1434.5637.60
 ENSG00000038945MSR1Prostate cancer176807501.0010.0812.1050.96
 ENSG00000171862PTEN_HUMANProstate cancer1768073.0228.2314.2223.20
 ENSG00000006744RNZ2_HUMANProstate cancer1768079.103.0311.1135.47
 ENSG00000002822MAD1L1Prostate cancer17680747.6027.3725.4221.28
 ENSG00000159339PADI4Rheumatoid arthritis180300501.0037.6421.2239.84
 ENSG00000168593NFKBIL1Rheumatoid arthritis180300501.0044.7913.3737.77
 ENSG00000159216RUNX1Rheumatoid arthritis18030030.3218.2133.5449.95
 ENSG00000134242PTPN22Rheumatoid arthritis18030027.2738.6438.7710.17
 ENSG00000197208SLC22A4Rheumatoid arthritis18030024.2019.2821.252.03
 ENSG00000138293NCOA4Thyroid carcinoma, papillary18855013.0941.9721.3749.99
 ENSG00000108946PRKAR1AThyroid carcinoma, papillary1885502.0146.9621.3817.25
 ENSG00000198400NTRK1Thyroid carcinoma, papillary188550501.0031.5027.3022.28
 ENSG00000114354TFGThyroid carcinoma, papillary18855042.5318.3710.1622.32
 ENSG00000047410TPRThyroid carcinoma, papillary18855012.134.105.0322.35
 ENSG00000129991TNNI3Cardiomyopathy, familial hypertrophic; CMH192600501.0021.3639.6717.17
 ENSG00000160808MYL3Cardiomyopathy, familial hypertrophic; CMH19260044.351.001.001.00
 ENSG00000140416TPM1Cardiomyopathy, familial hypertrophic; CMH19260044.411.001.022.01
 ENSG00000155657NP_59687.1Cardiomyopathy, familial hypertrophic; CMH19260035.231.004.0213.12
 ENSG00000092054MYH7Cardiomyopathy, familial hypertrophic; CMH19260039.231.0024.3748.79
 ENSG00000111245MYL2Cardiomyopathy, familial hypertrophic; CMH19260049.461.001.003.04
 ENSG00000118194TNNT2Cardiomyopathy, familial hypertrophic; CMH19260015.052.0146.632.01
 ENSG00000134571MYBPC3Cardiomyopathy, familial hypertrophic; CMH192600501.001.004.037.05
 ENSG00000101306MYLK2Cardiomyopathy, familial hypertrophic; CMH192600501.0041.5136.5811.14
 ENSG00000183785PEX26Adrenoleukodystrophy, autosomal neonatal form20237046.691.001.011.01
 ENSG00000162928PEX13Adrenoleukodystrophy, autosomal neonatal form20237046.791.0038.661.02
 ENSG00000127980PEX1Adrenoleukodystrophy, autosomal neonatal form20237040.417.2029.531.01
 ENSG00000157911PEX10Adrenoleukodystrophy, autosomal neonatal form20237025.111.0044.631.01
 ENSG00000139197PEX5Adrenoleukodystrophy, autosomal neonatal form20237031.381.001.011.00
 ENSG00000047579DTNBP1Hermansky-Pudlak syndrome; HPS20330025.1615.047.0742.81
 ENSG00000107521HPS1Hermansky-Pudlak syndrome; HPS20330037.353.0332.5411.12
 ENSG00000132842AP3B1Hermansky-Pudlak syndrome; HPS20330027.24501.0019.217.12
 ENSG00000110756HPS5Hermansky-Pudlak syndrome; HPS20330049.62501.0045.7050.93
 ENSG00000100099HPS4Hermansky-Pudlak syndrome; HPS20330011.1136.6346.868.08
 ENSG00000163755HPS3Hermansky-Pudlak syndrome; HPS203300501.00501.00501.00501.00
 ENSG00000092200RPGRIP1Leber congenital amaurosis, type I; LCA120400045.521.004.038.07
 ENSG00000132518GUCY2DLeber congenital amaurosis, type I; LCA1204000501.0045.6327.2811.13
 ENSG00000116745RPE65Leber congenital amaurosis, type I; LCA120400045.4835.4723.2321.29
 ENSG00000139988RDH12_HUMANLeber congenital amaurosis, type I; LCA120400037.5641.515.0426.28
 ENSG00000134376CRB1Leber congenital amaurosis, type I; LCA1204000501.0010.0918.152.00
 ENSG00000105392CRXLeber congenital amaurosis, type I; LCA120400042.3621.1516.1129.32
 ENSG00000112041TULP1Leber congenital amaurosis, type I; LCA12040007.032.0029.3529.48
 ENSG00000165731RETAutonomic control, congenital failure of20988011.0941.6712.1923.34
 ENSG00000124205EDN3Autonomic control, congenital failure of209880501.009.164.1043.81
 ENSG00000109132PHOX2BAutonomic control, congenital failure of20988050.8714.181.011.02
 ENSG00000176697BDNFAutonomic control, congenital failure of209880501.0034.4449.9237.61
 ENSG00000168621GDNFAutonomic control, congenital failure of209880501.0010.1034.683.04
 ENSG00000165533TTC8Bardet-Biedl syndrome; BBS209900501.005.051.011.00
 ENSG00000174483DPP3Bardet-Biedl syndrome; BBS20990038.3750.9413.2611.19
 ENSG00000138686BBS7_HUMANBardet-Biedl syndrome; BBS20990035.291.0021.2916.29
 ENSG00000125124BBS2Bardet-Biedl syndrome; BBS209900501.001.001.001.00
 ENSG00000125863MKKSBardet-Biedl syndrome; BBS20990046.5333.3643.7832.52
 ENSG00000140463BBS4Bardet-Biedl syndrome; BBS20990019.139.052.011.01
 ENSG00000113966ARL6Bardet-Biedl syndrome; BBS209900501.0032.5633.4521.29
 ENSG00000163093BBS5Bardet-Biedl syndrome; BBS2099002.02501.0010.2313.19
 ENSG00000133111RFXAPBare lymphocyte syndrome, type II20992029.4620.3620.481.00
 ENSG00000179583MHC2TABare lymphocyte syndrome, type II209920501.005.065.051.00
 ENSG00000064490RFXANKBare lymphocyte syndrome, type II20992020.3024.2718.281.00
 ENSG00000143390RFX5Bare lymphocyte syndrome, type II20992030.5726.3421.291.00
 ENSG00000159128IFNGR2Atypical mycobacteriosis, familial2099506.075.013.012.00
 ENSG00000096996IL12RB1Atypical mycobacteriosis, familial209950501.005.0318.121.00
 ENSG00000027697IFNGR1Atypical mycobacteriosis, familial2099502.031.003.021.00
 ENSG00000113302IL12BAtypical mycobacteriosis, familial209950501.005.0438.381.00
 ENSG00000115415STAT1Atypical mycobacteriosis, familial20995026.1923.326.0315.16
 ENSG00000073734ABCB11Cholestasis, progressive familial intrahepatic 1; PFIC1211600501.0037.7840.8819.33
 ENSG00000005471ABCB4Cholestasis, progressive familial intrahepatic 1; PFIC1211600501.0032.6914.3623.43
 ENSG00000081923ATP8B1Cholestasis, progressive familial intrahepatic 1; PFIC121160024.635.1433.7128.74
 ENSG00000099377HSD3B7Cholestasis, progressive familial intrahepatic 1; PFIC121160017.111.021.0211.29
 ENSG00000133703RASK_HUMANLung cancer21198040.3944.645.0419.24
 ENSG00000157764BRAFLung cancer2119806.0230.532.021.01
 ENSG00000141510TP53Lung cancer2119801.0013.2239.6649.97
 ENSG00000146648EGFRLung cancer21198025.2613.275.0915.24
 ENSG00000139197PEX5Zellweger syndrome; ZS2141007.036.031.001.00
 ENSG00000142655PEX14Zellweger syndrome; ZS21410014.069.103.012.00
 ENSG00000127980PEX1Zellweger syndrome; ZS21410014.061.031.003.01
 ENSG00000124587PEX6Zellweger syndrome; ZS2141003.012.032.011.02
 ENSG00000108733PEX12Zellweger syndrome; ZS21410019.1218.3142.621.00
 ENSG00000164751PXMP3Zellweger syndrome; ZS21410024.1435.5734.411.00
 ENSG00000183785PEX26Zellweger syndrome; ZS2141009.042.034.041.00
 ENSG00000034693PEX3Zellweger syndrome; ZS2141001.001.0014.111.00
 ENSG00000172660TAF15Chondrosarcoma21530033.3444.7245.669.13
 ENSG00000182197EXT1Chondrosarcoma21530015.057.1030.522.01
 ENSG00000151348EXT2Chondrosarcoma2153002.018.1217.311.02
 ENSG00000182944EWSR1Chondrosarcoma21530027.2341.7410.1815.33
 ENSG00000134183GNAT2Achromatopsia 2; ACHM221690042.7125.5334.7936.79
 ENSG00000144191CNGA3Achromatopsia 2; ACHM221690044.575.162.0335.87
 ENSG00000170289CNGB3Achromatopsia 2; ACHM2216900501.0021.6716.3311.33
 ENSG00000065618COHA1_HUMANEpidermolysis bullosa junctionalis, disentis type22665016.4134.4112.3112.19
 ENSG00000132470ITGB4Epidermolysis bullosa junctionalis, disentis type226650501.0038.789.1632.59
 ENSG00000196878LAMB3Epidermolysis bullosa junctionalis, disentis type226650501.003.041.049.15
 ENSG00000053747LAMA3Epidermolysis bullosa junctionalis, disentis type22665031.6520.3741.7715.21
 ENSG00000165281FANCGFanconi anemia; FA22765012.0336.3433.491.00
 ENSG00000144554FANCD2Fanconi anemia; FA227650501.002.011.011.00
 ENSG00000183161FANCFFanconi anemia; FA22765019.112.011.021.00
 ENSG00000158169FANCCFanconi anemia; FA22765021.102.0222.231.00
 ENSG00000115392FANCLFanconi anemia; FA22765040.4526.3619.311.00
 ENSG00000112039FANCEFanconi anemia; FA2276502.013.0115.251.00
 ENSG00000105379ETFBGlutaricaciduria IIA2316801.011.061.001.00
 ENSG00000140374ETFAGlutaricaciduria IIA2316802.013.061.001.00
 ENSG00000171503ETFDHGlutaricaciduria IIA2316801.0010.191.001.00
 ENSG00000188690UROSHydrops fetalis, idiopathic23675026.2050.968.1128.54
 ENSG00000169919GUSBHydrops fetalis, idiopathic23675028.4121.5446.9142.92
 ENSG00000177628GBAHydrops fetalis, idiopathic23675046.6740.9346.8827.64
 ENSG00000188536HBA1Hydrops fetalis, idiopathic23675012.0643.9316.466.14
 ENSG00000179477ALOX12BIchthyosiform erythroderma, congenital, nonbullous, 1; NCIE1242100501.0021.416.144.06
 ENSG00000011198ABHD5Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE12421007.1011.1928.5131.62
 ENSG00000092295TGM1Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE124210012.232.044.0436.60
 ENSG00000122735DNAI1Kartagener syndrome24440012.12501.006.1416.53
 ENSG00000039139DNAH5Kartagener syndrome244400501.001.011.041.04
 ENSG00000105877DNAH11Kartagener syndrome244400501.001.025.071.04
 ENSG00000112964GHRLaron syndrome, type II245590501.0011.1530.662.01
 ENSG00000189162GH1Laron syndrome, type II245590501.004.033.063.03
 ENSG00000173757STAT5BLaron syndrome, type II24559017.4531.6339.8812.21
 ENSG00000137992DBTMaple syrup urine disease, type IA24860029.4521.2741.542.00
 ENSG00000091140DLDMaple syrup urine disease, type IA24860022.1938.523.041.01
 ENSG00000142046BCKDHAMaple syrup urine disease, type IA2486004.0510.131.021.00
