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Background: Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. Results: GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. Conclusions: The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended. [ABSTRACT FROM AUTHOR]

William G. Hill, universally known as Bill Hill, died on December 17, 2021, aged 81. Bill also compared the observed and predicted long-term response to selection and concluded that these data had little power to distinguish among different models for quantitative genetic variation, including the "infinitesimal model" that postulates a very large number of loci with very small effects (Hill 2010). Appropriately, John Sved and Bill wrote an insightful I Perspective i in Genetics reviewing 100 years of work on LD (Sved and Hill 2018). In particular, he was the first to quantify the role of new mutations in producing the variability needed for long-term continued responses to selection, which are such a remarkable feature of many artificial selection experiments and animal and plant breeding programs (Hill 1982a,b; Keightley and Hill 1983). [Extracted from the article]

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% – 23% higher than corrected estimates using UK Biobank data. Methods for estimating heritability, the fraction of variance attributable to genetic factors, assume random mating. Here, the authors show that under assortative mating, when mate choice reflects phenotypic similarity, these methods produce overestimates. [ABSTRACT FROM AUTHOR]

Attributing the similarity between individuals to genetic and non-genetic factors is central to genetic analyses. In this paper we use the genomic relationship (π ) among 417,060 individuals to investigate the phenotypic covariance between pairs of individuals for 32 traits across the spectrum of relatedness, from unrelated pairs through to identical twins. We find linear relationships between phenotypic covariance and π that agree with the SNP-based heritability ( h ̂ S N P 2 ) in unrelated pairs (π < 0.02 ), and with pedigree-estimated heritability in close relatives (π > 0.05 ). The covariance increases faster than π h ̂ S N P 2 in distant relatives (0.02 > π > 0.05 ), and we attribute this to imperfect linkage disequilibrium between causal variants and the common variants used to construct π . We also examine the effect of assortative mating on heritability estimates from different experimental designs. We find that full-sib identity-by-descent regression estimates for height (0.66 s.e. 0.07) are consistent with estimates from close relatives (0.82 s.e. 0.04) after accounting for the effect of assortative mating. Assigning inter-individual similarities to genetic and non-genetic factors is central to quantitative genetics. Here, the authors look at phenotypic covariance among pairs of individuals for 32 traits across the UK Biobank, from nominally unrelated pairs through to monozygotic twins. [ABSTRACT FROM AUTHOR]

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results,we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide. [ABSTRACT FROM AUTHOR]

The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches. [ABSTRACT FROM AUTHOR]

Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the resemblance between relatives is solely due to additive genetic variation. We show that well-known results for balanced designs are special cases of a more general unified framework. For pedigree designs, the sampling variance is inversely proportional to the variance of relationship in the pedigree and it is proportional to 1/N, whereas for population samples it is approximately proportional to 1/N², where N is the sample size. Variation in relatedness is a key parameter in the quantification of the sampling variance of heritability. Consequently, the sampling variance is high for populations with large recent effective population size (e.g., humans) because this causes low variation in relationship. However, even using human population samples, low sampling variance is possible with high N. [ABSTRACT FROM AUTHOR]

This has been corrected in both the PDF and HTML versions of the Article. The original version of this Article contained an error in the third sentence of the results subsection "AM-induced bias persists when not all causal variants are measured" which incorrectly read "in simulated data that randomly dropped I i = 0, 50, or 75% of all I p i = 10 SP 6 sp variants, including both causal ( I m i = 10 SP 4 sp ) and non-causal ( I p i - I m i = 990,000) SNPs (Fig. These authors contributed equally: Matt Jones, Matthew C. Keller. [Extracted from the article]

Across species, offspring of related individuals often exhibit significant reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data. We analyzed the phenomes and genomes of ∼350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, significant enrichment of ID within genomic regions with high recombination rates (>21-fold; p < 10−5), with conserved function across species (>19-fold; p < 10−4), and within regulatory elements such as DNase I hypersensitive sites (∼5-fold; p = 8.9 × 10−7). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome. Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases. [ABSTRACT FROM AUTHOR]