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Genome Res. May 2009; 19(5): 711–722.
PMCID: PMC3647533

Constructing genomic maps of positive selection in humans: Where do we go from here?


Identifying targets of positive selection in humans has, until recently, been frustratingly slow, relying on the analysis of individual candidate genes. Genomics, however, has provided the necessary resources to systematically interrogate the entire genome for signatures of natural selection. To date, 21 genome-wide scans for recent or ongoing positive selection have been performed in humans. A key challenge is to begin synthesizing these newly constructed maps of positive selection into a coherent narrative of human evolutionary history and derive a deeper mechanistic understanding of how natural populations evolve. Here, I chronicle the recent history of the burgeoning field of human population genomics, critically assess genome-wide scans for positive selection in humans, identify important gaps in knowledge, and discuss both short- and long-term strategies for traversing the path from the low-resolution, incomplete, and error-prone maps of selection today to the ultimate goal of a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles.

In August 1858, Charles Robert Darwin and Alfred Russel Wallace communicated essays to the Linnean Society of London (Darwin and Wallace 1858) describing their independent discovery of the theory of natural selection and, in doing so, fundamentally altered our understanding of life on Earth. Darwin's meticulously detailed book, On the Origin of Species by Means of Natural Selection, or the Preservation of Favored Races in the Struggle for Life, published 1 yr later (Darwin 1859), provided a more comprehensive account of his evidence for natural selection and its role in mediating evolutionary change. The immediate and enduring interest in Darwin's and Wallace's work is not only because of the powerful, unifying, and explanatory theory it provides for biology but also because it is a theory about us as humans and our place in the natural world.

Over the ensuing century and a half, considerable progress has been made in elucidating the molecular and mechanistic details of how natural populations evolve. Integral to this progress was the development and maturation of population genetics, the foundations of which can largely be ascribed to the seminal contributions of Wright, Fisher, and Haldane (Provine 1971), who have been referred to as the “great trinity” of population genetics (Crow 1994). Furthermore, we now recognize the contribution of both chance and natural selection as vehicles of evolutionary change (Kimura 1968; King and Jukes 1969). The very concept of natural selection itself has evolved, with different types of selection being distinguished. Essentially, the various modes of selection follow from whether an allele is advantageous or deleterious and the fitness relationships among genotypes (Box 1). Darwin and Wallace were primarily interested in adaptive evolution, which at the molecular level is governed by selection acting on advantageous alleles. The nomenclature of selection can be daunting for the uninitiated (Boxes 1, ,2),2), and therefore for simplicity I will refer to any form of selection acting on advantageous alleles as positive selection (Nielsen 2005).

Box 1.
Types of natural selection
Box 2.
Nomenclature of selective sweeps

Despite the significant advances made to date, intense research has thus far failed to reveal all of evolution's secrets. In particular, progress in humans has been frustratingly difficult to achieve. However, the tide seems to be turning, and the confluence of dense catalogs of human genetic variation and methodological tools have led to the construction of many genomic maps of positive selection. These maps simultaneously hold great promise and pose important challenges for arriving at a detailed understanding of how positive selection has shaped our genomes and our history.

Here, I will provide an overview of how studies of human evolutionary history have profited from the genomics era and critically assess recent attempts to construct genomic maps of positive selection in humans. My goals are twofold. First, rather than focus on the details of any particular study, which have been extensively reviewed elsewhere (Ronald and Akey 2005; Biswas and Akey 2006; Harris and Meyer 2006; Sabeti et al. 2006; Nielsen et al. 2007), I will discuss several broad themes of recent human evolutionary history emerging from the synthesis of results across studies. Second, I will enumerate both short- and long-term strategies for translating maps of positive selection into a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles.

Making the case for population genomics

Before the genomics era, inferences regarding natural selection were made almost exclusively through candidate gene studies (Sabeti et al. 2006). These single gene analyses have yielded some notable success stories illuminating deep insights into recent human evolutionary history, including compelling evidence for positive selection of LCT (Bersaglieri et al. 2004), which allows lactose tolerance to persist throughout adulthood, as well as a number of genes that reduce susceptibility to malaria infection, such as G6PD (Tishkoff et al. 2001), DARC (Hamblin and Di Rienzo 2000; Hamblin et al. 2002), and HBB (Friedman 1978; Currat at el. 2002).

