Estimating SNP heritability in presence of population substructure in biobank-scale datasets

Lin, Zhaotong; Seal, Souvik; Basu, Saonli

doi:10.1093/genetics/iyac015

Cited by 6 publications

(12 citation statements)

References 32 publications

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“…But despite the widespread use of , its interpretation remains unclear, particularly in the presence of admixture and population structure. It is generally accepted that can be biased in structured populations because of confounding effects of unobserved environmental factors and LD between causal variants [4, 7, 9–11, 46]. But may be biased even in the absence of confounding because of misspecification of the GREML model, i.e., if the model does not represent the genetic architecture from which the trait is sampled [14–16, 47, 48].…”

Section: Discussionmentioning

confidence: 99%

“…Despite the utility and simple intuition of , there is much confusion about its interpretation and equivalence to h 2 , particularly in the presence of population structure and assortative mating [7–12]. But much of the discussion of heritability in structured populations has focused on biases in – the estimator – due to confounding effects of shared environment and linkage disequilibrium (LD) with other variants [7, 9–11, 13]. There is comparatively little discussion, at least in human genetics, on the fact that LD due to population structure also contributes to genetic variance, and therefore, is a component of heritability [1] (but see [14–16] for a rigorous discussion).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the utility and simple intuition of h 2 snp , there is much confusion about its interpretation and equivalence to h 2 , particularly in the presence of population structure and assortative mating [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Interpreting SNP heritability in admixed populations

Huang

Basu

Shriver

et al. 2023

Preprint

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SNP heritability (h2snp) is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability (h2), being equal to it if all causal variants are known. Despite the simple intuition behind h2snp, its interpretation and equivalence to h2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in h2snp estimates. Here we use analytical theory and simulations to demonstrate that estimates of h2snp are not guaranteed to be equal to h2 in admixed populations, even in the absence of confounding and even if the causal variants are known. We interpret this discrepancy arising not because the estimate is biased, but because the estimand itself as defined under the random effects model may not be equal to h2. The model assumes that SNP effects are uncorrelated which may not be true, even for unlinked loci in admixed and structured populations, leading to over- or under-estimates of h2snp relative to h2. For the same reason, local ancestry heritability (hgamma) may also not be equal to the variance explained by local ancestry in admixed populations. We describe the quantitative behavior of h2snp and h2gamma as a function of admixture history and the genetic architecture of the trait and discuss its implications for genome-wide association and polygenic prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpreting SNP heritability in admixed populations

Huang

Basu

Shriver

et al. 2023

Preprint

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“…In general, MOM estimators had much larger standard errors compared to the likelihood-based estimators. However, the computational gain of these MOM estimators over the likelihood estimators is significant for large n and M and often outweighs limitation of large standard error ( Lin et al . 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Comparing heritability estimators under alternative structures of linkage disequilibrium

Min

Thompson

Basu

2022

G3 Genes|Genomes|Genetics

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The SNP heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms (SNPs). The existing approaches to estimate SNP heritability can be broadly classified into two categories. One set of approaches model the SNP effects as fixed effects and the other treats the SNP effects as random effects. These methods make certain assumptions about the dependency among individuals (familial relationship) as well as the dependency among markers (linkage disequilibrium, LD) to provide consistent estimates of SNP heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here we investigate the impact of different structures of LD and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of LD and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the two method-of-moments estimators, one using a fixed-SNP-effects approach, and another using a random-SNP-effects approach.

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“…Alternative to the REML estimation approaches, there are other ways of estimating heritability from the LMM framework that can be computationally much faster but cost significant efficiency [ 15 – 17 ]. There are also methods like LDAK [ 18 ], MultiBLUP [ 19 ] which are based on more realistic assumptions than the standard LMM framework considered in the GCTA-GREML methods.…”

Section: Introductionmentioning

confidence: 99%

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

2022

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With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.

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Estimating SNP heritability in presence of population substructure in biobank-scale datasets

Cited by 6 publications

References 32 publications

Interpreting SNP heritability in admixed populations

Interpreting SNP heritability in admixed populations

Comparing heritability estimators under alternative structures of linkage disequilibrium

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

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