Pitfalls of predicting complex traits from SNPs

Wray, Naomi R.; Yang, Jian; Hayes, Ben J.; Price, Alkes L.; Goddard, Michael E.; Visscher, Peter M.

doi:10.1038/nrg3457

Cited by 668 publications

(659 citation statements)

References 81 publications

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“…3B) using the overdominant encoding. We conclude that our strategy enabled excellent phenotypic predictions and subsequent estimation of variance in our diallel, but we note that variance estimates cannot be necessarily extrapolated to other genotypes (49). We further estimated the contribution of the significant loci to total phenotypic variation.…”

Section: Resultsmentioning

confidence: 80%

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.T he often observed phenotypic superiority of progeny relative to their parents, or heterosis, is a universal phenomenon and of great importance to plant agriculture. The earliest description of heterosis (also known as hybrid vigor or superiority) dates to Darwin's studies of cross-fertilization in plants. He noticed that intercrossing distantly related individuals gave rise to larger, more vigorous progeny (1). Four decades later, George Shull coined the term "heterosis" (2) for this phenomenon, which he and Edward East had independently described for hybrids of inbred maize in 1908 (3, 4). Heterosis has long been of interest to evolutionary biologists as a potential explanation for the ubiquity of cross-fertilization in plants and animals, but it is also a central component of agricultural breeding programs. The combination of hybrid seed technology and inbred line improvement has driven an unprecedented improvement in maize yield over the past century (5). Despite the economic importance of heterosis and its intensive investigation in a wide spectrum of species, prediction of hybrid performance from parental information remains a major challenge (6).In the terms of quantitative genetics, hybrid vigor (and its opposite, inferiority) describes a deviation of progeny from the phenotypic mean of the parents. This means that heterosis cannot be explained by the addition of the effects of contributing alleles (7). Nonadditive genetic variance can result from a nonlinear phenotypic effect of alleles at one locus, as in the case of dominant/recessive allele pairs in classical genetics, or from epistatic interactions ...

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Section: Resultsmentioning

confidence: 80%

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…However, there is no overlap between the Australian at‐risk subjects and the PGC discovery sample, nor are the Australian at‐risk subjects related to this sample. While the overall contribution of those NIMH relatives to the PGC discovery sample was very small (<2% of the cases from the PGC discovery dataset), our findings should be considered as an extension rather than an independent replication of the PGC findings (see also discussion in [Wray et al, 2013]). Using only the most significant SNPs is also a limitation of our study, and we acknowledge that many more variants of importance in conferring risk to bipolar disorder have not been assessed.…”

Section: Limitationsmentioning

confidence: 72%

“…Indeed, fewer than half of the 32 SNPs in our panel are represented on any one high‐density SNP chip currently commercially available, although direct genotyping of a larger number of SNPs showing nominally significant association will be possible with the PsychChip (Illumina, San Diego, CA). Imputation was used in the determination of genotypes in the unrelated control group for AUC estimation, and while imputation accuracy was high (97.4% concordance), this is a limitation of the AUC analysis and, together with the small overlap between the PGC discovery sample and the US family samples, should be taken into consideration with interpretation of the AUC data [Wray et al, 2013]. It should be noted that limitations of genotyping platform and imputation did not apply to the key within‐family results.…”

Section: Limitationsmentioning

confidence: 99%

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

Fullerton

Koller

Edenberg

et al. 2015

American J of Med Genetics Pt B

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Recent studies have revealed the polygenic nature of bipolar disorder (BP), and identified common risk variants associated with illness. However, the role of common polygenic risk in multiplex families has not previously been examined. The present study examined 249 European‐ancestry families from the NIMH Genetics Initiative sample, comparing subjects with narrowly defined BP (excluding bipolar II and recurrent unipolar depression; n = 601) and their adult relatives without BP (n = 695). Unrelated adult controls (n = 266) were from the NIMH TGEN control dataset. We also examined a prospective cohort of young (12–30 years) offspring and siblings of individuals with BPI and BPII disorder (at risk; n = 367) and psychiatrically screened controls (n = 229), ascertained from five sites in the US and Australia and assessed with standardized clinical protocols. Thirty‐two disease‐associated SNPs from the PGC‐BP Working Group report (2011) were genotyped and additive polygenic risk scores (PRS) derived. We show increased PRS in adult cases compared to unrelated controls (P = 3.4 × 10−5, AUC = 0.60). In families with a high‐polygenic load (PRS score ≥32 in two or more subjects), PRS distinguished cases with BPI/SAB from other relatives (P = 0.014, RR = 1.32). Secondly, a higher PRS was observed in at‐risk youth, regardless of affected status, compared to unrelated controls (GEE‐χ2 = 5.15, P = 0.012). This report is the first to explore common polygenic risk in multiplex families, albeit using only a small number of robustly associated risk variants. We show that individuals with BP have a higher load of common disease‐associated variants than unrelated controls and first‐degree relatives, and illustrate the potential utility of PRS assessment in a family context. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

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“…11,12 Originally applied to animal and plant breeding, 13,14 LMMs have become a powerful method to estimate SNP-based heritability using GWAS data. 15,16 By assuming a common distribution that describes the allelic effects at all SNPs and focusing on the aggregated genetic effects rather than individual genetic effects, LMMs and BLUP are particularly attractive for modeling polygenic architecture. On the other hand, to capture minute effects spread over a majority of the genome, this assumed common distributionusually a Gaussian distribution-fails to capture major trait loci with large effects efficiently.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Coram

Fang

Candille

et al. 2017

The American Journal of Human Genetics

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An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

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Pitfalls of predicting complex traits from SNPs

Cited by 668 publications

References 81 publications

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

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