Common SNPs explain a large proportion of the heritability for human height

Yang, Jian; Benyamin, Beben; McEvoy, Brian; Gordon, Scott D.; Henders, Anjali K.; Nyholt, Dale R.; Madden, Pamela A. F.; Heath, Andrew C.; Martin, Nicholas G.; Montgomery, Grant W.; Goddard, Michael E.; Visscher, Peter M.

doi:10.1038/ng.608

Cited by 4,057 publications

(5,032 citation statements)

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“…Ruling out these two possible causes for the missing heritability, the most likely explanation is the presence of many minor‐effect loci (Yang et al . 2010; Rockman 2012). Alternatively, part of the missing heritability may be caused by unknown loci that interact epistatically (Huang et al .…”

Section: Discussionmentioning

confidence: 99%

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The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host–pathogen co‐evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major‐effect loci. Sigma virus resistance involves more genes – we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co‐evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major‐effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near‐Mendelian trait.

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Section: Discussionmentioning

confidence: 99%

“…2012; Mackay 2014), many very small effect loci (Yang et al . 2010; Rockman 2012) and large numbers of rare alleles of large effect (Bansal et al . 2010; Thornton et al .…”

Section: Introductionmentioning

confidence: 99%

The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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“…Genome-wide association studies (GWAS) based on common variants have been the dominant approach to achieve this objective [1]. However, the genomic variants identified in GWAS studies often explain only a small portion of the phenotypic variation related to heritable human diseases, a phenomenon known as "missing heritability" in genomics literature [2].…”

Section: Introductionmentioning

confidence: 99%

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

Chen

Sun

2013

BMC Genomics

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BackgroundGenome-wide association studies (GWAS) have identified many common polymorphisms associated with complex traits. However, these associated common variants explain only a small fraction of the phenotypic variances, leaving a substantial portion of genetic heritability unexplained. As a result, searches for "missing" heritability are drawing increasing attention, particularly for rare variant studies that often require a large sample size and, thus, extensive sequencing effort. Although the development of next generation sequencing (NGS) technologies has made it possible to sequence a large number of reads economically and efficiently, it is still often cost prohibitive to sequence thousands of individuals that are generally required for association studies. A more efficient and cost-effective design would involve pooling the genetic materials of multiple individuals together and then sequencing the pools, instead of the individuals. This pooled sequencing approach has improved the plausibility of association studies for rare variants, while, at the same time, posed a great challenge to the pooled sequencing data analysis, essentially because individual sample identity is lost, and NGS sequencing errors could be hard to distinguish from low frequency alleles.ResultsA unified approach for estimating minor allele frequency, SNP calling and association studies based on pooled sequencing data using an expectation maximization (EM) algorithm is developed in this paper. This approach makes it possible to study the effects of minor allele frequency, sequencing error rate, number of pools, number of individuals in each pool, and the sequencing depth on the estimation accuracy of minor allele frequencies. We show that the naive method of estimating minor allele frequencies by taking the fraction of observed minor alleles can be significantly biased, especially for rare variants. In contrast, our EM approach can give an unbiased estimate of the minor allele frequency under all scenarios studied in this paper. A SNP calling approach, EM-SNP, for pooled sequencing data based on the EM algorithm is then developed and compared with another recent SNP calling method, SNVer. We show that EM-SNP outperforms SNVer in terms of the fraction of db-SNPs among the called SNPs, as well as transition/transversion (Ti/Tv) ratio. Finally, the EM approach is used to study the association between variants and type I diabetes.ConclusionsThe EM-based approach for the analysis of pooled sequencing data can accurately estimate minor allele frequencies, call SNPs, and find associations between variants and complex traits. This approach is especially useful for studies involving rare variants.

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“…More than 20 variants have now been identified that contribute to AD,20, 21, 22 and in IS recent progress has resulted in the identification of a number of variants, the majority of which have been associated with specific stroke subtypes 23, 24, 25, 26, 27, 28. Indeed, recent evidence from GWAS indicates that a large proportion of risk in complex diseases such as AD and IS is attributable to the combined effects of a large number of common genetic variants,29, 30 each conferring only a small amount of disease risk 31, 32, 33. Recent studies estimate the proportion of variance explained by the genetic contribution from common variants to AD and IS to be approximately 24.0% and 18.0%, respectively.…”

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confidence: 99%

Shared genetic contribution to ischemic stroke and Alzheimer's disease

Traylor¹,

Adib-Samii²,

Harold³

et al. 2016

Annals of Neurology

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ObjectiveIncreasing evidence suggests epidemiological and pathological links between Alzheimer's disease (AD) and ischemic stroke (IS). We investigated the evidence that shared genetic factors underpin the two diseases.MethodsUsing genome‐wide association study (GWAS) data from METASTROKE + (15,916 IS cases and 68,826 controls) and the International Genomics of Alzheimer's Project (IGAP; 17,008 AD cases and 37,154 controls), we evaluated known associations with AD and IS. On the subset of data for which we could obtain compatible genotype‐level data (4,610 IS cases, 1,281 AD cases, and 14,320 controls), we estimated the genome‐wide genetic correlation (rG) between AD and IS, and the three subtypes (cardioembolic, small vessel, and large vessel), using genome‐wide single‐nucleotide polymorphism (SNP) data. We then performed a meta‐analysis and pathway analysis in the combined AD and small vessel stroke data sets to identify the SNPs and molecular pathways through which disease risk may be conferred.ResultsWe found evidence of a shared genetic contribution between AD and small vessel stroke (rG [standard error] = 0.37 [0.17]; p = 0.011). Conversely, there was no evidence to support shared genetic factors in AD and IS overall or with the other stroke subtypes. Of the known GWAS associations with IS or AD, none reached significance for association with the other trait (or stroke subtypes). A meta‐analysis of AD IGAP and METASTROKE + small vessel stroke GWAS data highlighted a region (ATP5H/KCTD2/ICT1) associated with both diseases (p = 1.8 × 10−8). A pathway analysis identified four associated pathways involving cholesterol transport and immune response.InterpretationOur findings indicate shared genetic susceptibility to AD and small vessel stroke and highlight potential causal pathways and loci. Ann Neurol 2016;79:739–747

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Common SNPs explain a large proportion of the heritability for human height

Cited by 4,057 publications

References 32 publications

The genetic architecture of resistance to virus infection in Drosophila

The genetic architecture of resistance to virus infection in Drosophila

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

Shared genetic contribution to ischemic stroke and Alzheimer's disease

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