 ENSG00000083123BCKDHBMaple syrup urine disease, type IA24860031.405.0330.442.00
 ENSG00000167792NDUFV1Complex I, mitochondrial respiratory chain, deficiency of25201037.341.003.013.00
 ENSG00000115286NDUFS7Complex I, mitochondrial respiratory chain, deficiency of25201042.621.014.013.01
 ENSG00000164258NDUFS4Complex I, mitochondrial respiratory chain, deficiency of25201027.351.011.001.00
 ENSG00000023228NDUFS1Complex I, mitochondrial respiratory chain, deficiency of25201042.611.001.001.00
 ENSG00000158864NDUFS2Complex I, mitochondrial respiratory chain, deficiency of25201012.061.001.001.00
 ENSG00000110092CCND1Myeloma, multiple2545002.0250.9244.6744.85
 ENSG00000130076IGHG3Myeloma, multiple25450041.6810.2044.8435.70
 ENSG00000068078FGFR3Myeloma, multiple25450021.293.0315.344.03
 ENSG00000137265IRF4Myeloma, multiple25450030.4119.2922.4015.21
 ENSG00000115286NDUFS7Leigh syndrome; LS2560009.022.031.001.00
 ENSG00000074582BCS1LLeigh syndrome; LS25600012.0723.323.039.05
 ENSG00000073578SDHALeigh syndrome; LS2560009.0432.538.086.06
 ENSG00000110536NDUFS3Leigh syndrome; LS2560008.041.021.001.01
 ENSG00000091140DLDLeigh syndrome; LS25600039.2346.7925.2926.38
 ENSG00000110717NDUFS8Leigh syndrome; LS2560005.005.033.001.00
 ENSG00000006071ABCC8Nesidioblastosis of pancreas25645037.4850.9944.8936.81
 ENSG00000106633GCKNesidioblastosis of pancreas25645046.7119.262.1030.63
 ENSG00000148672GLUD1Nesidioblastosis of pancreas25645013.0718.307.2329.50
 ENSG00000141655TNFRSF11AOsteogenic sarcoma259500501.002.1229.674.11
 ENSG00000139687RB1Osteogenic sarcoma25950023.2531.5839.6224.50
 ENSG00000183765CHEK2Osteogenic sarcoma25950049.9244.9046.8825.57
 ENSG00000081087OSTM1Osteopetrosis, autosomal recessive2597007.31501.0012.3526.66
 ENSG00000103249CLCN7Osteopetrosis, autosomal recessive25970040.7130.7533.7930.68
 ENSG00000110719TCIRG1Osteopetrosis, autosomal recessive25970046.8230.706.084.27
 ENSG00000133703RASK_HUMANPancreatic carcinoma26035050.6224.197.0617.25
 ENSG00000141510TP53Pancreatic carcinoma26035012.1230.3121.2918.20
 ENSG00000145050ARMETPancreatic carcinoma26035038.43501.0041.4141.58
 ENSG00000147889CDKN2APancreatic carcinoma260350501.0020.2548.5145.55
 ENSG00000141646SMAD4Pancreatic carcinoma2603501.0134.3428.2638.65
 ENSG00000139618BRCA2Pancreatic carcinoma26035045.7533.4042.5015.18
 ENSG00000144191CNGA3Achromatopsia 3; ACHM326230042.589.201.0234.85
 ENSG00000134183GNAT2Achromatopsia 3; ACHM326230036.6825.5436.7938.83
 ENSG00000170289CNGB3Achromatopsia 3; ACHM3262300501.0021.6815.3310.33
 ENSG00000107187LHX3Pituitary dwarfism III26260030.4041.8631.4641.90
 ENSG00000163666HESX1Pituitary dwarfism III262600501.0036.7946.916.02
 ENSG00000175325PROP1Pituitary dwarfism III262600501.0037.7837.762.01
 ENSG00000111319SCNN1APseudohypoaldosteronism, type I, autosomal recessive; PHA126435025.691.002.052.10
 ENSG00000168447SCNN1BPseudohypoaldosteronism, type I, autosomal recessive; PHA126435018.311.006.075.07
 ENSG00000151623NR3C2Pseudohypoaldosteronism, type I, autosomal recessive; PHA126435039.7230.6940.9724.58
 ENSG00000168878SFTPBPulmonary alveolar proteinosis265120501.002.0118.607.31
 ENSG00000100368CSF2RBPulmonary alveolar proteinosis265120501.0033.6446.9950.99
 ENSG00000168484SFTPCPulmonary alveolar proteinosis265120501.002.001.0115.43
 ENSG00000164751PXMP3Refsum disease, infantile form26651047.875.1527.6535.90
 ENSG00000127980PEX1Refsum disease, infantile form2665103.0537.7816.501.00
 ENSG00000183785PEX26Refsum disease, infantile form26651029.581.023.011.00
 ENSG00000197375SLC22A5Inflammatory bowel disease 1; IBD12666006.0547.9624.5848.97
 ENSG00000151208DLG5Inflammatory bowel disease 1; IBD126660038.7420.379.2725.55
 ENSG00000167207CARD15Inflammatory bowel disease 1; IBD1266600501.0045.937.2422.62
 ENSG00000085563ABCB1Inflammatory bowel disease 1; IBD1266600501.0014.2442.8936.79
 ENSG00000092929UNC13DReticulosis, familial histiocytic267700501.0049.97501.005.16
 ENSG00000166349RAG1Reticulosis, familial histiocytic267700511.0012.2950.9039.92
 ENSG00000180644PRF1Reticulosis, familial histiocytic26770021.2444.958.1236.56
 ENSG00000135903PAX3Rhabdomyosarcoma 2; RMS226822019.3912.3123.4536.86
 ENSG00000150907FOXO1ARhabdomyosarcoma 2; RMS226822048.8439.8310.2535.97
 ENSG00000009709PAX7Rhabdomyosarcoma 2; RMS22682203.0619.419.3018.40
 ENSG00000108576SLC6A4Sudden infant death syndrome27212033.6723.5112.4129.71
 ENSG00000183873SCN5ASudden infant death syndrome27212040.755.064.0439.85
 ENSG00000053918KCNQ1Sudden infant death syndrome272120501.0042.8946.6539.64
 ENSG00000163599CTLA4Graves disease275000501.002.03501.0025.81
 ENSG00000145321GCGraves disease2750005.0821.5125.432.04
 ENSG00000111424VDRGraves disease2750008.0738.8728.5615.37
 ENSG00000134982APCTurcot syndrome2763002.0041.7240.6837.86
 ENSG00000076242MLH1Turcot syndrome27630032.712.0612.4027.61
 ENSG00000122512PMS2Turcot syndrome2763004.072.072.032.07
 ENSG00000100146SOX10Waardenburg-Shah syndrome27758034.573.0646.8527.73
 ENSG00000124205EDN3Waardenburg-Shah syndrome277580501.0046.9723.562.04
 ENSG00000136160EDNRBWaardenburg-Shah syndrome277580501.006.091.015.13
 ENSG00000112357PEX7Rhizomelic chondrodysplasia punctata, type 3; RCDP360012118.2940.7927.7343.97
 ENSG00000116906GNPATRhizomelic chondrodysplasia punctata, type 3; RCDP360012126.3520.4440.764.04
 ENSG00000018510AGPSRhizomelic chondrodysplasia punctata, type 3; RCDP360012113.231.0540.752.01
 ENSG00000170175CHRNB1Myasthenic syndrome, slow-channel congenital; SCCMS6014625.0635.692.0947.95
 ENSG00000135902CHRNDMyasthenic syndrome, slow-channel congenital; SCCMS60146215.112.001.007.14
 ENSG00000138435CHRNA1Myasthenic syndrome, slow-channel congenital; SCCMS601462501.001.001.002.02
 ENSG00000175426PCSK1Obesity60166548.6247.8428.3120.27
 ENSG00000115138POMCObesity60166524.1417.2235.306.03
 ENSG00000116678LEPRObesity6016651.0045.8614.152.00
 ENSG00000166603MC4RObesity601665501.0018.2241.654.02
 ENSG00000174483DPP3Obesity60166518.1123.371.023.01
 ENSG00000174697LEPObesity601665501.0026.3037.424.01
 ENSG00000130203APOEObesity60166534.2411.2414.1638.33
 ENSG00000142156COL6A1Ossification of the posterior longitudinal ligament of spine; OPLL602475501.0024.4813.3215.17
 ENSG00000112112COL11A2Ossification of the posterior longitudinal ligament of spine; OPLL60247532.575.185.211.06
 ENSG00000197594ENPP1Ossification of the posterior longitudinal ligament of spine; OPLL602475501.0024.4341.7120.42
 ENSG00000115211EIF2B4Leukoencephalopathy with vanishing white matter; VWM6038962.001.001.001.00
 ENSG00000070785EIF2B3Leukoencephalopathy with vanishing white matter; VWM6038961.001.002.001.00
 ENSG00000111361EIF2B1Leukoencephalopathy with vanishing white matter; VWM6038961.001.001.001.00
 ENSG00000119718EIF2B2Leukoencephalopathy with vanishing white matter; VWM6038961.001.001.001.00
 ENSG00000145191EIF2B5Leukoencephalopathy with vanishing white matter; VWM6038961.002.001.001.00
 ENSG00000138061CYP1B1Peters anomaly60422921.3533.489.1123.31
 ENSG00000054598FOXC1Peters anomaly60422915.2127.3647.7950.99
 ENSG00000164093PITX2Peters anomaly604229501.0024.372.0341.80
 ENSG00000007372PAX6Peters anomaly60422940.7718.3228.5115.23
 ENSG00000144285SCN1AGeneralized epilepsy with febrile seizures plus; GEFS+60423333.552.024.018.04
 ENSG00000113327GABRG2Generalized epilepsy with febrile seizures plus; GEFS+60423340.634.2025.4923.64
 ENSG00000105711SCN1BGeneralized epilepsy with febrile seizures plus; GEFS+60423311.121.021.0131.70
 ENSG00000157764BRAFLymphoma, non-Hodgkin, familial60502741.531.0310.1513.30
 ENSG00000003400CASP10Lymphoma, non-Hodgkin, familial60502715.166.064.0327.32
 ENSG00000149311ATMLymphoma, non-Hodgkin, familial60502735.3036.3735.5032.61
 ENSG00000085999RAD54LLymphoma, non-Hodgkin, familial60502737.3524.4125.291.00
 ENSG00000143294PRCCRenal cell carcinoma, papillary60507438.75501.0031.7429.59
 ENSG00000068323TFE3Renal cell carcinoma, papillary6050748.0819.5743.822.02
 ENSG00000105976METRenal cell carcinoma, papillary60507431.464.3614.3736.74
 ENSG00000102245TNFL5_HUMANImmunodeficiency with hyper-IgM, type 2605258501.0032.6243.853.11
 ENSG00000101017CD40Immunodeficiency with hyper-IgM, type 26052583.086.1721.2916.37
 ENSG00000111732AICDAImmunodeficiency with hyper-IgM, type 2605258501.0029.4633.6818.50
 ENSG00000139515IPF1Maturity-onset diabetes of the young; MODY606391501.0040.6632.541.00
 ENSG00000135100TCF1Maturity-onset diabetes of the young; MODY606391501.0033.4326.561.01
 ENSG00000101076HNF4AMaturity-onset diabetes of the young; MODY6063919.0840.6420.188.08
 ENSG00000106633GCKMaturity-onset diabetes of the young; MODY60639128.3047.7225.3624.25
 ENSG00000162992NEUROD1Maturity-onset diabetes of the young; MODY6063919.073.0340.574.01
 ENSG00000102245TNFL5_HUMANImmunodeficiency with hyper-IgM, type 3606843501.0031.6045.883.12
 ENSG00000101017CD40Immunodeficiency with hyper-IgM, type 36068434.096.1620.3117.38
 ENSG00000111732AICDAImmunodeficiency with hyper-IgM, type 3606843501.0029.5130.6616.50
