Relatedness disequilibrium regression estimates heritability without environmental bias.
Ontology highlight
ABSTRACT:
SUBMITTER: Young AI
PROVIDER: S-EPMC6130754 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Publications
Relatedness disequilibrium regression estimates heritability without environmental bias.
Young Alexander I AI Frigge Michael L ML Gudbjartsson Daniel F DF Thorleifsson Gudmar G Bjornsdottir Gyda G Sulem Patrick P Masson Gisli G Thorsteinsdottir Unnur U Stefansson Kari K Kong Augustine A
Nature genetics 20180813 9
Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is important in the nature-versus-nurture debate. However, existing estimates of heritability may be biased by environmental effects. Here, we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR avoids most sources of environmental bias by exploiting variation in relatedness due to random Mendelian segregation. We used a sample ...[more]
Similar Datasets
Project description:Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin- and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than 0.025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus nonurban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal.
| S-EPMC4126504 | biostudies-literature
Project description:Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes.We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.
| S-EPMC3282397 | biostudies-literature
Project description:Assortative mating is pervasive in wild populations and commonly described as a positive correlation between the phenotypes of males and females across mated pairs. This correlation is often assumed to reflect non-random mate choice based on phenotypic similarity. However, phenotypic resemblance between mates can also arise when their traits respond plastically to a shared environmental effect creating a (within-pair) residual correlation in traits. Using long-term data collected in pairs of wild blue tits and a covariance partitioning approach, we empirically demonstrate that such residual covariance indeed exists and can generate phenotypic correlations (or mask assortative mating) in behavioural and morphometric traits. These findings (i) imply that residual covariance is likely to be common and bias phenotypic estimates of assortative mating, which can have consequences for evolutionary predictions, (ii) call for the use of rigorous statistical approaches in the study of assortative mating, and (iii) show the applicability of one of these approaches in a common study system.
| S-EPMC6083224 | biostudies-literature
Project description:Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi-quantitative description of species' ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities-particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models-fitted with and without group-level predictors (e.g., habitat type)-to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot-level differences in mean EIV scores between time-periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R 2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.
| S-EPMC6202714 | biostudies-literature
Project description:The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism 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) to provide consistent estimates of single nucleotide polymorphism 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 linkage disequilibrium 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 linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach.
| S-EPMC9339317 | biostudies-literature
Project description:BackgroundSingle nucleotide polymorphism-based heritability is a fundamental quantity in the genetic analysis of complex traits. For case-control phenotypes, for which the continuous distribution of risk in the population is unobserved, observed-scale heritability estimates must be transformed to the more interpretable liability scale. This article describes how the field standard approach incorrectly performs the liability correction in that it does not appropriately account for variation in the proportion of cases across the cohorts comprising the meta-analysis. We propose a simple solution that incorporates cohort-specific ascertainment using the summation of effective sample sizes across cohorts. This solution is applied at the stage of single nucleotide polymorphism-based heritability estimation and does not require generating updated meta-analytic genome-wide association study summary statistics.MethodsWe began by performing a series of simulations to examine the ability of the standard approach and our proposed approach to recapture liability-scale heritability in the population. We went on to examine the differences in estimates obtained from these 2 approaches for real data for 12 major case-control genome-wide association studies of psychiatric and neurologic traits.ResultsWe found that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as approximately 50% in simulation and approximately 30% in real data.ConclusionsPrior estimates of liability-scale heritability for genome-wide association study meta-analysis may be drastically underestimated. To this end, we strongly recommend using our proposed approach of using the sum of effective sample sizes across contributing cohorts to obtain unbiased estimates.