In general, however, candidate gene studies suffer from two key limitations. First, they require an a priori hypothesis about which genes may have been subject to selection, and in order to form this hypothesis, it is necessary to have an understanding about genotype–phenotype relationships. Although this has been feasible for some phenotypes that have a well-defined, almost Mendelian architecture (such as lactose tolerance), the genetic architecture of phenotypic variation remains enigmatic for most traits, constraining our ability to intelligently nominate candidate genes to study. Another avenue for identifying candidate genes has been to focus on loci belonging to classes of genes that appear to be frequent targets of selection, such as those involved in immunity and defense (Bamshad and Wooding 2003). While this approach is intuitively appealing, it potentially leads to a biased set of loci that have been subject to selection. Furthermore, candidate gene studies are an inefficient study design for detecting positive selection in regulatory regions far removed from genic loci.

The second key limitation of candidate gene approaches is due to the confounding effects of genetic drift, manifested through population demographic history, and natural selection on extant patterns of genetic variation (Simonsen et al. 1995; Przeworski et al. 2000; Akey et al. 2004; Stajich and Hahn 2005). Thus, interpreting patterns of genetic variation at individual loci is often difficult, making robust inferences of positive selection challenging.

Genomics has offered a new paradigm for detecting signatures of selection, which has been referred to as population genomics. The term population genomics appears to have been introduced into the lexicon of genomics jargon independently by Hedges (2000) and Black IV et al. (2001). In its most general form, population genomics refers to the inference of population genetic and evolutionary parameters from genome-wide data sets (Black IV et al. 2001).

In the context of identifying substrates of positive selection, population genomics offers a potential solution to the two key limitations of candidate gene studies. First, the genome can be surveyed without any a priori assumptions regarding which genes may be under selection, yielding a less biased set of putatively selected loci. Second, population genomics provides a framework for distinguishing, at least in principle, between population demographic history and natural selection. Specifically, the most commonly used population genomics approach involves sampling a large number of loci throughout the genome, calculating a summary statistic that quantifies some aspect of genetic variation, constructing an empirical distribution of this statistic across all loci, and defining putative targets of selection based on “outliers” in the extreme tail of the empirical distribution (Fig. 1). The underlying rationale of this approach is predicated on several implicit assumptions (Fig. 1), the most important of which is that population demographic history is a genome-wide force affecting all loci equally, whereas selection is a locus-specific force acting on a subset of loci (Black IV et al. 2001). If all goes well, selection pulls individual loci into the tails of the empirical distribution, which can be identified as outlier loci. The criteria for defining outliers is often arbitrary, for example, loci falling in 99th percentile of the empirical distribution, although simulations of neutral evolution have also been used to either guide the selection of or evaluate the efficiency of outlier thresholds. Increasingly realistic models of human demographic history, recombination, gene conversion, and mutation rate heterogeneity (Schaffner et al. 2005) will ultimately allow more robust definitions of what constitutes an outlier locus.

Figure 1.
A typical population genomics study design for detecting positive selection. Population genomic studies begin by sampling loci, typically SNPs, throughout the genome. The majority of loci are presumably influenced only by genome-wide forces such as genetic ...

Enabling resources for human population genomics

The recent advent of population genomics is not due to a great intellectual leap forward. Indeed, the conceptual foundation of the population genomics paradigm was outlined nearly four decades ago (Cavalli-Sforza 1966; Lewontin and Krakauer 1978). Rather, progress in sequencing the human genome (Lander et al. 2001; Venter et al. 2001) and single nucleotide polymorphism (SNP) genotyping technology (and to a lesser extent microsatellite genotyping) allowed dense catalogs of genomic variation to be developed, thus enabling population genomics approaches to become a reality. One of the earliest human population genomics resources was the systematic discovery of over 1.42 million SNPs (Sachidanandam et al. 2001), ~26,500 of which were genotyped by the SNP Consortium in three populations (Thorisson and Stein 2003). This early resource was quickly superseded by the International HapMap Project (International HapMap Consortium 2005) and Perlegen Biosciences data sets (Hinds et al. 2005), which genotyped ~3 million SNPs in 210 unrelated individuals from four populations and 1.6 million SNPs in three populations, respectively. Although the HapMap and Perlegen data sets have served as the primary starting points for much of human population genomics, the continued infusion of additional SNP (Jakobsson et al. 2008; Li et al. 2008), structural variation (Redon et al. 2006; Kidd et al. 2008), and large-scale resequencing data (Livingston et al. 2004) provides a rich repository of raw material to test evolutionary hypotheses. An important characteristic of these data sets that has fueled the rapid growth of human population genomics is their public availability, which has stimulated both methodological development and applications beyond their original purposes in ways that would not have been possible except for open and unfettered access.