 ENSG00000022355GABRA1Myoclonic epilepsy, juvenile; JME60690436.3026.2537.4932.51
 ENSG00000096093EFHC1Myoclonic epilepsy, juvenile; JME606904501.00501.00501.006.07
 ENSG00000182389CACNB4Myoclonic epilepsy, juvenile; JME60690445.7737.5047.6723.44
 ENSG00000114859CLCN2Myoclonic epilepsy, juvenile; JME606904501.0011.1841.478.14
 ENSG00000179295PTPN11Juvenile myelomonocytic leukemia60778532.418.0836.655.12
 ENSG00000196712NF1Juvenile myelomonocytic leukemia60778544.643.022.051.00
 ENSG00000133703RASK_HUMANJuvenile myelomonocytic leukemia60778536.392.044.015.04
 ENSG00000168638NRASJuvenile myelomonocytic leukemia6077854.0114.253.015.06
 ENSG00000197499HLA-AMycobacterium tuberculosis, susceptibility to infection by60794849.9138.7042.9334.53
 ENSG00000165471MBL2Mycobacterium tuberculosis, susceptibility to infection by607948501.0032.8849.9513.34
 ENSG00000111424VDRMycobacterium tuberculosis, susceptibility to infection by60794835.8231.6928.5714.37
 ENSG00000108556CHRNEMyasthenic syndrome, congenital, fast-channel608930501.0036.6944.611.00
 ENSG00000138435CHRNA1Myasthenic syndrome, congenital, fast-channel608930501.001.001.001.00
 ENSG00000135902CHRNDMyasthenic syndrome, congenital, fast-channel60893012.121.001.001.00
100 Genes:
 ENSG00000091513TFAlzheimer disease; AD10430070.2980.6986.7446.39
 ENSG00000175899A2MAlzheimer disease; AD10430072.3769.6994.922.02
 ENSG00000143801PSEN2Alzheimer disease; AD10430031.0810.0857.4592.86
 ENSG00000123384LRP1Alzheimer disease; AD1043001001.0044.3139.3233.30
 ENSG00000142192APPAlzheimer disease; AD10430071.3872.6776.6030.20
 ENSG00000130203APOEAlzheimer disease; AD10430034.0876.6368.565.02
 ENSG00000010704HFEAlzheimer disease; AD10430052.1234.7088.611.00
 ENSG00000080815PSEN1Alzheimer disease; AD10430026.0711.0692.7898.98
 ENSG00000101439CST3Amyloidosis VI10515010.2296.9896.9859.71
 ENSG00000165029ABCA1Amyloidosis VI1051501001.0080.9370.813.07
 ENSG00000136156ITM2BAmyloidosis VI10515046.626.1618.3592.99
 ENSG00000171560FGAAmyloidosis, familial visceral1052001001.0088.9887.8571.81
 ENSG00000090382LYZAmyloidosis, familial visceral10520066.7334.2676.8072.80
 ENSG00000118137APOA1Amyloidosis, familial visceral10520014.1959.5428.3058.79
 ENSG00000124164VAPBAmyotrophic lateral sclerosis 1; ALS110540012.0522.1561.5823.17
 ENSG00000003393ALS2Amyotrophic lateral sclerosis 1; ALS11054001001.0038.3292.8368.60
 ENSG00000142168SOD1Amyotrophic lateral sclerosis 1; ALS110540032.102.0026.1192.93
 ENSG00000100285NEFHAmyotrophic lateral sclerosis 1; ALS110540062.3619.1275.5284.76
 ENSG00000135406PRPHAmyotrophic lateral sclerosis 1; ALS110540063.251001.0022.1575.60
 ENSG00000112112COL11A2Stickler syndrome, type I; STL110830083.8926.2913.209.16
 ENSG00000139219COL2A1Stickler syndrome, type I; STL11083001001.0035.3635.3864.69
 ENSG00000060718COL11A1Stickler syndrome, type I; STL11083001001.0026.2914.1910.16
 ENSG00000139687RB1Bladder cancer10980037.4878.8953.5954.71
 ENSG00000068078FGFR3Bladder cancer10980048.5422.3866.7639.62
 ENSG00000174775HRASBladder cancer10980039.4817.1788.82951.00
 ENSG00000139618BRCA2Breast cancer11448062.1723.0616.0221.04
 ENSG00000138376BARD1Breast cancer11448031.0648.226.011.00
 ENSG00000124151NCOA3Breast cancer11448074.3114.0421.0826.05
 ENSG00000121879PIK3CABreast cancer11448037.0775.501.0078.40
 ENSG00000012048BRCA1Breast cancer1144802.0026.1429.109.02
 ENSG00000170836PPM1DBreast cancer11448070.3114.0473.4747.19
 ENSG00000141510TP53Breast cancer11448057.1543.203.0017.04
 ENSG00000023287RB1CC1Breast cancer11448059.1565.4099.8478.40
 ENSG00000183765CHEK2Breast cancer11448010.028.029.0424.07
 ENSG00000169083ARBreast cancer1144801001.0069.3872.3372.38
 ENSG00000105976METHepatocellular carcinoma11455079.8541.4817.314.03
 ENSG00000168036CTNNB1Hepatocellular carcinoma11455042.5290.9832.5319.23
 ENSG00000141510TP53Hepatocellular carcinoma11455072.7752.7087.92961.00
 ENSG00000138109CYP2C9Coumarin resistance12270061.6288.9328.4241.44
 ENSG00000167397VKOR1_HUMANCoumarin resistance12270039.1563.7389.7910.10
 ENSG00000198470CYP2A6Coumarin resistance12270095.9827.2450.7062.72
 ENSG00000101981F9Coumarin resistance1227001001.0036.3044.7029.39
 ENSG00000162992NEUROD1Diabetes mellitus, non–insulin-dependent; NIDDM1258536.0016.048.0273.60
 ENSG00000121653MAPK8IP1Diabetes mellitus, non–insulin-dependent; NIDDM12585382.3240.3038.2213.05
 ENSG00000163581SLC2A2Diabetes mellitus, non–insulin-dependent; NIDDM12585386.4116.035.0150.25
 ENSG00000181856SLC2A4Diabetes mellitus, non–insulin-dependent; NIDDM12585363.1511.039.023.00
 ENSG00000105221AKT2Diabetes mellitus, non–insulin-dependent; NIDDM12585315.0180.7147.3038.11
 ENSG00000101076HNF4ADiabetes mellitus, non–insulin-dependent; NIDDM1258539.0177.5278.5857.22
 ENSG00000142330CAPN10Diabetes mellitus, non–insulin-dependent; NIDDM1258531001.0044.3220.0922.07
 ENSG00000135100TCF1Diabetes mellitus, non–insulin-dependent; NIDDM1258531001.0080.6181.6156.37
 ENSG00000104918RETNDiabetes mellitus, non–insulin-dependent; NIDDM12585395.4682.611001.0015.06
 ENSG00000171867PRNPDementia, Lewy body; DLB12775023.241001.0037.4487.95
 ENSG00000145335SNCADementia, Lewy body; DLB1277508.1190.9850.6973.90
 ENSG00000130203APOEDementia, Lewy body; DLB12775086.9318.4087.9090.94
 ENSG00000070182SPTBElliptocytosis, Rhesus-unlinked type1306001001.007.0937.631.00
 ENSG00000163554SPTA1Elliptocytosis, Rhesus-unlinked type13060059.4512.1723.262.02
 ENSG00000004939SLC4A1Elliptocytosis, Rhesus-unlinked type13060039.3725.3618.169.09
 ENSG00000186847KRT14Epidermolysis bullosa of hands and feet1318001001.0013.041.0113.01
 ENSG00000186081KRT5Epidermolysis bullosa of hands and feet1318001001.008.048.032.00
 ENSG00000132470ITGB4Epidermolysis bullosa of hands and feet1318001001.0060.5647.4025.21
 ENSG00000114270COL7A1Epidermolysis bullosa of hands and feet13180021.1014.1039.2663.59
 ENSG00000127870RNF6Esophageal cancer13323927.2169.5314.108.04
 ENSG00000134982APCEsophageal cancer1332394.0192.9111.1114.08
 ENSG00000147889CDKN2AEsophageal cancer1332391001.0075.5736.3123.17
 ENSG00000008226DLEC1Esophageal cancer1332391001.0021.163.034.03
 ENSG00000061337LZTS1Esophageal cancer1332391001.0084.7837.2917.09
 ENSG00000141510TP53Esophageal cancer13323967.5669.6177.811.02
 ENSG00000187323DCCEsophageal cancer1332391001.0035.2322.203.03
 ENSG00000186153NP_570606.1Esophageal cancer13323932.1969.5735.291.00
 ENSG00000140522RLBP1Fundus albipunctatus13688093.7765.7291.903.02
 ENSG00000112619RDSFundus albipunctatus1368801001.0037.4483.8269.71
 ENSG00000135437RDH5Fundus albipunctatus1368801001.0055.5539.473.03
 ENSG00000130203APOEFundus albipunctatus13688096.917.0293.926.05
 ENSG00000063169GLTSCR1Glioma of brain, familial13780064.361001.001001.001001.00
 ENSG00000111087GLI1_HUMANGlioma of brain, familial13780017.0731.176.0325.14
 ENSG00000108231LGI1Glioma of brain, familial13780045.171001.001001.001001.00
 ENSG00000147889CDKN2AGlioma of brain, familial1378001001.0097.9627.2062.45
 ENSG00000146648EGFRGlioma of brain, familial1378002.003.0050.4232.19
 ENSG00000132170PPARGGlioma of brain, familial13780050.2265.6422.1497.90
 ENSG00000117298ECE1Hirschsprung disease14262338.1887.7247.3756.50
 ENSG00000136160EDNRBHirschsprung disease1426231001.0044.3464.4629.14
 ENSG00000169554ZFHX1BHirschsprung disease1426239.0270.5413.095.08
 ENSG00000165731RETHirschsprung disease14262343.2156.5447.3338.29
 ENSG00000124205EDN3Hirschsprung disease1426231001.0027.2141.3112.10
 ENSG00000168621GDNFHirschsprung disease1426231001.0070.5439.2344.47
 ENSG00000130164LDLRHypercholesterolemia, familial14389023.223.045.048.04
 ENSG00000055955ITIH4Hypercholesterolemia, familial14389046.3925.2750.4499.99
 ENSG00000158874APOA2Hypercholesterolemia, familial1438901001.0013.149.0824.20
 ENSG00000169174PCSK9Hypercholesterolemia, familial14389011.0940.4449.4644.35
 ENSG00000120915EPHX2Hypercholesterolemia, familial14389054.3180.8291.8426.18
 ENSG00000084674APOBHypercholesterolemia, familial14389050.611.012.0525.23
 ENSG00000111664GNB3Hypertension, essential1455002.0035.2455.2629.13
 ENSG00000124212PTGISHypertension, essential14550068.3964.5669.5236.28
 ENSG00000087274NP_789771.1Hypertension, essential14550028.0675.5865.5595.90
 ENSG00000106258CYP3A5Hypertension, essential14550074.4635.2981.6347.32
 ENSG00000028137TNFRSF1BHypertension, essential14550036.1047.3371.5423.12
 ENSG00000144891AGTR1Hypertension, essential14550040.152.0128.1665.46
 ENSG00000135744AGTHypertension, essential14550043.1919.1348.2958.47
 ENSG00000105227PRXHypertrophic neuropathy of Dejerine-Sottas1459001001.001.014.0359.64
 ENSG00000109099PMP22Hypertrophic neuropathy of Dejerine-Sottas14590092.9246.4172.718.08
 ENSG00000122877EGR2Hypertrophic neuropathy of Dejerine-Sottas14590026.2063.7593.9748.58
 ENSG00000158887MPZHypertrophic neuropathy of Dejerine-Sottas1459001001.007.073.041.02
 ENSG00000169083ARHypospadias1464501001.0087.9765.6651.58
 ENSG00000101871MID1Hypospadias1464501001.0017.0864.587.04
