Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

Abstract Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

Download Full-text

On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge

Genetics ◽

10.1093/genetics/163.1.347 ◽

2003 ◽

Vol 163 (1) ◽

pp. 347-365 ◽

Cited By ~ 3

Author(s):

Daniel Gianola ◽

Miguel Perez-Enciso ◽

Miguel A Toro

Keyword(s):

Genetic Variability ◽

Multilevel Models ◽

Selection Index ◽

Spatial Covariance ◽

Linear Unbiased Prediction ◽

Lorenz Curves ◽

Gini Coefficients ◽

Best Linear Unbiased ◽

Genetic Value ◽

Dense Marker

Abstract Marked-assisted genetic improvement of agricultural species exploits statistical dependencies in the joint distribution of marker genotypes and quantitative traits. An issue is how molecular (e.g., dense marker maps) and phenotypic information (e.g., some measure of yield in plants) is to be used for predicting the genetic value of candidates for selection. Multiple regression, selection index techniques, best linear unbiased prediction, and ridge regression of phenotypes on marker genotypes have been suggested, as well as more elaborate methods. Here, phenotype-marker associations are modeled hierarchically via multilevel models including chromosomal effects, a spatial covariance of marked effects within chromosomes, background genetic variability, and family heterogeneity. Lorenz curves and Gini coefficients are suggested for assessing the inequality of the contribution of different marked effects to genetic variability. Classical and Bayesian methods are presented. The Bayesian approach includes a Markov chain Monte Carlo implementation. The generality and flexibility of the Bayesian method is illustrated when a Lorenz curve is to be inferred.

Download Full-text

Comparative transcriptome analysis of mule uncovered several heterosis-related genes for improving muscular endurance and cognition

10.1101/823047 ◽

2019 ◽

Author(s):

Shan Gao

Keyword(s):

Alternative Splicing ◽

Molecular Genetic ◽

Hybrid Vigor ◽

Neuronal System ◽

Future Studies ◽

Genome Wide ◽

A Genome ◽

Genetic Mechanisms ◽

Productive Traits ◽

Genome Wide Gene Expression

AbstractHeterosis has been widely exploited in animal and plant breeding to enhance the productive traits of hybrid progeny of two breeds or two species. Although, there were multiple models for explaining the hybrid vigor, such as dominance and over-dominance hypothesis, its underlying molecular genetic mechanisms remain equivocal. The aim of this study is through comparing the different expression genes (DEGs) and different alternative splicing (DAS) genes to explore the mechanism of heterosis. Here, we performed a genome-wide gene expression and alternative splicing analysis of two heterotic crosses between donkey and horse in three tissues. The results showed that the DAS genes influenced the heterosis-related phenotypes in a unique than DEGs and about 10% DEGs are DAS genes. In addition, over 69.7% DEGs and 87.2% DAS genes showed over-dominance or dominance, respectively. Furthermore, the “Muscle Contraction” and “Neuronal System” pathways were significantly enriched both for the DEGs and DAS genes in muscle. TNNC2 and RYR1 genes may contribute to mule’s great endurance while GRIA2 and GRIN1 genes may be related with mule’s cognition. Together, these DEGs and DAS genes provide the candidates for future studies of the genetic and molecular mechanism of heterosis in mule.

Download Full-text

Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize

Genetics ◽

10.1534/genetics.120.303278 ◽

2020 ◽

Vol 216 (1) ◽

pp. 27-41

Author(s):

Simon Rio ◽

Laurence Moreau ◽

Alain Charcosset ◽

Tristan Mary-Huard

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Prediction Models ◽

Best Linear Unbiased Prediction ◽

Linear Unbiased Prediction ◽

Modeling Group ◽

A Genome ◽

Specific Allele ◽

Best Linear Unbiased ◽

Unbiased Prediction

Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.