Genome-wide scans of selection in humans: Promises and pitfalls

The first genome-wide scans of selection in humans were performed less than a decade ago (Akey et al. 2002; Payseur et al. 2002), albeit with considerably less dense maps of genetic variation compared with what is available today. Since these initial studies, genome-wide scans have been described at a frenetic pace. Specifically, as shown in Table 1, 21 genome-wide scans for recent or ongoing selection (Box 2) have been performed in humans. The majority of these recent analyses have used either the HapMap or Perlegen data, although several studies have been performed in distinct samples (Table 1). Furthermore, these 21 scans for selection have used a variety of statistical approaches to detect deviations from neutrality, which will become clear below, is an important factor in interpreting results across studies. A general synopsis of methods used to detect deviations from neutrality is provided in Box 3; detailed reviews can be found elsewhere (Kreitman 2000; Nielsen 2001; Ronald and Akey 2005; Biswas and Akey 2006; Nielsen et al. 2007).

Table 1.
Summary of genome-wide scans of positive selection in humans
Box 3.
Statistical tests of neutrality

The rapid accumulation of genomic maps of positive selection is an important milestone, increasing the number of loci putatively under selection by several orders of magnitude compared to candidate gene approaches. These maps hold considerable promise in guiding us toward a more detailed understanding of where, and why, positive selection has shaped extant patterns of human genetic variation, but only if they are guiding us toward genuine substrates of selection. It is important to note that the majority of the studies in Table 1 have defined targets of positive selection in the canonical population genomics fashion, namely, the identification of outlier loci. However, being an outlier is not necessarily synonymous with being under selection (Fig. 1).

One way to assess confidence in the results of genome-wide scans is to examine the overlap of outlier loci across studies. To this end, for eight recent studies performed on the HapMap and Perlegen data indicated in Table 1 (which describes inclusion criteria), I obtained genomic positions for all reported autosomal loci under selection, mapped their positions to the same genomic build (UCSC hg18), and merged overlapping loci into a set of nonredundant positions. In addition, I also included results based on a simple genome-wide scan in the HapMap samples using FST, which is a commonly used test statistic to detect local adaptation (Akey et al. 2002), the type of selection acting upon LCT (Bersaglieri et al. 2004; Tishkoff et al. 2007).

The integrated map of positive selection across these nine genome-wide scans is shown in Figure 2. In total, 5110 distinct regions were identified in one or more study. These regions encompass ~409 Mb of sequence (~14% of the genome) and contain 4243 UCSC RefSeq genes (~23% of all genes). Strikingly, only 722 regions (14.1%) were identified in two or more studies, 271 regions (5.3%) were identified in three or more studies, and 129 regions (2.5%) were identified in four or more studies (Fig. 1). Furthermore, the integrated map of positive selection does not include several of the most compelling genes with well-substantiated claims of positive selection, such as G6PD and DARC. G6PD is located on the X chromosome, which generally has not been included in genome-wide analyses. DARC, however, is an autosomal gene, but the HapMap and Perlegen samples are not representative of populations in which the signature of selection has been reported by Hamblin and Di Rienzo (2000) and Hamblin et al. (2002). However, even if additional populations were included, it is unclear if DARC would emerge as an outlier locus given that its signature of selection is confined to a very small genomic region (~10 kb).

Figure 2.
Integrated genomic map of positive selection. Vertical red lines on each autosome indicate loci that were identified in a single genome-wide scan, and blue lines denote regions identified in two or more studies. The histogram shows the proportion of putatively ...