 ENSG00000049319SRD5A2Hypospadias14645032.3913.0984.8341.46
 ENSG00000005471ABCB4Cholestasis, intrahepatic, of pregnancy; ICP1474801001.0031.4364.5363.66
 ENSG00000073734ABCB11Cholestasis, intrahepatic, of pregnancy; ICP1474801001.0094.9791.9537.41
 ENSG00000081923ATP8B1Cholestasis, intrahepatic, of pregnancy; ICP1474808.0318.2668.5972.78
 ENSG00000167768KRT1Keratosis palmoplantaris striata I14870079.8542.46951.004.01
 ENSG00000134760DSG1Keratosis palmoplantaris striata I1487001001.0013.2730.374.01
 ENSG00000096696DSPKeratosis palmoplantaris striata I14870034.232.031.011.01
 ENSG00000111275ALDH2Leiomyoma, uterine15069983.7833.3710.1037.45
 ENSG00000143196DPTLeiomyoma, uterine15069971.481001.001001.0012.05
 ENSG00000182185RAD51L1Leiomyoma, uterine1506991001.0081.8811.1159.64
 ENSG00000164919COX6CLeiomyoma, uterine15069928.1130.2413.0819.24
 ENSG00000087237CETPLongevity15243047.2865.7132.2649.55
 ENSG00000108599AKAP10Longevity152430100.9727.2219.1326.29
 ENSG00000136869TLR4Longevity15243029.1515.1729.2342.53
 ENSG00000005421PON1Longevity1524301001.007.1011.0445.42
 ENSG00000126594DNASE1Lupus erythematosus, systemic; SLE15270087.7455.5288.9577.81
 ENSG00000163599CTLA4Lupus erythematosus, systemic; SLE1527001001.0061.651001.0051.59
 ENSG00000134242PTPN22Lupus erythematosus, systemic; SLE15270072.5644.4138.3339.27
 ENSG00000143226FCGR2ALupus erythematosus, systemic; SLE15270022.1111.0723.1159.44
 ENSG00000168036CTNNB1Medulloblastoma15525546.4213.114.055.09
 ENSG00000134982APCMedulloblastoma1552553.0187.931.012.02
 ENSG00000117425PTCH2Medulloblastoma1552551001.0071.7050.5454.50
 ENSG00000107882SUFUMedulloblastoma15525543.3264.7229.3659.63
 ENSG00000107815PEO1Progressive external ophthalmoplegia with mtDNA deletions; PEO15764011.101011.00911.0073.88
 ENSG00000151729SLC25A4Progressive external ophthalmoplegia with mtDNA deletions; PEO1576407.0271.7978.8236.62
 ENSG00000140521POLGProgressive external ophthalmoplegia with mtDNA deletions; PEO15764077.929.0536.5690.97
 ENSG00000136827TOR1AMyoclonic dystonia15990025.2161.7219.2267.87
 ENSG00000127990SGCEMyoclonic dystonia15990026.3521.1873.8669.86
 ENSG00000149295DRD2Myoclonic dystonia15990018.2740.3449.421.01
 ENSG00000143632ACTA1Nemaline myopathy 1, autosomal dominant; NEM116180072.8359.6818.2512.15
 ENSG00000143549TPM3Nemaline myopathy 1, autosomal dominant; NEM116180011.0655.7522.3813.17
 ENSG00000198467TPM2Nemaline myopathy 1, autosomal dominant; NEM116180067.614.034.0411.15
 ENSG00000179142CYP11B2IgA nephropathy1619501001.0023.32951.0038.53
 ENSG00000135744AGTIgA nephropathy16195084.8919.1827.6767.80
 ENSG00000159640ACEIgA nephropathy1619502.0061.6875.9150.65
 ENSG00000007908SELEIgA nephropathy1619501001.0024.1820.241.01
 ENSG00000133256PDE6BNight blindness, congenital stationary; CSNB31635001001.0074.909.0969.76
 ENSG00000114349GNAT1Night blindness, congenital stationary; CSNB316350097.9630.2734.2657.60
 ENSG00000163914RHONight blindness, congenital stationary; CSNB31635001001.0082.8679.8383.94
 ENSG00000108576SLC6A4Obsessive-compulsive disorder 1; OCD116423012.109.0815.2688.97
 ENSG00000102468HTR2AObsessive-compulsive disorder 1; OCD116423084.7351.6889.9645.55
 ENSG00000176697BDNFObsessive-compulsive disorder 1; OCD11642301001.0020.2628.2246.67
 ENSG00000004948CALCROsteoporosis, involutional1667101001.0014.1160.6255.53
 ENSG00000136244IL6Osteoporosis, involutional16671019.1060.6274.7921.17
 ENSG00000162337LRP5Osteoporosis, involutional16671030.1113.1047.4515.09
 ENSG00000108821COL1A1Osteoporosis, involutional16671012.074.011.0173.50
 ENSG00000111424VDROsteoporosis, involutional16671093.7287.8936.3160.52
 ENSG00000145335SNCAParkinson disease; PD16860051.3742.4047.561.00
 ENSG00000154277UCHL1Parkinson disease; PD16860037.2482.8331.334.06
 ENSG00000185345PARK2Parkinson disease; PD16860092.8838.3016.121.01
 ENSG00000064692SNCAIPParkinson disease; PD16860025.081001.0081.631.00
 ENSG00000081248CACNA1SHypokalemic periodic paralysis; HOKPP1704001001.0070.8259.765.12
 ENSG00000175538KCNE3Hypokalemic periodic paralysis; HOKPP1704001001.0033.2714.267.06
 ENSG00000007314SCN4AHypokalemic periodic paralysis; HOKPP17040047.5751.5833.7254.77
 ENSG00000165731RETPheochromocytoma17130091.9254.5412.0764.88
 ENSG00000117118SDHBPheochromocytoma17130059.7051.6444.5150.70
 ENSG00000196712NF1Pheochromocytoma17130088.9284.9360.5653.62
 ENSG00000183765CHEK2Prostate cancer17680725.2152.4831.4114.08
 ENSG00000085117CD82Prostate cancer17680722.091001.0057.6176.78
 ENSG00000067082KLF6Prostate cancer17680753.2894.8878.6927.22
 ENSG00000133216EPHB2Prostate cancer17680766.4735.271001.0053.43
 ENSG00000135828RNASELProstate cancer1768071001.0060.3566.6625.15
 ENSG00000038945MSR1Prostate cancer1768071001.0024.1638.2894.83
 ENSG00000171862PTEN_HUMANProstate cancer1768076.0120.076.0273.57
 ENSG00000006744RNZ2_HUMANProstate cancer17680758.3621.1066.6360.53
 ENSG00000002822MAD1L1Prostate cancer17680776.6159.5547.5189.78
 ENSG00000159339PADI4Rheumatoid arthritis1803001001.0077.9233.2955.58
 ENSG00000168593NFKBIL1Rheumatoid arthritis1803001001.0058.4164.6072.74
 ENSG00000159216RUNX1Rheumatoid arthritis18030043.2980.7285.8585.89
 ENSG00000134242PTPN22Rheumatoid arthritis18030057.3662.5989.9162.57
 ENSG00000197208SLC22A4Rheumatoid arthritis18030035.1740.4752.4417.14
 ENSG00000138293NCOA4Thyroid carcinoma, papillary1885507.0472.9170.821001.00
 ENSG00000108946PRKAR1AThyroid carcinoma, papillary1885507.0347.4531.4049.73
 ENSG00000198400NTRK1Thyroid carcinoma, papillary1885501001.0038.4131.2956.42
 ENSG00000114354TFGThyroid carcinoma, papillary18855070.5124.3727.3830.30
 ENSG00000047410TPRThyroid carcinoma, papillary18855016.0648.3611.0921.21
 ENSG00000129991TNNI3Cardiomyopathy, familial hypertrophic; CMH1926001001.0067.6370.7218.05
 ENSG00000160808MYL3Cardiomyopathy, familial hypertrophic; CMH19260017.021.001.001.01
 ENSG00000140416TPM1Cardiomyopathy, familial hypertrophic; CMH19260047.211.005.052.01
 ENSG00000155657NP_59687.1Cardiomyopathy, familial hypertrophic; CMH19260035.081.0112.0841.29
 ENSG00000092054MYH7Cardiomyopathy, familial hypertrophic; CMH19260052.145.0334.2342.32
 ENSG00000111245MYL2Cardiomyopathy, familial hypertrophic; CMH19260094.581.001.003.03
 ENSG00000118194TNNT2Cardiomyopathy, familial hypertrophic; CMH19260045.104.0393.781.00
 ENSG00000134571MYBPC3Cardiomyopathy, familial hypertrophic; CMH1926001011.001.007.0611.14
 ENSG00000101306MYLK2Cardiomyopathy, familial hypertrophic; CMH1926001001.0047.4037.4442.38
 ENSG00000183785PEX26Adrenoleukodystrophy, autosomal neonatal form20237075.461.005.0114.06
 ENSG00000162928PEX13Adrenoleukodystrophy, autosomal neonatal form20237097.941.0086.671.01
 ENSG00000127980PEX1Adrenoleukodystrophy, autosomal neonatal form20237091.6433.3774.508.04
 ENSG00000157911PEX10Adrenoleukodystrophy, autosomal neonatal form20237082.462.0079.6110.03
 ENSG00000139197PEX5Adrenoleukodystrophy, autosomal neonatal form20237062.272.001.004.01
 ENSG00000047579DTNBP1Hermansky-Pudlak syndrome; HPS20330078.5121.1444.2793.89
 ENSG00000107521HPS1Hermansky-Pudlak syndrome; HPS20330087.838.0725.2129.24
 ENSG00000132842AP3B1Hermansky-Pudlak syndrome; HPS20330033.141001.0052.4134.42
 ENSG00000110756HPS5Hermansky-Pudlak syndrome; HPS20330096.941001.0044.4350.46
 ENSG00000100099HPS4Hermansky-Pudlak syndrome; HPS20330067.5084.8387.8313.14
 ENSG00000163755HPS3Hermansky-Pudlak syndrome; HPS2033001001.001001.001001.001001.00
 ENSG00000092200RPGRIP1Leber congenital amaurosis, type I; LCA120400030.151.024.0316.07
 ENSG00000132518GUCY2DLeber congenital amaurosis, type I; LCA12040001001.0037.2449.2671.68
 ENSG00000116745RPE65Leber congenital amaurosis, type I; LCA120400045.1535.2346.2764.56
 ENSG00000139988RDH12_HUMANLeber congenital amaurosis, type I; LCA120400040.2031.2614.0873.51
 ENSG00000134376CRB1Leber congenital amaurosis, type I; LCA12040001011.005.0139.191.00
 ENSG00000105392CRXLeber congenital amaurosis, type I; LCA120400056.2153.367.0522.09
 ENSG00000112041TULP1Leber congenital amaurosis, type I; LCA120400013.045.0250.4059.49
 ENSG00000165731RETAutonomic control, congenital failure of20988038.1381.8442.3212.12
 ENSG00000124205EDN3Autonomic control, congenital failure of2098801001.0023.3020.1771.77
 ENSG00000109132PHOX2BAutonomic control, congenital failure of20988079.4569.603.023.02
 ENSG00000176697BDNFAutonomic control, congenital failure of2098801001.0086.8584.7135.39
 ENSG00000168621GDNFAutonomic control, congenital failure of2098801001.0030.1880.5613.12
 ENSG00000165533TTC8Bardet-Biedl syndrome; BBS2099001001.0015.1012.059.02
 ENSG00000174483DPP3Bardet-Biedl syndrome; BBS20990093.6937.1951.3431.22
 ENSG00000138686BBS7_HUMANBardet-Biedl syndrome; BBS20990033.121.0173.5286.82
 ENSG00000125124BBS2Bardet-Biedl syndrome; BBS2099001001.002.011.002.02
 ENSG00000125863MKKSBardet-Biedl syndrome; BBS20990057.2380.6387.6773.59
 ENSG00000140463BBS4Bardet-Biedl syndrome; BBS20990031.1523.109.055.03