| S-EPMC10066905 | biostudies-literature
Project description:Heritability is the proportion of phenotypic variance in a population that is attributable to individual genotypes. Heritability is considered an important measure in both evolutionary biology and in medicine, and is routinely estimated and reported in genetic epidemiology studies. In population-based genome-wide association studies (GWAS), mixed models are used to estimate variance components, from which a heritability estimate is obtained. The estimated heritability is the proportion of the model's total variance that is due to the genetic relatedness matrix (kinship measured from genotypes). Current practice is to use bootstrapping, which is slow, or normal asymptotic approximation to estimate the precision of the heritability estimate; however, this approximation fails to hold near the boundaries of the parameter space or when the sample size is small. In this paper we propose to estimate variance components via a Haseman-Elston regression, find the asymptotic distribution of the variance components and proportions of variance, and use them to construct confidence intervals (CIs). Our method is further developed to obtain unbiased variance components estimators and construct CIs by meta-analyzing information from multiple studies. We demonstrate our approach on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).
| S-EPMC5857391 | biostudies-literature
Project description:In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing [Formula: see text] values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ([Formula: see text]), standard errors of these standardized estimates are reasonable ([Formula: see text]) for [Formula: see text] trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., [Formula: see text] as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS's on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.
| S-EPMC8093160 | biostudies-literature
Project description:ObjectiveTo illustrate the impact of combining 24 h recall (24hR) and FFQ estimates using regression calibration (RC) and enhanced regression calibration (ERC) on diet-disease associations.SettingWageningen area, the Netherlands, 2011-2013.DesignFive approaches for obtaining self-reported dietary intake estimates of protein and K were compared: (i) uncorrected FFQ intakes (FFQ); (ii) uncorrected average of two 24hR ( $\overline {\rm R}$ ); (iii) average of FFQ and $\overline {\rm R}$ ( ${\overline {\rm F}}\,\overline {\rm R}}$ ); (iv) RC from regression of 24hR v. FFQ; and (v) ERC by adding individual random effects to the RC approach. Empirical attenuation factors (AF) were derived by regression of urinary biomarker measurements v. the resulting intake estimates.ParticipantsData of 236 individuals collected within the National Dietary Assessment Reference Database.ResultsBoth FFQ and 24hR dietary intake estimates were measured with substantial error. Using statistical techniques to correct for measurement error (i.e. RC and ERC) reduced bias in diet-disease associations as indicated by their AF approaching 1 (RC 1·14, ERC 0·95 for protein; RC 1·28, ERC 1·34 for K). The larger sd and narrower 95% CI of AF obtained with ERC compared with RC indicated that using ERC has more power than using RC. However, the difference in AF between RC and ERC was not statistically significant, indicating no significantly better de-attenuation by using ERC compared with RC. AF larger than 1, observed for the ERC for K, indicated possible overcorrection.ConclusionsOur study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.
| S-EPMC10260535 | biostudies-literature
Project description:BackgroundThe use of molecular genetic technologies for broodstock management and selective breeding of aquaculture species is becoming increasingly more common with the continued development of genome tools and reagents. Several laboratories have produced genetic maps for rainbow trout to aid in the identification of loci affecting phenotypes of interest. These maps have resulted in the identification of many quantitative/qualitative trait loci affecting phenotypic variation in traits associated with albinism, disease resistance, temperature tolerance, sex determination, embryonic development rate, spawning date, condition factor and growth. Unfortunately, the elucidation of the precise allelic variation and/or genes underlying phenotypic diversity has yet to be achieved in this species having low marker densities and lacking a whole genome reference sequence. Experimental designs which integrate segregation analyses with linkage disequilibrium (LD) approaches facilitate the discovery of genes affecting important traits. To date the extent of LD has been characterized for humans and several agriculturally important livestock species but not for rainbow trout.ResultsWe observed that the level of LD between syntenic loci decayed rapidly at distances greater than 2 cM which is similar to observations of LD in other agriculturally important species including cattle, sheep, pigs and chickens. However, in some cases significant LD was also observed up to 50 cM. Our estimate of effective population size based on genome wide estimates of LD for the NCCCWA broodstock population was 145, indicating that this population will respond well to high selection intensity. However, the range of effective population size based on individual chromosomes was 75.51 - 203.35, possibly indicating that suites of genes on each chromosome are disproportionately under selection pressures.ConclusionsOur results indicate that large numbers of markers, more than are currently available for this species, will be required to enable the use of genome-wide integrated mapping approaches aimed at identifying genes of interest in rainbow trout.
| S-EPMC2800115 | biostudies-literature