Although the poor concordance among studies is sobering, further inspection suggests some room for optimism. In particular, as noted above, the integrated map of positive selection contains 5110 regions spanning 409 Mb of total sequence. The amount of sequence contained in the 722, 271, and 129 regions identified in two, three, and four or more studies is 245, 148, and 92 Mb, respectively. Thus, ~60%, 36%, and 22% of the total sequence encompassing the integrated map is supported by two, three, and four or more studies, respectively. This paradoxical observation, little overlap in the number of regions but considerably more so of sequence, is due to the marked difference in the average size of regions identified in single versus multiple studies (~80 kb and 300 kb, respectively). I suggest that these results are consistent with at least two mutually compatible explanations. First, loci deemed as an outlier in multiple analyses are more likely to represent the most dramatic selective events, which in general will tend to leave larger footprints in the genome because they are some combination of young, strong, or in regions of low recombination. Second, although multiple studies may demark similar loci, they home in on different positions within the larger genomic interval formed after merging overlapping signatures of selection. In this respect, there seems to be considerable promise in using the chromosomal distribution of various neutrality test statistics to more precisely map targets of positive selection.

Despite the above considerations, there is no escaping the general conclusion that the overlap among studies is underwhelming. However, this is unsurprising for a number of reasons. For example, a number of neutrality test statistics have been used to scan the genome for signatures of positive selection (Box 3), which are likely recovering selective events from different time periods and for different stages of the selective sweep (Box 2) (see also Biswas and Akey 2006; Sabeti et al. 2006). Furthermore, studies tend to only report the most extreme loci. This conceivably has the effect of reducing overlap among studies if a locus is deemed an outlier in one analysis because it falls in the 99th percentile of the empirical distribution, but is not called an outlier in another study where it falls in the 98th percentile of a different empirical distribution. Finally, and perhaps most importantly, several simulation studies have shown that outlier approaches likely suffer from low power and high false positive rates (Kelley et al. 2006; Teshima et al. 2006). Unfortunately, the actual power and false positive rates depend on a large number of parameters that are difficult to estimate, such as the fraction of the genome under selection; strength of selection; whether adaptive alleles are recessive, dominant, or additive; and whether selection acts on newly arisen versus preexisting variation. Additional theoretical studies of outlier approaches, systematically comparing a broad set of commonly used neutrality test statistics under a range of demographic and selective models, would be invaluable for guiding efficient study designs in genome-wide scans of selection.

In summary, genome-wide analyses of positive selection described to date suggest widespread signatures of positive selection in the human genome. Although these newly constructed maps of selection likely include genuine substrates of positive selection, they also likely possess many false positives and false negatives, and thus considerable caution is needed when interpreting such maps.

What have we learned from recent genome-wide scans of selection?

Although the interpretation of genome-wide scans of selection is hampered by the low-resolution, incomplete, and error-prone maps described above, careful inspection of the results across studies allows several general themes to begin to come into focus. Here, I discuss general insights derived by analyzing the higher confidence selected loci that are supported by two or more studies (Supplemental Table 1).

Familiar friends, new faces

The 722 regions that have been identified in multiple genome-wide scans contain 2465 genes, a number of which have been previously implicated as targets of positive selection. Examples include LCT (Bersaglieri et al. 2004), TRPV6 (Akey et al. 2004, 2006), CYP3A (Thompson et al. 2004), CYP1A2 (Wooding et al. 2002), IL13 (Zhou et al. 2004), and IL4 (Rockman et al. 2003) among others. Perhaps more interestingly, many new well-supported genes emerge that would not necessarily be strong a priori candidate genes of selection to study. For instance, in a rare example of multiple analyses converging on a single gene, PCDH15 was identified in six out of the nine genome-wide scans. Mutations in PCDH15, which plays a critical role in retinal and cochlear function, can result in Usher syndrome type IF and Autosomal Recessive Deafness 23 (Ahmed et al. 2003). Interestingly, three myosin genes (MYO1B, MYO3A, and MYO6) that are integral in cochlear function (Dumont et al. 2002; Walsh et al. 2002; Sanggaard et al. 2008) are also among the set of loci supported by multiple analyses.