 ENSG00000113966ARL6Bardet-Biedl syndrome; BBS2099001001.0030.2145.3935.28
 ENSG00000163093BBS5Bardet-Biedl syndrome; BBS20990012.041001.0011.0514.05
 ENSG00000133111RFXAPBare lymphocyte syndrome, type II2099209.0971.7748.521.00
 ENSG00000179583MHC2TABare lymphocyte syndrome, type II2099201001.0017.2218.091.00
 ENSG00000064490RFXANKBare lymphocyte syndrome, type II20992036.2576.7146.261.00
 ENSG00000143390RFX5Bare lymphocyte syndrome, type II20992073.6568.754.031.00
 ENSG00000159128IFNGR2Atypical mycobacteriosis, familial2099503.018.056.033.02
 ENSG00000096996IL12RB1Atypical mycobacteriosis, familial2099501001.007.0820.176.03
 ENSG00000027697IFNGR1Atypical mycobacteriosis, familial20995029.187.056.062.02
 ENSG00000113302IL12BAtypical mycobacteriosis, familial2099501001.009.1050.443.01
 ENSG00000115415STAT1Atypical mycobacteriosis, familial20995076.6358.6025.1349.45
 ENSG00000073734ABCB11Cholestasis, progressive familial intrahepatic 1; PFIC12116001001.0095.9785.9533.31
 ENSG00000005471ABCB4Cholestasis, progressive familial intrahepatic 1; PFIC12116001001.0051.5574.6878.78
 ENSG00000081923ATP8B1Cholestasis, progressive familial intrahepatic 1; PFIC12116004.0313.2484.8178.82
 ENSG00000099377HSD3B7Cholestasis, progressive familial intrahepatic 1; PFIC121160041.145.065.0336.31
 ENSG00000133703RASK_HUMANLung cancer21198049.4063.7112.1615.14
 ENSG00000157764BRAFLung cancer21198022.0815.173.043.02
 ENSG00000141510TP53Lung cancer2119802.0258.6143.601001.00
 ENSG00000146648EGFRLung cancer21198065.6043.4217.1810.13
 ENSG00000139197PEX5Zellweger syndrome; ZS21410021.0218.042.011.00
 ENSG00000142655PEX14Zellweger syndrome; ZS21410021.045.012.012.00
 ENSG00000127980PEX1Zellweger syndrome; ZS21410053.1810.092.014.03
 ENSG00000124587PEX6Zellweger syndrome; ZS21410039.126.071.0211.11
 ENSG00000108733PEX12Zellweger syndrome; ZS21410081.4454.4657.401.00
 ENSG00000164751PXMP3Zellweger syndrome; ZS21410053.1975.5986.671.00
 ENSG00000183785PEX26Zellweger syndrome; ZS21410048.1811.0612.065.03
 ENSG00000034693PEX3Zellweger syndrome; ZS2141001.001.0041.191.00
 ENSG00000172660TAF15Chondrosarcoma21530080.7572.7480.6935.35
 ENSG00000182197EXT1Chondrosarcoma21530035.1716.1789.862.03
 ENSG00000151348EXT2Chondrosarcoma2153004.0210.1572.664.07
 ENSG00000182944EWSR1Chondrosarcoma21530016.0691.9514.1215.17
 ENSG00000134183GNAT2Achromatopsia 2; ACHM221690050.5568.8690.9888.97
 ENSG00000144191CNGA3Achromatopsia 2; ACHM221690091.9034.326.0674.83
 ENSG00000170289CNGB3Achromatopsia 2; ACHM22169001001.0053.7620.2838.58
 ENSG00000065618COHA1_HUMANEpidermolysis bullosa junctionalis, disentis type2266508.0853.4836.3647.38
 ENSG00000132470ITGB4Epidermolysis bullosa junctionalis, disentis type2266501001.0052.5825.2785.93
 ENSG00000196878LAMB3Epidermolysis bullosa junctionalis, disentis type2266501001.0013.114.0736.41
 ENSG00000053747LAMA3Epidermolysis bullosa junctionalis, disentis type22665095.9450.5060.6218.17
 ENSG00000165281FANCGFanconi anemia; FA22765059.2173.5729.131.00
 ENSG00000144554FANCD2Fanconi anemia; FA2276501001.0017.0919.101.00
 ENSG00000183161FANCFFanconi anemia; FA22765025.0914.1021.121.00
 ENSG00000158169FANCCFanconi anemia; FA22765042.2418.0943.331.00
 ENSG00000115392FANCLFanconi anemia; FA22765080.6970.569.064.01
 ENSG00000112039FANCEFanconi anemia; FA2276501.0020.0921.161.00
 ENSG00000105379ETFBGlutaricaciduria IIA2316801.015.091.001.00
 ENSG00000140374ETFAGlutaricaciduria IIA2316802.001.071.011.00
 ENSG00000171503ETFDHGlutaricaciduria IIA2316801.0022.331.001.00
 ENSG00000188690UROSHydrops fetalis, idiopathic23675029.1791.942.0328.25
 ENSG00000169919GUSBHydrops fetalis, idiopathic23675091.8475.8543.3681.93
 ENSG00000177628GBAHydrops fetalis, idiopathic23675081.7974.8958.6452.65
 ENSG00000188536HBA1Hydrops fetalis, idiopathic23675020.1282.9654.5042.37
 ENSG00000179477ALOX12BIchthyosiform erythroderma, congenital, nonbullous, 1; NCIE12421001001.0052.7417.1714.17
 ENSG00000011198ABHD5Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE124210031.223.041.012.02
 ENSG00000092295TGM1Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE124210048.554.054.0685.90
 ENSG00000122735DNAI1Kartagener syndrome2444003.031001.0013.2224.22
 ENSG00000039139DNAH5Kartagener syndrome2444001001.002.031.0114.11
 ENSG00000105877DNAH11Kartagener syndrome2444001001.004.032.025.08
 ENSG00000112964GHRLaron syndrome, type II2455901001.0042.3513.124.04
 ENSG00000189162GH1Laron syndrome, type II2455901001.006.0512.126.10
 ENSG00000173757STAT5BLaron syndrome, type II24559032.3674.9178.9333.37
 ENSG00000137992DBTMaple syrup urine disease, type IA24860087.8154.4776.683.01
 ENSG00000091140DLDMaple syrup urine disease, type IA24860033.1878.8342.271.02
 ENSG00000142046BCKDHAMaple syrup urine disease, type IA2486004.0323.278.091.00
 ENSG00000083123BCKDHBMaple syrup urine disease, type IA24860053.409.0663.491.00
 ENSG00000167792NDUFV1Complex I, mitochondrial respiratory chain, deficiency of25201065.406.035.017.02
 ENSG00000115286NDUFS7Complex I, mitochondrial respiratory chain, deficiency of25201063.352.0112.049.05
 ENSG00000164258NDUFS4Complex I, mitochondrial respiratory chain, deficiency of25201068.561.011.011.00
 ENSG00000023228NDUFS1Complex I, mitochondrial respiratory chain, deficiency of25201074.536.011.002.00
 ENSG00000158864NDUFS2Complex I, mitochondrial respiratory chain, deficiency of25201037.205.012.001.00
 ENSG00000110092CCND1Myeloma, multiple2545002.0086.8393.9524.22
 ENSG00000130076IGHG3Myeloma, multiple25450018.1338.4490.9194.97
 ENSG00000068078FGFR3Myeloma, multiple25450065.662.0461.6222.18
 ENSG00000137265IRF4Myeloma, multiple25450054.4453.6646.3747.48
 ENSG00000115286NDUFS7Leigh syndrome; LS25600019.022.011.001.00
 ENSG00000074582BCS1LLeigh syndrome; LS25600020.0560.5236.2753.41
 ENSG00000073578SDHALeigh syndrome; LS2560005.0117.1147.3926.16
 ENSG00000110536NDUFS3Leigh syndrome; LS2560006.012.011.011.02
 ENSG00000091140DLDLeigh syndrome; LS25600096.654.0465.4936.31
 ENSG00000110717NDUFS8Leigh syndrome; LS2560004.012.002.012.01
 ENSG00000006071ABCC8Nesidioblastosis of pancreas25645086.8475.9559.8486.97
 ENSG00000106633GCKNesidioblastosis of pancreas25645093.8934.3931.2862.77
 ENSG00000148672GLUD1Nesidioblastosis of pancreas25645035.167.0936.5866.73
 ENSG00000141655TNFRSF11AOsteogenic sarcoma2595001001.0017.1820.2512.26
 ENSG00000139687RB1Osteogenic sarcoma25950056.5082.9153.5221.24
 ENSG00000183765CHEK2Osteogenic sarcoma25950095.9427.3985.8972.87
 ENSG00000081087OSTM1Osteopetrosis, autosomal recessive25970010.161001.0036.4835.46
 ENSG00000103249CLCN7Osteopetrosis, autosomal recessive25970091.9380.8470.7674.92
 ENSG00000110719TCIRG1Osteopetrosis, autosomal recessive25970029.4537.3530.2944.58
 ENSG00000133703RASK_HUMANPancreatic carcinoma26035030.1111.096.0316.11
 ENSG00000141510TP53Pancreatic carcinoma26035033.2183.8036.3267.61
 ENSG00000145050ARMETPancreatic carcinoma26035043.301001.0092.8983.75
 ENSG00000147889CDKN2APancreatic carcinoma2603501001.008.0997.9296.93
 ENSG00000141646SMAD4Pancreatic carcinoma2603503.0385.8372.7370.65
 ENSG00000139618BRCA2Pancreatic carcinoma26035083.7490.8584.8325.15
 ENSG00000144191CNGA3Achromatopsia 3; ACHM326230091.8931.288.0772.81
 ENSG00000134183GNAT2Achromatopsia 3; ACHM326230052.5969.8592.9782.96
 ENSG00000170289CNGB3Achromatopsia 3; ACHM32623001001.0051.7020.2839.58
 ENSG00000107187LHX3Pituitary dwarfism III26260040.3190.9873.7788.99
 ENSG00000163666HESX1Pituitary dwarfism III2626001011.0093.9844.686.04
 ENSG00000175325PROP1Pituitary dwarfism III2626001001.0074.8117.214.03
 ENSG00000111319SCNN1APseudohypoaldosteronism, type I, autosomal recessive; PHA126435011.171.013.037.22
 ENSG00000168447SCNN1BPseudohypoaldosteronism, type I, autosomal recessive; PHA126435044.622.011.0017.17
 ENSG00000151623NR3C2Pseudohypoaldosteronism, type I, autosomal recessive; PHA126435056.5686.9592.9726.37
 ENSG00000168878SFTPBPulmonary alveolar proteinosis2651201001.002.0511.1529.59
 ENSG00000100368CSF2RBPulmonary alveolar proteinosis2651201001.0075.9167.9965.89
 ENSG00000168484SFTPCPulmonary alveolar proteinosis2651201001.002.021.0256.77
 ENSG00000164751PXMP3Refsum disease, infantile form26651062.6237.3248.5192.98
 ENSG00000127980PEX1Refsum disease, infantile form26651010.1288.9767.791.00
 ENSG00000183785PEX26Refsum disease, infantile form26651044.422.033.021.00
 ENSG00000197375SLC22A5Inflammatory bowel disease 1; IBD126660018.1258.769.065.04
 ENSG00000151208DLG5Inflammatory bowel disease 1; IBD126660099.9766.6115.2174.83
 ENSG00000167207CARD15Inflammatory bowel disease 1; IBD12666001001.0078.8935.3617.15
 ENSG00000085563ABCB1Inflammatory bowel disease 1; IBD12666001001.0039.5023.2015.20
 ENSG00000092929UNC13DReticulosis, familial histiocytic2677001001.0016.2113.1928.40
 ENSG00000166349RAG1Reticulosis, familial histiocytic2677001001.0050.6177.88971.00
 ENSG00000180644PRF1Reticulosis, familial histiocytic26770014.121001.0025.2783.92
 ENSG00000135903PAX3Rhabdomyosarcoma 2; RMS226822037.2564.7259.7293.99
