Concepts, estimation and interpretation of SNP-based heritability

Yang, Jian; Zeng, Jian; Goddard, Michael E.; Wray, Naomi R.; Visscher, Peter M.

doi:10.1038/ng.3941

Cited by 378 publications

(349 citation statements)

References 66 publications

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“…These estimates require assumptions about the relationship between allele frequency, effect size, and linkage disequilibrium (reviewed by Yang et al. ), and direct assessment of individual polygene effects is difficult in the context of small effects, complex genetic backgrounds, and low minor allele frequencies. Here, we used a classical genetics approach to isolate small genomic intervals and directly assess their effects on complex traits.…”

Section: Discussionmentioning

confidence: 99%

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation inC. elegans

et al. 2019

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Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population‐based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near‐isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome‐wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL‐like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large‐effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.

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Section: Discussionmentioning

confidence: 99%

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation inC. elegans

et al. 2019

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“…Several studies (Clayton, 2009;Cleynen et al, 2016;Hamshere et al, 2011;Pashayan et al, 2015;Pharoah, Antoniou, Easton, & Ponder, 2008;Sawcer, Ban, Wason, & Dudbridge, 2010) have shown that use of a limited number of top ranking SNPs can help discriminate diseased cases from unaffected controls, or between different disease sub-phenotypes, but that the utility for individual risk prediction generally falls far short of clinically useful levels. However, even when the combined set of SNPs explain a large proportion of variance, much larger sample sizes are required to achieve high prediction accuracy (Yang et al, 2017) because the individual SNP effects are substantially smaller than the total variance they explain. For example Dudbridge (2013) showed that the disappointing AUCs reported by Machiela et al (2011) were entirely consistent with the theoretical AUC values of 52-54% predicted on the basis of their discovery set sample size, but that these values could be increased to ≈ 80 − 90% if the samples were infinitely large.…”

Section: Discussionmentioning

confidence: 99%

Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

Cherlin

Plant

Taylor

et al. 2018

Genetic Epidemiology

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Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome‐wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10‐fold cross validation to assess predictive performance, with nested 10‐fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

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“…SNP heritability is the ceiling for GWAS and for GPS heritability because all three rely on the additive effects of SNPs genotyped on SNP chips93. The missing heritability gap between GPS heritability (10%) and SNP heritability (25%) can be narrowed by increasing GWAS sample size.…”

Section: What Is Intelligence?mentioning

confidence: 99%

The new genetics of intelligence

2018

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Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants. In this Review, we highlight the latest innovations and insights from the genetics of intelligence and their applications and implications for science and society.

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Concepts, estimation and interpretation of SNP-based heritability

Cited by 378 publications

References 66 publications

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation inC. elegans

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation inC. elegans

Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

The new genetics of intelligence

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