In addition to analyses of individual loci, several interesting observations emerge from examining the general functional classes of these 2465 genes. Table 2 shows PANTHER Biological Process terms (Thomas et al. 2003) that are overrepresented among the set of genes identified in multiple genome-wide analyses. One of the more striking observations in Table 2 is the dominant role that positive selection on metabolic processes seems to have played in recent human evolutionary history. For example, a significant overrepresentation is observed for terms such as protein modification, protein metabolism, carbohydrate metabolism, and phosphate metabolism. Although this observation is in accord with known dramatic shifts in diet during recent human history (Larsen 1995), the pervasive signature of positive selection across so many metabolic processes has not generally been appreciated. The other interesting point gleaned from a cursory examination of Table 2 is that far from acting on a few classes of genes, positive selection appears to have affected a wide variety of biological processes.

Table 2.
Enriched PANTHER biological process terms

Spatially varying selection

A recurring observation of genome-wide studies is that signatures of positive selection are not uniformly distributed across populations, but rather show clear spatial heterogeneity (i.e., local adaptation). In particular, ~80% of the 722 loci observed in multiple scans show evidence of local adaptation. This is consistent with the large number of previous single gene studies describing spatially varying patterns of selection (for review, see Ronald and Akey 2005). The observation of widespread local adaptation is not surprising given the environmental heterogeneity that human populations are confronted with throughout the world.

Although the evidence that local adaptation has played a prominent role in recent human evolutionary history is compelling, some caution is required when comparing signatures of selection across populations. In particular, genome-wide scans are essentially searching for positions in the genome that have a large scaled selection coefficient, 4Nes, where Ne is the effective population size and s is the magnitude of selection. As Ne is influenced by population demographic history, the signature of selection is jointly determined by both the strength of selection and demographic history, as well as local rates of recombination and mutation (Kaplan et al. 1989). Thus, population differences in any of these parameters can influence whether a locus appears to be under selection in a single population or multiple populations.

A specific example of the difficulties in interpreting signatures of spatially varying selection is the observation that non-African populations tend to show more evidence for recent positive selection relative to African populations (Akey et al. 2004; Storz et al. 2004; Williamson et al. 2007; but see Voight et al. 2006). While this may be due to increased selection as humans migrated out of Africa and were confronted with new environmental pressures (such as novel climates, diets, and pathogens), differences in demographic history or rates of recombination and mutation between African and non-African populations may obscure the relationship between signatures of selection across populations. Until a wider set of African populations are studied, inferences about the relative frequency of positive selection between African and non-African populations and patterns of shared selective events will remain speculative.

Regulatory versus protein adaptive evolution

An ongoing debate, now over three decades old and still going strong (King and Wilson 1975; Hoekstra and Coyne 2007; Wray 2007), is the relative contribution of changes in gene regulation versus protein structure as mechanisms of evolutionary change. The poor resolution of current genomic maps of selection precludes definitive inferences about the proportion of recent positive selection in humans due to regulatory versus coding evolution. However, we can gain some insight by considering the genic content of the 722 regions supported by multiple studies. In total, 101 of the 722 regions (~14%) contain no UCSC RefSeq protein coding genes and are therefore attractive candidates for harboring adaptive regulatory variation. Obviously, selected regions that overlap protein coding genes may also be the result of regulatory changes, so 14% provides a rough lower bound on the number of well-supported regions in the integrated map of positive selection that are driven by regulatory evolution.

An interesting example of a nonprotein-containing region is a 150-kb interval on chromosome 20 downstream from BMP2, which is a member of the transforming growth factor beta superfamily involved in bone and cartilage formation (Wang et al. 1990). In mice, an enhancer in the 3′ region of BMP2 has been identified that influences expression in osteoblast progenitor cells (Chandler et al. 2007), and in humans, SNPs in the 3′ region are associated with otosclerosis (Schrauwen et al. 2008), a progressive disease of the temporal bone that can lead to hearing loss. Additional bioinformatics analyses of the BMP2 downstream region, and the remaining 100 nongenic regions, may provide valuable insights into distinguishing characteristics of these loci, guide experimental studies, and generate testable evolutionary hypotheses.

Genetic draft and “off-target” effects

Genomic maps of selection suggest widespread genetic hitchhiking (Box 2) throughout the genome. Although the veracity of this statement is subject to the limitations described above, it is fair to say that the number of strong selective events thought to exist in the human genome today is considerably more than that imagined less than a decade ago. Again, restricting our attention to the 722 loci identified in two or more genome-wide scans, ~245 Mb (~8%) of the genome has been influenced by positive selection, and an even larger fraction may have been subject to more modest selective pressure.

