Estimation of SNP heritability from dense genotype data.
Ontology highlight
ABSTRACT:
SUBMITTER: Lee SH
PROVIDER: S-EPMC3852919 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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Project description:Identification of genomic loci and segments that are identical by descent (IBD) allows inference on problems such as relatedness detection, IBD disease mapping, heritability estimation and detection of recent or ongoing positive selection. Here, employing a novel statistical method, we use IBD to find signals of selection in the Maasai from Kinyawa, Kenya (MKK). In doing so, we demonstrate the advantage of statistical tools that can probabilistically estimate IBD sharing without having to thin genotype data because of linkage disequilibrium (LD), and that allow for both inbreeding and more than one allele to be shared IBD. We use our novel method, GIBDLD, to estimate IBD sharing between all pairs of individuals at all genotyped SNPs in the MKK, and, by looking for genomic regions showing excess IBD sharing in unrelated pairs, find loci that are known to have undergone recent selection (eg, the LCT gene and the HLA region) as well as many novel loci. Intriguingly, those loci that show the highest amount of excess IBD, with the exception of HLA, also show a substantial number of unrelated pairs sharing all four of their alleles IBD. In contrast to other IBD detection methods, GIBDLD provides accurate probabilistic estimates at each locus for all nine possible IBD sharing states between a pair of individuals, thus allowing for consanguinity, while also modeling LD, thus removing the need to thin SNPs. These characteristics will prove valuable for those doing genetic studies, and estimating IBD, in the wide variety of human populations.
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Project description:The heritability of a trait (h(2)) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs (h(2)(SNP)). Thus far, the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining h(2)(SNP)) under circumstances wider than those under which it has so far been derived.
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Project description:MotivationImproved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty.ResultsWe introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which 'borrows strength' from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals.Availability and implementationThe C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BACKGROUND: During this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform. RESULTS: We have developed SNP HiTLink (SNP High Throughput Linkage analysis system). In this system, SNP chip data of the Affymetrix Mapping 100 k/500 k array set and Genome-Wide Human SNP array 5.0/6.0 can be directly imported and passed to parametric or model-free linkage analysis programs; MLINK, Superlink, Merlin and Allegro. Various marker-selecting functions are implemented to avoid the effect of typing-error data, markers in linkage equilibrium or to select informative data. CONCLUSION: The results using the 100 k SNP dataset were comparable or even superior to those obtained from analyses using microsatellite markers in terms of LOD scores obtained. General personal computers are sufficient to execute the process, as runtime for whole-genome analysis was less than a few hours. This system can be widely applied to linkage analysis using microarray-based SNP data and with which one can expect high-throughput and reliable linkage analysis.
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