Multilocus estimation of selfing and its heritability.
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ABSTRACT:
SUBMITTER: McClure NS
PROVIDER: S-EPMC3424919 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Predominantly selfing populations are expected to have reduced effective population sizes due to nonrandom sampling of gametes, demographic stochasticity (bottlenecks or extinction-recolonization), and large scale hitchhiking (reduced effective recombination). Thus, they are expected to display low genetic diversity, which was confirmed by empirical studies. The structure of genetic diversity in predominantly selfing species is dramatically different from outcrossing ones, with populations often dominated by one or a few multilocus genotypes (MLGs) coexisting with several rare genotypes. Therefore, multilocus diversity indices are relevant to describe diversity in selfing populations. Here, we use simulations to provide analytical expectations for multilocus indices and examine whether selfing alone can be responsible for the high-frequency MLGs persistent through time in the absence of selection. We then examine how combining single and multilocus indices of diversity may be insightful to distinguish the effects of selfing, population size, and more complex demographic events (bottlenecks, migration, admixture, or extinction-recolonization). Finally, we examine how temporal changes in MLG frequencies can be insightful to understand the evolutionary trajectory of a given population. We show that combinations of selfing and small demographic sizes can result in high-frequency MLGs, as observed in natural populations. We also show how different demographic scenarios can be distinguished by the parallel analysis of single and multilocus indices of diversity, and we emphasize the importance of temporal data for the study of predominantly selfing populations. Finally, the comparison of our simulations with empirical data on populations of Medicago truncatula confirms the pertinence of our simulation framework.
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Project description:Estimating the genetic variance available for traits informs us about a population's ability to evolve in response to novel selective challenges. In selfing species, theory predicts a loss of genetic diversity that could lead to an evolutionary dead-end, but empirical support remains scarce. Genetic variability in a trait is estimated by correlating the phenotypic resemblance with the proportion of the genome that two relatives share identical by descent ('realized relatedness'). The latter is traditionally predicted from pedigrees (? A : expected value) but can also be estimated using molecular markers (average number of alleles shared). Nevertheless, evolutionary biologists, unlike animal breeders, remain cautious about using marker-based relatedness coefficients to study complex phenotypic traits in populations. In this paper, we review published results comparing five different pedigree-free methods and use simulations to test individual-based models (hereafter called animal models) using marker-based relatedness coefficients, with a special focus on the influence of mating systems. Our literature review confirms that Ritland's regression method is unreliable, but suggests that animal models with marker-based estimates of relatedness and genomic selection are promising and that more testing is required. Our simulations show that using molecular markers instead of pedigrees in animal models seriously worsens the estimation of heritability in outcrossing populations, unless a very large number of loci is available. In selfing populations the results are less biased. More generally, populations with high identity disequilibrium (consanguineous or bottlenecked populations) could be propitious for using marker-based animal models, but are also more likely to deviate from the standard assumptions of quantitative genetics models (non-additive variance).
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Project description:Estimation of narrow-sense heritability, h(2), from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ~300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%-10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h(2) estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h(2) are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h(2) estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h(2) estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.
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Project description:Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.
| S-EPMC4317649 | biostudies-literature
Project description: Not available
| S-EPMC3852919 | biostudies-literature
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.
| S-EPMC4159725 | biostudies-literature
Project description:Linear mixed models (LMM) are widely used to estimate narrow sense heritability explained by tagged single-nucleotide polymorphisms (SNPs). However, those estimates are valid only if large sample sizes are used. We propose a Bayesian covariance component model (BCCM) that takes into account the genetic correlation among phenotypes and genetic correlation among individuals. The use of the BCCM allows us to circumvent issues related to small sample sizes, including overfitting and boundary estimates. Using expression of genes in breast cancer pathway, obtained from the Multiple Tissue Human Expression Resource (MuTHER) project, we demonstrate a significant improvement in the accuracy of SNP-based heritability estimates over univariate and likelihood-based methods. According to the BCCM, except CHURC1 (h2 = 0.27, credible interval = (0.2, 0.36)), all tested genes have trivial heritability estimates, thus explaining why recent progress in their eQTL identification has been limited.
| S-EPMC5945852 | biostudies-other
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.
| S-EPMC7672693 | biostudies-literature
Project description:While novel statistical methods quantifying the shared heritability of traits and diseases between ancestral distinct populations have been recently proposed, a thorough evaluation of these approaches under differing circumstances remain elusive. Brown et al.2016 proposed the method Popcorn to estimate the shared heritability, i.e. genetic correlation, using only summary statistics. Here, we evaluate Popcorn under several parameters and circumstances: sample size, number of SNPs, sample size of external reference panel, various population pairs, inappropriate external reference panel, and admixed population involved. Our results determined the minimum sample size of the external reference panel, summary statistics, and number of SNPs required to accurately estimate both the genetic correlation and heritability. Moreover, the number of individuals and SNPs required to produce accurate and stable estimates was directly proportional with heritability in Popcorn. Misrepresentation of the reference panel overestimated the genetic correlation by 20% and heritability by 60%. Lastly, applying Popcorn to homogeneous (EUR) and admixed (ASW) populations underestimated the genetic correlation by 15%. Although statistical approaches estimating the shared heritability between ancestral populations will provide novel etiologic insight, caution is required ensuring results are based on the appropriate sample size, number of SNPs, and the generalizability of the reference panel to the discovery populations.
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Project description:MotivationGene-environment (GxE) interactions are one of the least studied aspects of the genetic architecture of human traits and diseases. The environment of an individual is inherently high dimensional, evolves through time and can be expensive and time consuming to measure. The UK Biobank study, with all 500 000 participants having undergone an extensive baseline questionnaire, represents a unique opportunity to assess GxE heritability for many traits and diseases in a well powered setting.ResultsWe have developed a randomized Haseman-Elston non-linear regression method applicable when many environmental variables have been measured on each individual. The method (GPLEMMA) simultaneously estimates a linear environmental score (ES) and its GxE heritability. We compare the method via simulation to a whole-genome regression approach (LEMMA) for estimating GxE heritability. We show that GPLEMMA is more computationally efficient than LEMMA on large datasets, and produces results highly correlated with those from LEMMA when applied to simulated data and real data from the UK Biobank.Availability and implementationSoftware implementing the GPLEMMA method is available from https://jmarchini.org/gplemma/.Supplementary informationSupplementary data are available at Bioinformatics online.
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