If a substantial fraction of the genome has indeed been influenced by positive selection, this would have important theoretical and practical implications. For example, it may be necessary to consider models, such as genetic draft (Gillespie 2000), where the stochastic population genetic dynamics of neutral variation is governed more by the indirect effects of selection on adaptive alleles than by genetic drift (the primary force governing levels of variation within and between populations in neutral and nearly neutral models) (Kimura 1983; Ohta and Gillespie 1996). In this vein, it will also be important to incorporate background selection (the effect of purifying selection on linked patterns of variation) (Charlesworth et al. 1993; Reed et al. 2005) into drift and draft models to better understand the causes and consequences of genomic patterns of human genetic variation.

From a more practical perspective, it will be of considerable interest to determine how often genetic hitchhiking has influenced the population genetic characteristics of neutral or nearly neutral alleles that contribute to phenotypic variation. For instance, ~10% of the significant results from recent genome-wide association studies (GWAS) summarized in the NHGRI GWAS catalog (Hindorff et al. 2009) are located in the higher confidence selected regions. As this is not significantly more than expected by chance (P-value >0.05), it seems unlikely that all significant GWAS results have been direct targets of selection, implying that the frequency and distribution of at least some alleles contributing to human phenotypic variation and disease susceptibility may have been indirectly influenced by positive selection.

Outliers are simple, their evolutionary history may not be

The typical outcome of a genome-wide scan for positive selection is a list of loci that have been categorized as either “an outlier” or “not an outlier.” This simple binary classification belies the complex evolutionary history that outlier loci may have experienced. Previous candidate gene analyses provide a glimpse into the types of complexity that may be found when the signature of selection at individual outlier loci is studied in more detail. Examples include convergent evolution (Lamason et al. 2005; Tishkoff et al. 2007; Enattah et al. 2008), selection on standing variation (Hamblin et al. 2002; Enattah et al. 2008; Magalon et al. 2008), evidence for both directional and balancing selection acting on the same locus (Tishkoff et al. 2001; Verrelli et al. 2002), and epistatic selection (Williams et al. 2005).

Hints of complexity are already apparent in the results from genome-wide scans. For instance, ~8% of the regions supported by multiple studies show evidence of selection in both European and African samples, suggesting these loci may have experienced independent selective pressures. Furthermore, we have recently performed a detailed population genetics analysis of ALMS1, which occurs in a region identified in seven out of the nine genome-wide scans, and found compelling evidence for geographically restricted selection, selection from standing variation, and three ancient and divergent haplogroup lineages (Scheinfeldt et al. 2009). Thus, when subjected to further scrutiny, outlier loci will not be uniformly explained by classic models of a newly arisen advantageous mutation sweeping to fixation (Maynard-Smith and Haigh 1974). Rather, they will likely be the result of a range of evolutionary models, a deeper understanding of which will provide basic insights into the mechanistic details of how natural populations adapt.

Back to the future: A return to candidate gene studies?

Earlier, I espoused the virtues of population genomics over candidate gene approaches. However, after the dust settles from genome-wide scans of selection, we are left with many regions that possess unusual patterns of variation consistent with the hypothesis of selection and perhaps a rough estimate of when and where selection occurred. Genome-wide scans are a powerful beginning but are clearly not the end toward developing a detailed, precise, and mechanistic understanding of human evolutionary history. In other words, genome-wide scans are a hatchet, whereas what we need now is a scalpel. In-depth follow-up studies of individual outlier loci can be one such scalpel, more precisely defining important population genetic parameters such as the timing and magnitude of selection, the geographic distribution of selected variation, the interaction of population demographic history, recombination, and selection in shaping patterns of variation, and the functional form of selection acting on individual outlier loci.

