Interpretation of Association Signals and Identification of Causal Variants from Genome-wide Association Studies

被引:115
作者
Wang, Kai [1 ]
Dickson, Samuel P. [2 ]
Stolle, Catherine A. [3 ]
Krantz, Ian D. [4 ,5 ]
Goldstein, David B. [2 ]
Hakonarson, Hakon [1 ,4 ,5 ]
机构
[1] Childrens Hosp Philadelphia, Ctr Appl Genom, Philadelphia, PA 19104 USA
[2] Duke Univ, Ctr Human Genome Variat, Inst Genome Sci & Policy, Durham, NC 27708 USA
[3] Childrens Hosp Philadelphia, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA
[4] Childrens Hosp Philadelphia, Div Human Genet, Philadelphia, PA 19104 USA
[5] Univ Penn, Dept Pediat, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院; 英国惠康基金;
关键词
LINKAGE-DISEQUILIBRIUM PATTERNS; COMMON DISEASE; RARE VARIANTS; CROHNS-DISEASE; MISSING HERITABILITY; COMPLEX DISEASES; MUTATIONS; GENETICS; SUSCEPTIBILITY; CONTRIBUTE;
D O I
10.1016/j.ajhg.2010.04.003
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
GWAS have been successful in identifying disease susceptibility loci, but it remains a challenge to pinpoint the causal variants in subsequent fine-mapping studies. A conventional fine-mapping effort starts by sequencing dozens of randomly selected samples at susceptibility loci to discover candidate variants, which are then placed on custom arrays or used in imputation algorithms to find the causal variants. We propose that one or several rare or low-frequency causal variants can hitchhike the same common tag SNP, so causal variants may not be easily unveiled by conventional efforts. Here, we first demonstrate that the true effect size and proportion of variance explained by a collection of rare causal variants can be underestimated by a common tag SNP, thereby accounting for some of the "missing heritability" in GWAS. We then describe a case-selection approach based on phasing long-range haplotypes and sequencing cases predicted to harbor causal variants. We compare this approach with conventional strategies on a simulated data set, and we demonstrate its advantages when multiple causal variants are present. We also evaluate this approach in a GWAS on hearing loss, where the most common causal variant has a minor allele frequency (MAF) of 1.3% in the general population and 8.2% in 329 cases. With our case-selection approach, it is present in 88% of the 32 selected cases (MAF = 66%), so sequencing a subset of these cases can readily reveal the causal allele. Our results suggest that thinking beyond common variants is essential in interpreting GWAS signals and identifying causal variants.
引用
收藏
页码:730 / 742
页数:13
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