 ENSG00000150907FOXO1ARhabdomyosarcoma 2; RMS226822053.3180.9149.5378.95
 ENSG00000009709PAX7Rhabdomyosarcoma 2; RMS226822017.1378.8065.5763.61
 ENSG00000108576SLC6A4Sudden infant death syndrome27212056.6129.4116.2786.97
 ENSG00000183873SCN5ASudden infant death syndrome27212091.956.107.0711.17
 ENSG00000053918KCNQ1Sudden infant death syndrome2721201001.009.095.1231.41
 ENSG00000163599CTLA4Graves disease2750001001.0010.111001.0079.97
 ENSG00000145321GCGraves disease27500018.2169.7858.4522.14
 ENSG00000111424VDRGraves disease27500035.2478.9312.094.08
 ENSG00000134982APCTurcot syndrome2763002.0188.963.0314.15
 ENSG00000076242MLH1Turcot syndrome27630057.641.0050.7214.18
 ENSG00000122512PMS2Turcot syndrome2763002.031.007.102.04
 ENSG00000100146SOX10Waardenburg-Shah syndrome27758045.4728.1793.9879.93
 ENSG00000124205EDN3Waardenburg-Shah syndrome2775801001.0085.9668.844.09
 ENSG00000136160EDNRBWaardenburg-Shah syndrome2775801001.001.011.0135.35
 ENSG00000112357PEX7Rhizomelic chondrodysplasia punctata, type 3; RCDP360012170.5617.2259.7078.90
 ENSG00000116906GNPATRhizomelic chondrodysplasia punctata, type 3; RCDP360012167.7265.7366.769.06
 ENSG00000018510AGPSRhizomelic chondrodysplasia punctata, type 3; RCDP360012150.576.0362.692.02
 ENSG00000170175CHRNB1Myasthenic syndrome, slow-channel congenital; SCCMS60146214.1330.2918.2567.79
 ENSG00000135902CHRNDMyasthenic syndrome, slow-channel congenital; SCCMS60146245.271.001.0033.32
 ENSG00000138435CHRNA1Myasthenic syndrome, slow-channel congenital; SCCMS6014621001.001.001.003.02
 ENSG00000175426PCSK1Obesity60166513.0342.2820.1220.09
 ENSG00000115138POMCObesity60166572.4557.5573.622.00
 ENSG00000116678LEPRObesity6016655.0063.6028.242.00
 ENSG00000166603MC4RObesity6016651001.005.0368.574.04
 ENSG00000174483DPP3Obesity60166514.0756.492.024.02
 ENSG00000174697LEPObesity6016651001.0087.8292.8120.12
 ENSG00000130203APOEObesity60166525.1415.1863.4043.23
 ENSG00000142156COL6A1Ossification of the posterior longitudinal ligament of spine; OPLL6024751001.0027.2921.2043.47
 ENSG00000112112COL11A2Ossification of the posterior longitudinal ligament of spine; OPLL60247594.9023.3215.267.17
 ENSG00000197594ENPP1Ossification of the posterior longitudinal ligament of spine; OPLL6024751001.0035.4051.6426.43
 ENSG00000115211EIF2B4Leukoencephalopathy with vanishing white matter; VWM6038963.001.001.002.01
 ENSG00000070785EIF2B3Leukoencephalopathy with vanishing white matter; VWM6038964.001.002.005.01
 ENSG00000111361EIF2B1Leukoencephalopathy with vanishing white matter; VWM6038963.003.001.002.00
 ENSG00000119718EIF2B2Leukoencephalopathy with vanishing white matter; VWM6038964.001.001.001.00
 ENSG00000145191EIF2B5Leukoencephalopathy with vanishing white matter; VWM6038961.002.001.003.01
 ENSG00000138061CYP1B1Peters anomaly60422917.1440.3232.389.07
 ENSG00000054598FOXC1Peters anomaly60422916.0463.7494.8897.97
 ENSG00000164093PITX2Peters anomaly6042291001.0080.778.0868.81
 ENSG00000007372PAX6Peters anomaly60422990.8464.7138.404.04
 ENSG00000144285SCN1AGeneralized epilepsy with febrile seizures plus; GEFS+6042331.019.063.019.06
 ENSG00000113327GABRG2Generalized epilepsy with febrile seizures plus; GEFS+6042338.0425.3681.8465.86
 ENSG00000105711SCN1BGeneralized epilepsy with febrile seizures plus; GEFS+60423328.296.054.0291.92
 ENSG00000157764BRAFLymphoma, non-Hodgkin, familial60502780.6610.0948.4368.69
 ENSG00000003400CASP10Lymphoma, non-Hodgkin, familial60502742.291.016.053.05
 ENSG00000149311ATMLymphoma, non-Hodgkin, familial60502759.3088.8423.1489.96
 ENSG00000085999RAD54LLymphoma, non-Hodgkin, familial60502792.6933.317.074.01
 ENSG00000143294PRCCRenal cell carcinoma, papillary60507458.651001.0014.2025.37
 ENSG00000068323TFE3Renal cell carcinoma, papillary60507427.3058.9092.987.07
 ENSG00000105976METRenal cell carcinoma, papillary6050745.0225.4550.6882.98
 ENSG00000102245TNFL5_HUMANImmunodeficiency with hyper-IgM, type 26052581001.006.0829.322.04
 ENSG00000101017CD40Immunodeficiency with hyper-IgM, type 260525825.2420.1255.664.09
 ENSG00000111732AICDAImmunodeficiency with hyper-IgM, type 26052581001.0033.2673.6519.32
 ENSG00000139515IPF1Maturity-onset diabetes of the young; MODY6063911001.0078.7186.741.00
 ENSG00000135100TCF1Maturity-onset diabetes of the young; MODY6063911001.0089.8161.532.05
 ENSG00000101076HNF4AMaturity-onset diabetes of the young; MODY60639132.1478.7565.4719.10
 ENSG00000106633GCKMaturity-onset diabetes of the young; MODY60639175.4162.5060.5215.10
 ENSG00000162992NEUROD1Maturity-onset diabetes of the young; MODY6063914.0129.162.0010.07
 ENSG00000102245TNFL5_HUMANImmunodeficiency with hyper-IgM, type 36068431001.005.0728.304.09
 ENSG00000101017CD40Immunodeficiency with hyper-IgM, type 360684326.2610.0953.635.11
 ENSG00000111732AICDAImmunodeficiency with hyper-IgM, type 36068431001.0034.2572.6832.34
 ENSG00000022355GABRA1Myoclonic epilepsy, juvenile; JME60690454.3147.4156.429.06
 ENSG00000096093EFHC1Myoclonic epilepsy, juvenile; JME6069041001.001001.001001.0025.30
 ENSG00000182389CACNB4Myoclonic epilepsy, juvenile; JME60690495.8171.7143.3474.76
 ENSG00000114859CLCN2Myoclonic epilepsy, juvenile; JME6069041001.002.0017.0921.17
 ENSG00000179295PTPN11Juvenile myelomonocytic leukemia60778579.775.0660.7524.35
 ENSG00000196712NF1Juvenile myelomonocytic leukemia60778542.265.0613.121.00
 ENSG00000133703RASK_HUMANJuvenile myelomonocytic leukemia60778573.459.098.0513.12
 ENSG00000168638NRASJuvenile myelomonocytic leukemia60778524.0556.574.0219.16
 ENSG00000197499HLA-AMycobacterium tuberculosis, susceptibility to infection by60794846.6027.3350.7665.80
 ENSG00000165471MBL2Mycobacterium tuberculosis, susceptibility to infection by6079481001.0027.3311.2240.61
 ENSG00000111424VDRMycobacterium tuberculosis, susceptibility to infection by60794892.9883.9683.9045.68
 ENSG00000108556CHRNEMyasthenic syndrome, congenital, fast-channel6089301001.0036.3093.921.00
 ENSG00000138435CHRNA1Myasthenic syndrome, congenital, fast-channel6089301001.001.001.001.00
 ENSG00000135902CHRNDMyasthenic syndrome, congenital, fast-channel60893039.272.002.013.01
150 Genes:
 ENSG00000091513TFAlzheimer disease; AD104300121.53116.81135.9027.15
 ENSG00000175899A2MAlzheimer disease; AD10430098.31109.71144.9610.06
 ENSG00000143801PSEN2Alzheimer disease; AD10430078.1732.09109.61146.97
 ENSG00000123384LRP1Alzheimer disease; AD1043001501.0076.4472.4545.25
 ENSG00000142192APPAlzheimer disease; AD10430061.2797.83115.7251.22
 ENSG00000130203APOEAlzheimer disease; AD10430063.158.021.0025.10
 ENSG00000010704HFEAlzheimer disease; AD10430048.11117.76138.762.01
 ENSG00000080815PSEN1Alzheimer disease; AD10430060.1421.07143.94120.79
 ENSG00000101439CST3Amyloidosis VI10515048.391411.0093.74115.91
 ENSG00000165029ABCA1Amyloidosis VI1051501501.00128.9829.296.10
 ENSG00000136156ITM2BAmyloidosis VI10515099.7625.2949.5053.53
 ENSG00000171560FGAAmyloidosis, familial visceral1052001511.001501.00136.95120.93
 ENSG00000090382LYZAmyloidosis, familial visceral105200108.8926.20125.9455.48
 ENSG00000118137APOA1Amyloidosis, familial visceral10520046.3035.2249.31110.90
 ENSG00000124164VAPBAmyotrophic lateral sclerosis 1; ALS110540044.1054.2679.4478.43
 ENSG00000003393ALS2Amyotrophic lateral sclerosis 1; ALS11054001501.0077.38104.58103.67
 ENSG00000142168SOD1Amyotrophic lateral sclerosis 1; ALS110540060.236.0266.32137.98
 ENSG00000100285NEFHAmyotrophic lateral sclerosis 1; ALS1105400109.4724.13127.76140.92
 ENSG00000135406PRPHAmyotrophic lateral sclerosis 1; ALS1105400107.401501.0054.28129.81
 ENSG00000112112COL11A2Stickler syndrome, type I; STL1108300142.9829.3630.2723.28
 ENSG00000139219COL2A1Stickler syndrome, type I; STL11083001501.0062.5266.44110.87
 ENSG00000060718COL11A1Stickler syndrome, type I; STL11083001511.0056.4132.2533.33
 ENSG00000139687RB1Bladder cancer10980024.16124.982.0223.33
 ENSG00000068078FGFR3Bladder cancer10980034.2158.58110.9024.36
 ENSG00000174775HRASBladder cancer10980085.6656.30144.981431.00
 ENSG00000139618BRCA2Breast cancer114480117.3750.1442.0934.08
 ENSG00000138376BARD1Breast cancer11448088.19110.5621.077.02
 ENSG00000124151NCOA3Breast cancer114480128.4940.1037.1034.05
 ENSG00000121879PIK3CABreast cancer114480106.28123.691.0065.20
 ENSG00000012048BRCA1Breast cancer1144805.0086.3842.1050.14
 ENSG00000170836PPM1DBreast cancer114480110.4614.02124.6696.42
 ENSG00000141510TP53Breast cancer11448028.05114.562.0183.24
 ENSG00000023287RB1CC1Breast cancer11448064.1086.3852.1436.07
 ENSG00000183765CHEK2Breast cancer11448018.0225.0762.2366.23
 ENSG00000169083ARBreast cancer1144801501.00124.72132.7476.21
 ENSG00000105976METHepatocellular carcinoma114550135.9488.7065.4711.07
 ENSG00000168036CTNNB1Hepatocellular carcinoma1145502.0283.6585.7036.35
 ENSG00000141510TP53Hepatocellular carcinoma11455085.48111.8995.69128.92
 ENSG00000138109CYP2C9Coumarin resistance122700125.82129.9376.5696.67
 ENSG00000167397VKOR1_HUMANCoumarin resistance12270060.24113.9258.3036.20