However, follow-up studies of outlier loci offer several new methodological challenges. For instance, it is necessary to carefully consider how hypothesis testing is performed, as the study of outlier loci introduces an ascertainment bias that needs to be properly taken into account (Kreitman and Di Rienzo 2004; Thornton and Jensen 2007). As a concrete example, consider a hypothetical locus that is an outlier in the empirical distribution of FST derived from the HapMap data. In a follow-up resequencing study in the same samples, additional neutrality test statistics, such as Tajima's D, are calculated. Because FST and Tajima's D at individual loci are not independent, it would be misleading to evaluate the statistical significance of the latter without taking into account the initial ascertainment on strong population structure. Approaches have already been developed to address issues of ascertainment bias encountered in follow-up studies of selected loci (Thornton and Jensen 2007), and additional work in this area would provide further insights into how best to design, analyze, and interpret follow-up studies of outlier loci.

In short, although delving into the minutiae of individual outlier loci is perhaps less glamorous than the initial genome-wide analysis, it is a necessary step toward developing a coherent principled narrative of recent human evolutionary history. The analysis of individual outlier loci differs, however, in important conceptual and methodological ways from earlier candidate gene approaches and therefore should not be viewed as simply a return to candidate gene studies.

The missing (phenotype) link

A significant impediment to understanding and interpreting signatures of positive selection, and ultimately identifying adaptive alleles, is that we are often ignorant about the phenotype the selected locus influences. This outcome is a direct consequence of the “bottom-up” strategy used in genome-wide scans for selection, where inferences are made directly from patterns of genetic variation (Fig. 3). However, the direct substrate of selection is phenotypic variation that influences fitness. Thus, the relationship between positive selection and patterns of genetic variation depends on the underlying genetic architecture of phenotypes, which is increasingly being cast in a systems biology framework (Benfey and Mitchell-Olds 2008; Ellegren and Sheldon 2008). A better understanding of how genetic variation influences variation in molecular networks, which interact with each other and the environment to shape patterns of phenotypic variation, would significantly accelerate the interpretation of signatures of positive selection (Fig. 3; Ellegren and Sheldon 2008).

Figure 3.
Bottom-up population genomics. Genome-wide scans of positive selection are agnostic to phenotypic data and make inferences of selection directly from patterns of genetic variation (dashed black arrow). However, selection acts directly on phenotypic variation ...

An excellent recent example of how phenotypic context can facilitate a deeper understanding of selection is SLC24A5 (Lamason et al. 2005). In a mutant screen, the investigators showed the zebrafish homolog of SLC24A5 affects pigmentation. Next, they examined patterns of genetic variation for this gene in the HapMap data and found a dramatic signature of positive selection (Lamason et al. 2005). Guided by the zebrafish data and evidence for positive selection in humans, they went on to demonstrate that a nonsynonymous SNP in SLC24A5 influenced differences in pigmentation levels between individuals of West African and European ancestry. It is worth noting that SLC24A5 is among the most well-supported loci in the integrated map of positive selection described above (Fig. 2), having been identified in six out of the nine genome-wide scans. Without prior knowledge that SLC24A5 was a pigmentation gene, there would have been little impetus for performing the subsequent association analyses demonstrating its phenotypic effect in humans, leaving this locus as an anonymous outlier without all of the deep biological and evolutionary insights that now exist. Although the details differ, a common theme underlying almost every other well-understood instance of positive selection in humans (i.e., LCT, G6PD, HBB, DARC, etc.) is that something was known about the phenotype these genes influenced.

Aside from facilitating interpretations of positively selected loci, a deeper understanding of phenotypic variation in a systems biology framework will also expand the scope of evolutionary inferences that are possible. Specifically, genome-wide scans for recent positive selection only have reasonable power to detect fairly strong selective effects (4Nes ~400) (Kelley et al. 2006; Teshima et al. 2006). Thus, the current catalog of positively selected loci identified through genome-wide scans represents only the tip of the selective iceberg. At this time, there seems little reason for optimism in detecting weak positive selection acting at a single locus. However, it may be possible to increase the power to detect more subtle selection by combining information across loci, allowing inferences about the influence of adaptive evolution on specific pathways or modules (Hancock et al. 2008).

Is Darwinian evolution enough?