Download Full-text

Population Genomic Evidence Reveals Subtle Patterns of Differentiation in the Trophically Polymorphic Cuatro Ciénegas Cichlid, Herichthys minckleyi

Journal of Heredity ◽

10.1093/jhered/esz004 ◽

2019 ◽

Vol 110 (3) ◽

pp. 361-369 ◽

Cited By ~ 1

Author(s):

Katherine L Bell ◽

Chris C Nice ◽

Darrin Hulsey

Keyword(s):

Gene Flow ◽

Biological Diversity ◽

Molecular Genetic ◽

Molecular Ecology ◽

Cichlid Fish ◽

Genome Wide ◽

A Genome ◽

The Face ◽

Or Gene ◽

Herichthys Minckleyi

Abstract In recent decades, an increased understanding of molecular ecology has led to a reinterpretation of the role of gene flow during the evolution of reproductive isolation and biological novelty. For example, even in the face of ongoing gene flow strong selection may maintain divergent polymorphisms, or gene flow may introduce novel biological diversity via hybridization and introgression from a divergent species. Herein, we elucidate the evolutionary history and genomic basis of a trophically polymorphic trait in a species of cichlid fish, Herichthys minckleyi. We explored genetic variation at 3 hierarchical levels; between H. minckleyi (n = 69) and a closely related species Herichthys cyanoguttatus (n = 10), between H. minckleyi individuals from 2 geographic locations, and finally between individuals with alternate morphotypes at both a genome-wide and locus-specific scale. We found limited support for the hypothesis that the H. minckleyi polymorphism is the result of ongoing hybridization between the 2 species. Within H. minckleyi we found evidence of geographic genetic structure, and using traditional population genetic analyses found that individuals of alternate morphotypes within a pool appear to be panmictic. However, when we used a locus-specific approach to examine the relationship between multi-locus genotype, tooth size, and geographic sampling, we found the first evidence for molecular genetic differences between the H. minckleyi morphotypes.

Download Full-text

Familial heterogeneity in breast cancer predisposition: a study of 22 Utah families

10.1101/092841 ◽

2016 ◽

Author(s):

Elizabeth O’Brien ◽

Richard A. Kerber ◽

Raymond L. White

Keyword(s):

Breast Cancer ◽

Rare Variants ◽

Disease Risk ◽

Breast Cancer Predisposition ◽

Large Numbers ◽

Genome Wide ◽

A Genome ◽

Medium Risk ◽

Genomic Regions ◽

Marker Spacing

AbstractThe problem of “missing heritability” in genome-wide analyses of complex diseases is thought to be attributable to some combination of: rare variants of moderate to large effect, common variants of very small effect, and epigenetic, epistatic, or shared environmental effects. Rare variants do not affect large numbers of people by definition, but identified genes and pathways frequently lead to important insights into pathogenesis, and become targets of chemoprevention or therapy. Family studies remain an efficient way to identify rare variants with sizable effects on disease risk. We present a genome-wide study of breast cancer in 22 large high-risk families including 154 women diagnosed with breast cancer. Appropriate marker spacing was achieved by simulation studies of founder haplotypes to reduce the chance that linkage disequilibrium produced spurious linkage peaks. For each family, we generated 100 simulations of null linkage genome-wide to estimate the probability that individual results were due to chance. We identified a total of 12 putative susceptibility regions with per-family genome-wide probability < 0.05. These regions were located on 10 chromosomes; 10 of the 22 families showed linkage at these locations; two or more families showed linkage to 6 regions on 5 chromosomes (4q, 5q, 6p, 14q, 18p, and 18q). These results indicate that there is considerable heterogeneity among families in genomic regions and thus variants predisposing to breast cancer. Moreover, they suggest that uncommon high– or medium-risk genetic variants remain to be found, and that family designs can be an efficient way to identify them.

Download Full-text

Genome-wide Hierarchical Mixed Model Association Analysis

10.21203/rs.3.rs-315869/v1 ◽

2021 ◽

Author(s):

Runqing Yang ◽

Di Liu ◽

Zhiyu Hao ◽

Yuxin Song ◽

Runqing Yang ◽

...