 ENSG00000198470CYP2A6Coumarin resistance12270079.4845.30124.8683.60
 ENSG00000101981F9Coumarin resistance1227001501.0073.4454.3623.20
 ENSG00000162992NEUROD1Diabetes mellitus, non–insulin-dependent; NIDDM1258535.0041.1317.0279.26
 ENSG00000121653MAPK8IP1Diabetes mellitus, non–insulin-dependent; NIDDM125853138.5385.434.022.01
 ENSG00000163581SLC2A2Diabetes mellitus, non–insulin-dependent; NIDDM125853130.4529.068.01112.50
 ENSG00000181856SLC2A4Diabetes mellitus, non–insulin-dependent; NIDDM125853122.2915.0817.062.00
 ENSG00000105221AKT2Diabetes mellitus, non–insulin-dependent; NIDDM12585363.05109.7972.3049.16
 ENSG00000101076HNF4ADiabetes mellitus, non–insulin-dependent; NIDDM12585327.02127.85141.87100.48
 ENSG00000142330CAPN10Diabetes mellitus, non–insulin-dependent; NIDDM1258531131.0076.4637.1356.19
 ENSG00000135100TCF1Diabetes mellitus, non–insulin-dependent; NIDDM1258531501.00130.88118.60126.66
 ENSG00000104918RETNDiabetes mellitus, non–insulin-dependent; NIDDM125853104.22105.531501.0059.24
 ENSG00000171867PRNPDementia, Lewy body; DLB12775057.401501.0080.55137.99
 ENSG00000145335SNCADementia, Lewy body; DLB12775026.1743.3293.87128.97
 ENSG00000130203APOEDementia, Lewy body; DLB127750145.9948.59137.9894.80
 ENSG00000070182SPTBElliptocytosis, Rhesus-unlinked type1306001501.007.1182.821.01
 ENSG00000163554SPTA1Elliptocytosis, Rhesus-unlinked type130600101.5826.2667.452.02
 ENSG00000004939SLC4A1Elliptocytosis, Rhesus-unlinked type13060097.6145.5026.2021.15
 ENSG00000186847KRT14Epidermolysis bullosa of hands and feet1318001501.0024.075.0219.03
 ENSG00000186081KRT5Epidermolysis bullosa of hands and feet1318001501.0015.0814.053.00
 ENSG00000132470ITGB4Epidermolysis bullosa of hands and feet1318001501.0047.2393.6251.35
 ENSG00000114270COL7A1Epidermolysis bullosa of hands and feet13180059.2532.1572.3964.37
 ENSG00000127870RNF6Esophageal cancer13323948.17107.6815.0830.15
 ENSG00000134982APCEsophageal cancer1332392.00136.9730.1528.13
 ENSG00000147889CDKN2AEsophageal cancer1332391501.00111.7398.5361.31
 ENSG00000008226DLEC1Esophageal cancer1332391501.0019.1111.049.05
 ENSG00000061337LZTS1Esophageal cancer1332391501.00141.9651.2657.25
 ENSG00000141510TP53Esophageal cancer133239143.8597.64133.8714.07
 ENSG00000187323DCCEsophageal cancer1332391451.0051.2844.3026.09
 ENSG00000186153NP_570606.1Esophageal cancer13323929.11111.7698.502.00
 ENSG00000140522RLBP1Fundus albipunctatus136880139.9043.27124.866.03
 ENSG00000112619RDSFundus albipunctatus1368801501.0078.56116.81141.96
 ENSG00000135437RDH5Fundus albipunctatus1368801501.0081.64101.715.06
 ENSG00000130203APOEFundus albipunctatus1368801471.001.0010.0423.11
 ENSG00000063169GLTSCR1Glioma of brain, familial13780048.161501.001511.001511.00
 ENSG00000111087GLI1_HUMANGlioma of brain, familial1378005.0117.0616.0633.15
 ENSG00000108231LGI1Glioma of brain, familial13780077.271501.001501.001501.00
 ENSG00000147889CDKN2AGlioma of brain, familial1378001501.00145.9969.3877.34
 ENSG00000146648EGFRGlioma of brain, familial1378002.004.02109.6527.08
 ENSG00000132170PPARGGlioma of brain, familial137800106.45107.8418.11110.75
 ENSG00000117298ECE1Hirschsprung disease14262387.3287.4893.53117.79
 ENSG00000136160EDNRBHirschsprung disease1426231501.0096.58121.6859.28
 ENSG00000169554ZFHX1BHirschsprung disease1426231.00125.8724.1029.19
 ENSG00000165731RETHirschsprung disease14262392.3457.2723.0982.44
 ENSG00000124205EDN3Hirschsprung disease1426231501.0042.2332.1510.08
 ENSG00000168621GDNFHirschsprung disease1426231501.00113.6285.3795.70
 ENSG00000130164LDLRHypercholesterolemia, familial14389043.1515.067.074.02
 ENSG00000055955ITIH4Hypercholesterolemia, familial14389092.3763.3883.62135.94
 ENSG00000158874APOA2Hypercholesterolemia, familial1438901501.0065.3027.1731.25
 ENSG00000169174PCSK9Hypercholesterolemia, familial14389049.2050.2893.7290.66
 ENSG00000120915EPHX2Hypercholesterolemia, familial143890114.4759.32136.9591.47
 ENSG00000084674APOBHypercholesterolemia, familial143890105.557.039.0523.18
 ENSG00000111664GNB3Hypertension, essential14550018.0470.3017.0579.33
 ENSG00000124212PTGISHypertension, essential145500132.66114.86117.7169.49
 ENSG00000087274NP_789771.1Hypertension, essential14550063.16119.77124.80147.98
 ENSG00000106258CYP3A5Hypertension, essential14550090.3462.43100.6473.39
 ENSG00000028137TNFRSF1BHypertension, essential14550030.0987.48133.8281.42
 ENSG00000144891AGTR1Hypertension, essential14550068.1911.0432.2161.33
 ENSG00000135744AGTHypertension, essential14550093.3431.1177.4231.18
 ENSG00000105227PRXHypertrophic neuropathy of Dejerine-Sottas1459001501.002.034.0219.11
 ENSG00000109099PMP22Hypertrophic neuropathy of Dejerine-Sottas1459007.0277.59112.8512.19
 ENSG00000122877EGR2Hypertrophic neuropathy of Dejerine-Sottas14590079.38112.93139.98102.78
 ENSG00000158887MPZHypertrophic neuropathy of Dejerine-Sottas1459001501.0015.1213.084.05
 ENSG00000169083ARHypospadias1464501501.00112.92118.9025.20
 ENSG00000101871MID1Hypospadias1464501031.0020.11110.8110.06
 ENSG00000049319SRD5A2Hypospadias14645056.4821.11127.9370.63
 ENSG00000005471ABCB4Cholestasis, intrahepatic, of pregnancy; ICP1474801501.0073.61106.71112.74
 ENSG00000073734ABCB11Cholestasis, intrahepatic, of pregnancy; ICP1474801501.00139.99139.9885.58
 ENSG00000081923ATP8B1Cholestasis, intrahepatic, of pregnancy; ICP14748023.0741.3929.17121.92
 ENSG00000167768KRT1Keratosis palmoplantaris striata I14870071.6666.6167.546.05
 ENSG00000134760DSG1Keratosis palmoplantaris striata I1487001451.0035.4453.566.03
 ENSG00000096696DSPKeratosis palmoplantaris striata I14870060.283.041.032.02
 ENSG00000111275ALDH2Leiomyoma, uterine15069987.5274.5325.2275.64
 ENSG00000143196DPTLeiomyoma, uterine15069970.291501.001501.0020.11
 ENSG00000182185RAD51L1Leiomyoma, uterine1506991501.00127.9729.1951.34
 ENSG00000164919COX6CLeiomyoma, uterine15069959.2360.4131.1730.22
 ENSG00000087237CETPLongevity15243093.49104.9152.2860.52
 ENSG00000108599AKAP10Longevity152430147.9929.1445.2263.45
 ENSG00000136869TLR4Longevity1524305.0234.2753.3185.68
 ENSG00000005421PON1Longevity1524301501.0026.2012.0491.68
 ENSG00000126594DNASE1Lupus erythematosus, systemic; SLE152700143.9289.6765.52121.93
 ENSG00000163599CTLA4Lupus erythematosus, systemic; SLE1527001501.0097.821501.00106.84
 ENSG00000134242PTPN22Lupus erythematosus, systemic; SLE15270067.3237.2575.5466.48
 ENSG00000143226FCGR2ALupus erythematosus, systemic; SLE15270041.1811.1030.16123.75
 ENSG00000168036CTNNB1Medulloblastoma15525597.6535.2015.1021.21
 ENSG00000134982APCMedulloblastoma1552557.02130.982.0112.06
 ENSG00000117425PTCH2Medulloblastoma1552551501.00116.8882.57105.68
 ENSG00000107882SUFUMedulloblastoma15525591.50120.8787.5263.38
 ENSG00000107815PEO1Progressive external ophthalmoplegia with mtDNA deletions; PEO15764019.161501.0079.7369.66
 ENSG00000151729SLC25A4Progressive external ophthalmoplegia with mtDNA deletions; PEO15764011.03111.9565.5296.78
 ENSG00000140521POLGProgressive external ophthalmoplegia with mtDNA deletions; PEO157640139.9814.0786.75136.99
 ENSG00000136827TOR1AMyoclonic dystonia15990059.3493.8161.39130.97
 ENSG00000127990SGCEMyoclonic dystonia15990067.5136.2557.4862.64
 ENSG00000149295DRD2Myoclonic dystonia15990054.4163.4881.513.02
 ENSG00000143632ACTA1Nemaline myopathy 1, autosomal dominant; NEM116180075.6697.7947.3725.27
 ENSG00000143549TPM3Nemaline myopathy 1, autosomal dominant; NEM116180022.1362.5347.5123.27
 ENSG00000198467TPM2Nemaline myopathy 1, autosomal dominant; NEM1161800107.773.0210.0822.24
 ENSG00000179142CYP11B2IgA nephropathy1619501501.0040.4373.6447.43
 ENSG00000135744AGTIgA nephropathy161950118.7432.2392.68132.95
 ENSG00000159640ACEIgA nephropathy1619501.00114.88102.84113.83
 ENSG00000007908SELEIgA nephropathy1619501501.0034.2632.283.02
 ENSG00000133256PDE6BNight blindness, congenital stationary; CSNB31635001501.0097.6725.1771.55
 ENSG00000114349GNAT1Night blindness, congenital stationary; CSNB3163500109.7566.3571.4291.78
 ENSG00000163914RHONight blindness, congenital stationary; CSNB31635001501.00126.95137.96131.97
 ENSG00000108576SLC6A4Obsessive-compulsive disorder 1; OCD116423028.1323.1957.391501.00
 ENSG00000102468HTR2AObsessive-compulsive disorder 1; OCD1164230141.8986.82105.8395.76
 ENSG00000176697BDNFObsessive-compulsive disorder 1; OCD11642301501.0049.4149.3389.81
 ENSG00000004948CALCROsteoporosis, involutional1667101501.006.05102.689.07
 ENSG00000136244IL6Osteoporosis, involutional16671037.15112.8680.5030.16
 ENSG00000162337LRP5Osteoporosis, involutional16671057.246.0379.4910.05
 ENSG00000108821COL1A1Osteoporosis, involutional16671029.085.026.02125.71
 ENSG00000111424VDROsteoporosis, involutional166710127.74139.9834.14114.75
 ENSG00000145335SNCAParkinson disease; PD16860067.3693.63105.761.01
 ENSG00000154277UCHL1Parkinson disease; PD16860059.3672.3761.4516.12
 ENSG00000185345PARK2Parkinson disease; PD16860085.548.0435.192.03
 ENSG00000064692SNCAIPParkinson disease; PD16860041.121501.0071.432.00
