Dataset Information

Finding missing heritability in less significant Loci and allelic heterogeneity: genetic variation in human height.

ABSTRACT: Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of 'missing' heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10(-8)) showed evidence of allelic heterogeneity. Additionally, including less significant loci (i.e., p-value<5×10(-4)) and accounting for effects of allelic heterogeneity substantially improved the variance explained in height.

SUBMITTER: Zhang G

PROVIDER: S-EPMC3521016 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Finding missing heritability in less significant Loci and allelic heterogeneity: genetic variation in human height.

Zhang Ge G Karns Rebekah R Sun Guangyun G Indugula Subba Rao SR Cheng Hong H Havas-Augustin Dubravka D Novokmet Natalija N Durakovic Zijad Z Missoni Sasa S Chakraborty Ranajit R Rudan Pavao P Deka Ranjan R

PloS one 20121212 12

Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of 'missing' heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wi ...[more]

PMID: 23251454

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Project description:Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10??). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10??) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10?³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10?¹? and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10??), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10??) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10?¹? compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Dataset Information

Finding missing heritability in less significant Loci and allelic heterogeneity: genetic variation in human height.

Publications

Finding missing heritability in less significant Loci and allelic heterogeneity: genetic variation in human height.

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