The evolution of the additive variance of a trait under stabilizing selection after autopolyploidization

Whole‐genome duplication is a common mutation in eukaryotes with far‐reaching phenotypic effects. The resulting morphological, physiological and fitness consequences and how they affect the survival probability of polyploid lineages are intensively studied, but little is known about the effect of genome doubling on the evolutionary potential of populations. Historically, it has been argued polyploids should be less able to adapt because gene duplication dilutes the effects of alleles, such that polyploids are less likely to evolve new adaptive gene complexes compared with diploids. In this paper, I investigate the short‐ and long‐term consequences of genome doubling on the additive genetic variance of populations. To do so, I extended the classical models of quantitative traits under stabilizing selection to study the evolution of the additive variance of the trait under study after a shift from diploidy to tetraploidy. I found that, for realistic allele‐dosage effects, polyploidization is associated with an initial decrease in adaptive potential. In the long term, the better masking of recessive deleterious mutations associated with polyploidy compensates for the initial decrease in additive variance. The time for the tetraploid populations to reach or exceed the additive variance of their diploid progenitors is generally lower than 200 generations. These results highlight that polyploidization per se has a negligible negative effect on the adaptive potential of populations in the short term, and a substantial positive effect in the long term.

The Origin of Additive Genetic Variance Driven by Positive Selection

Fisher's fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability (h2) for traits important to fitness. However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity (e.g., humans), the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher's fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.

Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs

Simple Summary

The response to genetic selection in animal populations depends on both additive and nonadditive (e.g., dominance) effects. Neglecting nonadditive effects in genetic evaluations, when they are relevant, may lead to an overestimation of the genetic progress achievable. Our study evidenced that dominance effects influence the prediction of the total genetic progress achievable in heavy pigs, for growth, carcass, fresh ham and dry-cured ham seasoning traits, and indicated that neglecting nonadditive effects leads to an overestimation of the additive genetic variance. However, goodness of fit and ranking of breeding candidates obtained by models including litter and dominance effects simultaneously were not different from those obtained by models including only litter effects. Consequently, accounting for litter effects in the models for genetic evaluations, even when neglecting dominance effects, would be sufficient to prevent possible consequences arising from the overestimation of the genetic variance, with no repercussions on the ranking of animals and on accuracy of breeding values, ensuring at the same time computational efficiency.

Abstract

Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litter effects, but no effects on animal ranking and accuracy of breeding values were detected. Accounting for litter effects in the models for genetic evaluations would be sufficient to prevent the overestimation of the genetic variance while ensuring computational efficiency.

Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk

High blood cholesterol concentration, mainly caused by high dietary cholesterol, is a potential risk factor for human health. Dairy products are important sources of human dietary cholesterol intake. Therefore, monitoring bovine milk cholesterol concentration is important for human health benefit. Genetic selection for improvement of cow milk cholesterol content requires understanding of the genetics of milk cholesterol. For this purpose, we performed analyses of additive and dominance effects of 126 potentially functional SNPs within 43 candidate genes with milk cholesterol content [expressed as mg of cholesterol in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk)]. The additive and dominance effects of SNPs rs380643365 in AGPAT1 (P = 0.04) and rs134357240 in SOAT1 (P = 0.035) genes associated significantly with CHL_fat. Moreover, five (rs109326954 and rs523413537 in DGAT1, rs109376747 in LDLR, rs42781651 in FAM198B and rs109967779 in ACAT2) and four (rs137347384 in RBM19, rs109376747 in LDLR, rs42016945 in PPARG and rs110862179 in SCAP) SNPs were significantly associated with CHL_milk (P < 0.05) based on additive and dominance effect analyses respectively. Rs109326954 and rs523413537 in DGAT1 explained a considerable portion of the phenotypic variance of CHL_milk (7.54 and 6.84% respectively), and might be useful in selection programs for reduced milk cholesterol content. Several significantly associated SNPs were in genes (such as ACAT2 and LDLR) involved in cholesterol metabolism in the liver or cholesterol transport, suggesting multiple mechanisms regulating milk cholesterol content. Nine and seven SNPs identified by additive or dominance effect analyses associated significantly with milk yield and fat yield respectively. Further analyses are required to better understand the consequences of these variants and their potential use in genomic selection of the studied traits.

Decomposing Additive Genetic Variance Revealed Novel Insights into Trait Evolution in Synthetic Hexaploid Wheat

Whole genome duplication (WGD) is an evolutionary phenomenon, which causes significant changes to genomic structure and trait architecture. In recent years, a number of studies decomposed the additive genetic variance explained by different sets of variants. However, they investigated diploid populations only and none of the studies examined any polyploid organism. In this research, we extended the application of this approach to polyploids, to differentiate the additive variance explained by the three subgenomes and seven sets of homoeologous chromosomes in synthetic allohexaploid wheat (SHW) to gain a better understanding of trait evolution after WGD. Our SHW population was generated by crossing improved durum parents (Triticum turgidum; 2n = 4x = 28, AABB subgenomes) with the progenitor species Aegilops tauschii (syn Ae. squarrosa, T. tauschii; 2n = 2x = 14, DD subgenome). The population was phenotyped for 10 fungal/nematode resistance traits as well as two abiotic stresses. We showed that the wild D subgenome dominated the additive effect and this dominance affected the A more than the B subgenome. We provide evidence that this dominance was not inflated by population structure, relatedness among individuals or by longer linkage disequilibrium blocks observed in the D subgenome within the population used for this study. The cumulative size of the three homoeologs of the seven chromosomal groups showed a weak but significant positive correlation with their cumulative explained additive variance. Furthermore, an average of 69% for each chromosomal group's cumulative additive variance came from one homoeolog that had the highest explained variance within the group across all 12 traits. We hypothesize that structural and functional changes during diploidization may explain chromosomal group relations as allopolyploids keep balanced dosage for many genes. Our results contribute to a better understanding of trait evolution mechanisms in polyploidy, which will facilitate the effective utilization of wheat wild relatives in breeding.

Influence of gene interaction on complex trait variation with multilocus models

Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, per se, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the heterozygosity, for models where frequencies are widely dispersed, such as for selectively neutral mutations, contributions of epistatic variance are always small. Epistasis may be important in understanding the genetic architecture, for example, of function or human disease, but that does not imply that loci exhibiting it will contribute much genetic variance. Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution.

Estimation and partition of heritability in human populations using whole-genome analysis methods

Understanding genetic variation of complex traits in human populations has moved from the quantification of the resemblance between close relatives to the dissection of genetic variation into the contributions of individual genomic loci. However, major questions remain unanswered: How much phenotypic variation is genetic; how much of the genetic variation is additive and can be explained by fitting all genetic variants simultaneously in one model, and what is the joint distribution of effect size and allele frequency at causal variants? We review and compare three whole-genome analysis methods that use mixed linear models (MLMs) to estimate genetic variation. In all methods, genetic variation is estimated from the relationship between close or distant relatives on the basis of pedigree information and/or single nucleotide polymorphisms (SNPs). We discuss theory, estimation procedures, bias, and precision of each method and review recent advances in the dissection of genetic variation of complex traits in human populations. By using genome-wide data, it is now established that SNPs in total account for far more of the genetic variation than the statistically highly significant SNPs that have been detected in genome-wide association studies. All SNPs together, however, do not account for all of the genetic variance estimated by pedigree-based methods. We explain possible reasons for this remaining "missing heritability."