 ENSG00000081248CACNA1SHypokalemic periodic paralysis; HOKPP1704001501.0092.8071.6424.23
 ENSG00000175538KCNE3Hypokalemic periodic paralysis; HOKPP1704001501.0059.4238.4116.13
 ENSG00000007314SCN4AHypokalemic periodic paralysis; HOKPP17040016.1641.3948.58114.92
 ENSG00000165731RETPheochromocytoma171300143.9887.7730.14118.94
 ENSG00000117118SDHBPheochromocytoma17130036.2690.8688.77100.88
 ENSG00000196712NF1Pheochromocytoma171300139.9794.8343.36113.87
 ENSG00000183765CHEK2Prostate cancer17680735.0889.5356.4247.22
 ENSG00000085117CD82Prostate cancer17680724.031501.0038.47115.88
 ENSG00000067082KLF6Prostate cancer17680713.01145.9883.5861.40
 ENSG00000133216EPHB2Prostate cancer17680793.3364.3447.4181.46
 ENSG00000135828RNASELProstate cancer1768071501.00119.67103.7125.09
 ENSG00000038945MSR1Prostate cancer1768071501.0041.2264.4049.22
 ENSG00000171862PTEN_HUMANProstate cancer17680747.0825.0713.0496.50
 ENSG00000006744RNZ2_HUMANProstate cancer17680767.1712.04119.8742.21
 ENSG00000002822MAD1L1Prostate cancer176807114.50104.70107.84115.81
 ENSG00000159339PADI4Rheumatoid arthritis1803001501.0056.3883.5472.44
 ENSG00000168593NFKBIL1Rheumatoid arthritis1803001501.0089.60118.80117.86
 ENSG00000159216RUNX1Rheumatoid arthritis18030066.49108.9040.22132.96
 ENSG00000134242PTPN22Rheumatoid arthritis18030097.5764.41120.7859.39
 ENSG00000197208SLC22A4Rheumatoid arthritis18030063.3185.5889.535.02
 ENSG00000138293NCOA4Thyroid carcinoma, papillary18855027.11123.98113.84138.97
 ENSG00000108946PRKAR1AThyroid carcinoma, papillary18855023.0831.1859.34135.97
 ENSG00000198400NTRK1Thyroid carcinoma, papillary1885501501.0079.4967.46102.67
 ENSG00000114354TFGThyroid carcinoma, papillary188550116.6636.2053.4570.49
 ENSG00000047410TPRThyroid carcinoma, papillary18855010.02100.6320.1023.15
 ENSG00000129991TNNI3Cardiomyopathy, familial hypertrophic; CMH1926001501.00108.83135.8751.17
 ENSG00000160808MYL3Cardiomyopathy, familial hypertrophic; CMH19260052.071.001.012.02
 ENSG00000140416TPM1Cardiomyopathy, familial hypertrophic; CMH192600117.432.0016.071.00
 ENSG00000155657NP_59687.1Cardiomyopathy, familial hypertrophic; CMH19260093.291.0022.0898.51
 ENSG00000092054MYH7Cardiomyopathy, familial hypertrophic; CMH19260080.201.0057.3182.43
 ENSG00000111245MYL2Cardiomyopathy, familial hypertrophic; CMH192600129.522.001.0013.09
 ENSG00000118194TNNT2Cardiomyopathy, familial hypertrophic; CMH19260084.173.01137.912.00
 ENSG00000134571MYBPC3Cardiomyopathy, familial hypertrophic; CMH1926001501.001.001.0033.31
 ENSG00000101306MYLK2Cardiomyopathy, familial hypertrophic; CMH1926001501.0097.72123.7495.59
 ENSG00000183785PEX26Adrenoleukodystrophy, autosomal neonatal form202370124.541.0010.0233.15
 ENSG00000162928PEX13Adrenoleukodystrophy, autosomal neonatal form202370141.811.0083.451.00
 ENSG00000127980PEX1Adrenoleukodystrophy, autosomal neonatal form20237061.1774.4685.4525.15
 ENSG00000157911PEX10Adrenoleukodystrophy, autosomal neonatal form20237088.241.00108.629.02
 ENSG00000139197PEX5Adrenoleukodystrophy, autosomal neonatal form20237069.191.001.004.01
 ENSG00000047579DTNBP1Hermansky-Pudlak syndrome; HPS203300131.8027.2059.42145.98
 ENSG00000107521HPS1Hermansky-Pudlak syndrome; HPS203300118.5930.1537.3352.41
 ENSG00000132842AP3B1Hermansky-Pudlak syndrome; HPS20330074.301501.0088.6284.62
 ENSG00000110756HPS5Hermansky-Pudlak syndrome; HPS203300141.921501.0077.61101.76
 ENSG00000100099HPS4Hermansky-Pudlak syndrome; HPS20330050.2170.37100.7624.16
 ENSG00000163755HPS3Hermansky-Pudlak syndrome; HPS2033001501.001501.001501.001501.00
 ENSG00000092200RPGRIP1Leber congenital amaurosis, type I; LCA120400056.1821.115.0446.25
 ENSG00000132518GUCY2DLeber congenital amaurosis, type I; LCA12040001501.0069.3434.14117.70
 ENSG00000116745RPE65Leber congenital amaurosis, type I; LCA1204000103.4028.0990.46100.59
 ENSG00000139988RDH12_HUMANLeber congenital amaurosis, type I; LCA120400064.3459.3434.1670.35
 ENSG00000134376CRB1Leber congenital amaurosis, type I; LCA12040001501.0016.0652.244.00
 ENSG00000105392CRXLeber congenital amaurosis, type I; LCA120400083.24116.6734.147.02
 ENSG00000112041TULP1Leber congenital amaurosis, type I; LCA120400030.0917.0862.3568.42
 ENSG00000165731RETAutonomic control, congenital failure of20988076.26113.8931.1628.22
 ENSG00000124205EDN3Autonomic control, congenital failure of2098801501.0039.3744.2173.49
 ENSG00000109132PHOX2BAutonomic control, congenital failure of20988054.15122.868.054.02
 ENSG00000176697BDNFAutonomic control, congenital failure of2098801501.00132.94139.8889.64
 ENSG00000168621GDNFAutonomic control, congenital failure of2098801501.0053.24131.7548.26
 ENSG00000165533TTC8Bardet-Biedl syndrome; BBS2099001501.0051.2417.0613.02
 ENSG00000174483DPP3Bardet-Biedl syndrome; BBS209900147.9357.2297.5290.46
 ENSG00000138686BBS7_HUMANBardet-Biedl syndrome; BBS20990049.1413.0565.3388.50
 ENSG00000125124BBS2Bardet-Biedl syndrome; BBS2099001501.006.058.0517.05
 ENSG00000125863MKKSBardet-Biedl syndrome; BBS209900104.37126.78103.5354.27
 ENSG00000140463BBS4Bardet-Biedl syndrome; BBS20990071.2449.2123.089.03
 ENSG00000113966ARL6Bardet-Biedl syndrome; BBS2099001501.0042.2248.30106.61
 ENSG00000163093BBS5Bardet-Biedl syndrome; BBS20990044.081501.0031.1329.09
 ENSG00000133111RFXAPBare lymphocyte syndrome, type II20992033.23123.94110.762.00
 ENSG00000179583MHC2TABare lymphocyte syndrome, type II2099201501.0051.4110.041.00
 ENSG00000064490RFXANKBare lymphocyte syndrome, type II20992079.50122.9096.491.00
 ENSG00000143390RFX5Bare lymphocyte syndrome, type II209920120.81116.9415.071.00
 ENSG00000159128IFNGR2Atypical mycobacteriosis, familial20995010.0115.0711.0612.06
 ENSG00000096996IL12RB1Atypical mycobacteriosis, familial2099501501.0033.1545.2722.09
 ENSG00000027697IFNGR1Atypical mycobacteriosis, familial20995085.3113.0523.1313.07
 ENSG00000113302IL12BAtypical mycobacteriosis, familial2099501501.0010.0966.474.03
 ENSG00000115415STAT1Atypical mycobacteriosis, familial209950114.43109.8450.28101.63
 ENSG00000073734ABCB11Cholestasis, progressive familial intrahepatic 1; PFIC12116001501.001411.00144.9978.53
 ENSG00000005471ABCB4Cholestasis, progressive familial intrahepatic 1; PFIC12116001501.00102.80120.82133.91
 ENSG00000081923ATP8B1Cholestasis, progressive familial intrahepatic 1; PFIC121160023.0831.2838.24123.92
 ENSG00000099377HSD3B7Cholestasis, progressive familial intrahepatic 1; PFIC121160094.358.118.054.03
 ENSG00000133703RASK_HUMANLung cancer21198094.62103.7722.1061.30
 ENSG00000157764BRAFLung cancer21198040.1944.289.0214.06
 ENSG00000141510TP53Lung cancer21198012.03113.8240.381501.00
 ENSG00000146648EGFRLung cancer211980113.7685.6040.2733.24
 ENSG00000139197PEX5Zellweger syndrome; ZS21410026.0218.046.011.00
 ENSG00000142655PEX14Zellweger syndrome; ZS21410060.1115.054.012.00
 ENSG00000127980PEX1Zellweger syndrome; ZS21410049.098.037.0112.04
 ENSG00000124587PEX6Zellweger syndrome; ZS21410056.0914.049.0232.15
 ENSG00000108733PEX12Zellweger syndrome; ZS214100118.3979.58131.691.00
 ENSG00000164751PXMP3Zellweger syndrome; ZS214100117.413.01133.711.00
 ENSG00000183785PEX26Zellweger syndrome; ZS21410076.2317.0435.1414.05
 ENSG00000034693PEX3Zellweger syndrome; ZS2141003.001.0074.304.00
 ENSG00000172660TAF15Chondrosarcoma21530083.40114.96139.9118.15
 ENSG00000182197EXT1Chondrosarcoma21530077.2840.2831.234.07
 ENSG00000151348EXT2Chondrosarcoma21530019.0624.3027.1919.17
 ENSG00000182944EWSR1Chondrosarcoma21530028.1084.5936.2638.33
 ENSG00000134183GNAT2Achromatopsia 2; ACHM221690040.29121.97142.991431.00
 ENSG00000144191CNGA3Achromatopsia 2; ACHM2216900143.9759.4415.08120.92
 ENSG00000170289CNGB3Achromatopsia 2; ACHM22169001501.0091.8749.4770.77
 ENSG00000065618COHA1_HUMANEpidermolysis bullosa junctionalis, disentis type22665019.0894.6174.47102.59
 ENSG00000132470ITGB4Epidermolysis bullosa junctionalis, disentis type2266501501.0096.7347.35140.98
 ENSG00000196878LAMB3Epidermolysis bullosa junctionalis, disentis type2266501501.0023.1412.0998.66
 ENSG00000053747LAMA3Epidermolysis bullosa junctionalis, disentis type226650143.9993.68108.7455.33
 ENSG00000165281FANCGFanconi anemia; FA22765034.07118.8215.082.00
 ENSG00000144554FANCD2Fanconi anemia; FA2276501501.0053.2452.262.00
 ENSG00000183161FANCFFanconi anemia; FA22765043.1245.2647.262.00
 ENSG00000158169FANCCFanconi anemia; FA22765026.1749.2853.341.00
 ENSG00000115392FANCLFanconi anemia; FA22765056.21108.7430.174.01
 ENSG00000112039FANCEFanconi anemia; FA2276506.0140.2532.181.00
 ENSG00000105379ETFBGlutaricaciduria IIA2316807.0311.071.001.00
 ENSG00000140374ETFAGlutaricaciduria IIA2316809.033.091.011.00
 ENSG00000171503ETFDHGlutaricaciduria IIA2316801.0038.481.001.00
 ENSG00000188690UROSHydrops fetalis, idiopathic23675067.30104.916.0476.52
 ENSG00000169919GUSBHydrops fetalis, idiopathic236750143.96120.9598.55112.84
 ENSG00000177628GBAHydrops fetalis, idiopathic236750108.69118.95113.79115.85
 ENSG00000188536HBA1Hydrops fetalis, idiopathic23675042.16133.99112.6976.53
 ENSG00000179477ALOX12BIchthyosi