Keyword(s):

Association Analysis ◽

Statistical Power ◽

Mixed Model ◽

False Negative ◽

Best Linear Unbiased Prediction ◽

Least Square ◽

Linear Unbiased Prediction ◽

Genomic Breeding ◽

Genome Wide ◽

Best Linear Unbiased

Abstract We partitioned the genomic mixed model into two hierarchies to firstly estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and then statistically infer the association of GBVs with each SNP using the generalized least square. The genome-wide hierarchical mixed model association study (named Hi-LMM) can correct effectively confounders with polygenic effects as residuals in association tests, preventing potential false negative errors produced with GRAMMAR or EMMAX. The Hi-LMM performs the same statistical power as the exact FaST-LMM with the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, Hi-LMM outperforms existing methods in statistical power, especially through joint association analysis.

Download Full-text

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence

Molecular Psychiatry ◽

10.1038/mp.2015.108 ◽

2015 ◽

Vol 21 (8) ◽

pp. 1145-1151 ◽

Cited By ~ 15

Author(s):

S L Spain ◽

I Pedroso ◽

N Kadeva ◽

M B Miller ◽

W G Iacono ◽

...

Keyword(s):

Population Distribution ◽

Genetic Model ◽

Molecular Genetic ◽

Heritability Estimate ◽

Population Based ◽

Functional Protein ◽

Genome Wide ◽

A Genome ◽

Rare Alleles ◽

High Intelligence

Abstract Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case–control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence.

Download Full-text

Heritability and molecular genetic basis of antisaccade eye tracking error rate: A genome-wide association study

Psychophysiology ◽

10.1111/psyp.12347 ◽

2014 ◽

Vol 51 (12) ◽

pp. 1272-1284 ◽

Cited By ~ 18

Author(s):

Uma Vaidyanathan ◽

Stephen M. Malone ◽

Jennifer M. Donnelly ◽

Micah A. Hammer ◽

Michael B. Miller ◽

...

Keyword(s):

Association Study ◽

Error Rate ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Molecular Genetic ◽

Tracking Error ◽

Genome Wide Association ◽

Genome Wide ◽

Molecular Genetic Basis ◽

A Genome

Download Full-text

Heritability and molecular-genetic basis of resting EEG activity: A genome-wide association study

Psychophysiology ◽

10.1111/psyp.12344 ◽

2014 ◽

Vol 51 (12) ◽

pp. 1225-1245 ◽

Cited By ~ 36

Author(s):

Stephen M. Malone ◽

Scott J. Burwell ◽

Uma Vaidyanathan ◽

Michael B. Miller ◽

Matt MCGUE ◽

...

Keyword(s):

Association Study ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Molecular Genetic ◽

Genome Wide Association ◽

Eeg Activity ◽

Genome Wide ◽

Molecular Genetic Basis ◽

A Genome

Download Full-text

Estimates of Parental Best Linear Unbiased Predicted (BLUP) Additive Genetic Values for Oil Yield in an Indonesian Oil Palm Breeding Programme

Zuriat ◽

10.24198/zuriat.v15i1.6819 ◽

2015 ◽

Vol 15 (1) ◽

Author(s):

A. R. Purba ◽

A. Flori ◽

L. Baudouin ◽

P. Amblard ◽

S. Hamon

Keyword(s):

Oil Palm ◽

Oil Yield ◽

Breeding Programme ◽

Data Set ◽

Linear Unbiased Prediction ◽

Yield Trait ◽

African Populations ◽

Additive Gene ◽

Best Linear Unbiased ◽

Genetic Value

The additive genetic value for oil yield of 135 parents from several African populations tested in the first cycle of Indonesian Oil Palm Research Institute (IOPRI) breeding programme were estimated by best linear unbiased prediction (BLUP) using an unbalanced data set. These values were used to evaluate the possibility of reduction in generation selection time in an oil palm breeding programme. The ranks of parental additive genetic values obtained with early yield period and whole cycle was found to be consistent. These make that highly potential parents could be selected and recombined at a more precocious time in order to reduce the period between cycle. Since oil yield trait is mainly controlled under additive gene effect, the recombination should be done carefully for retaining as highly as possible this character in a parent to be improved.

Download Full-text