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Sci Rep. 2016 Jun 8;6:27644. doi: 10.1038/srep27644.

A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics.

Pare G1,2,3,4, Mao S3, Deng WQ5.

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Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
Population Genomics Program, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON L8S 4L8, Canada.
Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON L8L 2X2, Canada.
Thrombosis and Atherosclerosis Research Institute, Hamilton, ON L8L 2X2, Canada.
Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, Canada.


Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (Nā€‰=ā€‰7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.

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