The basic tenets that contemporary evolutionary studies, including genome-wide scans of selection, operate under are encapsulated in what is referred to as the modern synthesis. Excellent and detailed reviews on the origins and scope of the modern synthesis, can be found elsewhere (Provine 1971), but can succinctly be described as the coalescence of Darwinian selection, theoretical population genetics, and Mendelian principles into a unified account of how populations and species evolve. As new insights into basic biological mechanisms and data accumulated over the decades, particularly in the genomics era, a slow but steady call has been made to extend the modern synthesis (e.g., Kutschera and Niklas 2004; Pigliucci 2007, and references therein). General areas that are fueling the call for an expanded evolutionary synthesis include the evolution of evolvability, epigenetic inheritance, phenotypic plasticity, and the origins of complexity (for review, see Pigliucci 2007). Although these ideas, and more specifically their interpretation, are controversial (Pennisi 2008) and ultimately may not necessitate a major extension to the modern synthesis, they should not be categorically dismissed. Rather, they should be subjected to increased scrutiny, investigating on a case-by-case basis their contribution to evolutionary processes and relationships with currently held evolutionary paradigms.

To date, genome-wide scans have provided little insight into issues that potentially extend beyond the framework of the modern synthesis. Whether this is because they are not well suited for addressing such issues or because they have not been used to ask relevant questions remains to be determined. For instance, the recent observation that SNPs disrupting CpG sites can have unexpectedly large influences on the methylation status of a region (Kerkel et al. 2008) provides a link between heritable genetic variation and epigenetic variation. Perhaps some of these methylation altering SNPs are adaptive and thus could potentially be identified if explicitly looked for in genome-wide scans for positive selection. Although this is admittedly a nebulous example, it highlights the point that detailed analyses of outlier loci, guided by specific hypotheses, may be a powerful avenue for elucidating fundamental mechanisms of evolutionary change.

Future directions

Genomics has unquestionably and profoundly changed the field of human evolutionary genetics. Genome-wide scans have provided coarse maps of positive selection in humans, maps that may ultimately yield a deeper understanding into mechanisms of adaptive change. However, a key now is to begin traversing the path from the low-resolution, incomplete, and error-prone maps of selection today to the ultimate goal of a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles. How do we get from here to there?

Looking ahead, we can expect a continuing deluge of data, perhaps the most exciting of which are whole-genome sequences from thousands of geographically diverse individuals (Wise 2008). Although whole-genome sequences from thousands of individuals will approach the limits of complete genetic information, how much closer will it get us to our ultimate goals? Many of the already performed genome-wide analyses (Table 1) will and should be repeated on these data sets, because of their greater information content and lack of ascertainment bias, which has hampered evolutionary analyses of SNP data (Akey et al. 2003; Clark et al. 2005). These analyses will allow in-depth population genetic studies of already and soon to be identified outlier loci and a better understanding of how putatively selected variation is apportioned within and among populations.

Although important, these lines of investigation, in and of themselves, will not suffice for at least two reasons. First, there is considerable need for further theoretical and methodological research. Specific areas of fruitful inquiry include a more comprehensive statistical characterization of neutrality tests to a wider range of selective and demographic models, developing more realistic models of how selection operates in natural populations and methods to detect it (Orr and Betancourt 2001; Wakeley 2004; Hermisson and Pennings 2005; Przeworski et al. 2005), and models and methods of analysis for understanding adaptive evolution in the context of systems biology.

Second, and more fundamentally, the statistical analysis of DNA sequence variation, or any single approach, cannot provide a complete description of human evolutionary history. Indeed, as evolution is an inherently stochastic process and the result of a series of historical contingencies (Lewontin 1966; Jacob 1977), there are likely questions that may never be satisfactorily answered. However, an account of what is possible to know will be incomplete until the functional consequences of genetic variation can be determined in a high-throughput and comprehensive manner; until a deeper appreciation of how genetic variation perturbs regulatory and protein networks is attained; until the genetic and environmental architecture of phenotypic variation is elucidated; and until cultural and ecological aspects of human populations past and present are better delimited. In other words, it will require the continued efforts from all branches of science in increasingly synergistic and interdisciplinary ways.


I thank members of the Akey laboratory, particularly Laura Scheinfeldt and Shameek Biswas. I thank Matt Maurano for performing the analysis of the GWAS data. This work was supported by a research grant (1R01GM076036-01A1) from the NIH and a Sloan Fellowship in Computational Biology to J.M.A.


[Supplemental material is available online at www.genome.org.]

Article is online at http://www.genome.org/cgi/doi/10.1101/gr.086652.108.


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