scholarly journals Bite force performance from wild derived mice has undetectable heritability despite having heritable morphological components

2021 ◽  
Author(s):  
Samuel Ginot ◽  
Benedikt Hallgrímsson ◽  
Sylvie Agret ◽  
Julien Claude
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Bite Force ◽  
Craniofacial Morphology ◽  
Mapping Hypothesis ◽  
Performance Traits ◽  
Heritable Change ◽  
And Function ◽  
Force Relationship

AbstractFitness-related traits tend to have low heritabilities. Conversely, morphology tends to be highly heritable. Yet, many fitness-related performance traits such as running speed or bite force depend critically on morphology. Craniofacial morphology correlates with bite performance in several groups including rodents. However, within species, this relationship is less clear, and the genetics of performance, morphology and function are rarely analyzed in combination. Here, we use a half-sib design in outbred wild-derived Mus musculus to study the morphology-bite force relationship and determine whether there is additive genetic (co-)variance for these traits. Results suggest that bite force has undetectable additive genetic variance and heritability in this sample, while morphological traits related mechanically to bite force exhibit varying levels of heritability. The most heritable traits include the length of the mandible which relates to bite force. Despite its correlation with morphology, realized bite force was not heritable, which suggests it is less responsive to selection in comparison to its morphological determinants. We explain this paradox with a non-additive, many-to-one mapping hypothesis of heritable change in complex traits. We furthermore propose that performance traits could evolve if pleiotropic relationships among the determining traits are modified.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2021 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E. Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Large Sample ◽  
Human Complex

scholarly journals Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1055 ◽  
Author(s):  
Ying Liu ◽  
Lei Xu ◽  
Zezhao Wang ◽  
Ling Xu ◽  
Yan Chen ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Association Studies ◽  
Additive Genetic Variance ◽  
Additive Models ◽  
Carcass Weight ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

scholarly journals Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits

PLoS Genetics ◽  
2005 ◽  
Vol preprint (2008) ◽  
pp. e8 ◽  
Author(s):  
William (Bill) WG Hill ◽  
Michael E. Goddard ◽  
Peter M. Visscher
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Theory Point

scholarly journals Narrow-sense heritability estimation of complex traits using identity-by-descent information

2017 ◽  
Author(s):  
Luke M. Evans ◽  
Rasool Tahmasbi ◽  
Matthew Jones ◽  
Scott I. Vrieze ◽  
Gonçalo R. Abecasis ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Fundamental Parameter ◽  
Last Common Ancestor ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Heritability Estimation ◽  
Causal Variants ◽  
Heritability Estimates

ABSTRACTHeritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly-tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). IBD-based heritability estimates were unbiased when using unrelated individuals, even for traits with extremely rare CVs, but stratification led to strong biases in IBD-based heritability estimates with poor precision. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied successfully to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in this real data that were not predicted from our simulation, suggesting that more work may be required to understand factors that influence IBD-based estimates.

scholarly journals Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber (Cucumis sativus L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Ce Liu ◽  
Xiaoxiao Liu ◽  
Yike Han ◽  
Xi'ao Wang ◽  
Yuanyuan Ding ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Variance Components ◽  
Genetic Variance ◽  
Cross Validation ◽  
Additive Genetic Variance ◽  
Inbred Lines ◽  
Cucumis Sativus L ◽  
The Cross ◽  
Two Populations

Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from −0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.

scholarly journals Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits

PLoS Genetics ◽  
2008 ◽  
Vol 4 (2) ◽  
pp. e1000008 ◽  
Author(s):  
William G. Hill ◽  
Michael E. Goddard ◽  
Peter M. Visscher
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Theory Point

scholarly journals Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

2021 ◽  
Author(s):  
Seema Yadav ◽  
Xianming Wei ◽  
Priya Joyce ◽  
Felicity Atkin ◽  
Emily Deomano ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Prediction Models ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Additive Effects ◽  
Genetic Progress ◽  
Accurate Identification ◽  
Additive Genetic Effects

AbstractKey messageNon-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.AbstractIn the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2020 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Sampling Variance ◽  
Analysis Model ◽  
Additive Variance ◽  
Snp Data ◽  
Causal Variants ◽  
The Uk ◽  
Epistatic Variance

AbstractNon-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.

scholarly journals Genomic and phenomic insights from an atlas of genetic effects on DNA methylation

2020 ◽  
Author(s):  
Josine L Min ◽  
Gibran Hemani ◽  
Eilis Hannon ◽  
Koen F Dekkers ◽  
Juan Castillo-Fernandez ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Variance ◽  
Genetic Factors ◽  
Additive Genetic Variance ◽  
Genetic Influences ◽  
Methylation Quantitative Trait Loci ◽  
Shared Genetic

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

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