Luc Janss

Application of Gibbs sampling for inference in a mixed major gene-polygenic inheritance model in animal populations

The application of Gibbs sampling is considered for inference in a mixed inheritance model in animal populations. Implementation of the Gibbs sampler on scalar components, as used for human populations, appeared not to be efficient, and an approach with blockwise sampling of genotypes was proposed for use in animal populations. The blockwise sampling of genotypes was proposed for use in animal populations. The blockwise sampling by which genotypes of a sire and its final progeny were sampled jointly was effective in improving mixing, although further improvements could be looked for. Posterior densities of parameters were visualised from Gibbs samples; from the former highly marginalised Bayesian point and interval estimates can be obtained.

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed “expression assisted selection,” and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed “expression-assisted evaluation.”

Heterogeneity of genetic modifiers ensures normal cardiac development

Background— Mutations of the transcription factor Nkx2-5 cause pleiotropic heart defects with incomplete penetrance. This variability suggests that additional factors can affect or prevent the mutant phenotype. We assess here the role of genetic modifiers and their interactions. Methods and Results— Heterozygous Nkx2-5 knockout mice in the inbred strain background C57Bl/6 frequently have atrial and ventricular septal defects. The incidences are substantially reduced in the Nkx2-5+/− progeny of first-generation (F1) outcrosses to the strains FVB/N or A/J. Defects recur in the second generation (F2) of the F1×F1 intercross or backcrosses to the parental strains. Analysis of >3000 Nkx2-5+/− hearts from 5 F2 crosses demonstrates the profound influence of genetic modifiers on disease presentation. On the basis of their incidences and coincidences, anatomically distinct malformations have shared and unique modifiers. All 3 strains carry susceptibility alleles at different loci for atrial and ventricular septal defects. Relative to the other 2 strains, A/J carries polymorphisms that confer greater susceptibility to atrial septal defect and atrioventricular septal defects and C57Bl/6 to muscular ventricular septal defects. Segregation analyses reveal that ≥2 loci influence membranous ventricular septal defect susceptibility, whereas ≥2 loci and at least 1 epistatic interaction affect muscular ventricular and atrial septal defects. Conclusions— Alleles of modifier genes can either buffer perturbations on cardiac development or direct the manifestation of a defect. In a genetically heterogeneous population, the predominant effect of modifier genes is health.

A comparison of multivariate genome-wide association methods

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (α = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak.

Inferences from Genomic Models in Stratified Populations

Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all markers simultaneously. Important objectives in WGRR studies are to estimate the proportion of variance accounted for by the markers, the effect of individual markers, prediction of genetic values for complex traits, and prediction of genetic risk of diseases. Proposals to account for stratification in this context are unsatisfactory. Here we address this problem and describe a reparameterization of a WGRR model, based on an eigenvalue decomposition, for simultaneous inference of parameters and unobserved population structure. This allows estimation of genomic parameters with and without inclusion of marker-derived eigenvectors that account for stratification. The method is illustrated with grain yield in wheat typed for 1279 genetic markers, and with height, HDL cholesterol and systolic blood pressure from the British 1958 cohort study typed for 1 million SNP genotypes. Both sets of data show signs of population structure but with different consequences on inferences. The method is compared to an advocated approach consisting of including eigenvectors as fixed-effect covariates in a WGRR model. We show that this approach, used in the context of WGRR models, is ill posed and illustrate the advantages of the proposed model. In summary, our method permits a unified approach to the study of population structure and inference of parameters, is computationally efficient, and is easy to implement.

Biochemical pathways analysis of microarray results: regulation of myogenesis in pigs

BackgroundCombining microarray results and biological pathway information will add insight into biological processes. Pathway information is widely available in databases through the internet.Mammalian muscle formation has been previously studied using microarray technology in pigs because these animals are an interesting animal model for muscle formation due to selection for increased muscle mass. Results indicated regulation of the expression of genes involved in proliferation and differentiation of myoblasts, and energy metabolism. The aim of the present study was to analyse microarrays studying myogenesis in pigs. It was necessary to develop methods to search biochemical pathways databases.ResultsPERL scripts were developed that used the names of the genes on the microarray to search databases. Synonyms of gene names were added to the list by searching the Gene Ontology database. The KEGG database was searched for pathway information using this updated gene list. The KEGG database returned 88 pathways. Most genes were found in a single pathway, but others were found in up to seven pathways. Combining the pathways and the microarray information 21 pathways showed sufficient information content for further analysis. These pathways were related to regulation of several steps in myogenesis and energy metabolism. Pathways regulating myoblast proliferation and muscle fibre formation were described. Furthermore, two networks of pathways describing the formation of the myoblast cytoskeleton and regulation of the energy metabolism during myogenesis were presented.ConclusionCombining microarray results and pathways information available through the internet provide biological insight in how the process of porcine myogenesis is regulated.

Estimation of direct and maternal genetic (co)variances for survival within litters of piglets

Abstract Variation in piglets weaned per sow per year is the main factor explaining differences in income between piglet producers. This parameter is the result of prolificacy of sows and survival rate of piglets. Data on 54500 piglets born between 1988 and 1994 at four nucleus breeding units of a pig breeding organization were used in an analysis of piglet survival. Animals were from two different dam lines. Piglet survival until weaning was considered and management was aimed at weaning at 28 days of age. Variance and covariance components were obtained using mixed models which included direct genetic and maternal genetic effects. In addition, the model included sex, breed, parity and herd-year-season as fixed effect, birth weight as covariable and sow as permanent environmental effect. Bayesian analysis, implemented using Gibbs sampling, was used to estimate the effects and parameters. Heritability of direct and maternal genetic effects was 0.11 (±0.01) and 0.09 (±0.01), respectively, in the full model. Genetic correlation between direct and maternal genetic effects was —0.56 (±0.06). Consequences of excluding maternal genetic effects or permanent environmental effects were studied. Based on the presented results it is concluded that simultaneous selection on maternal and direct genetic merit offers the opportunity to increase piglet survival.

The importance of haplotype length and heritability using genomic selection in dairy cattle.

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites (h2) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h2 = 0.02 to 0.93 for h2 = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h2 = 0.02 to 0.86 for h2 = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.

Comparison of association mapping methods in a complex pedigreed population

Association mapping methods were compared using a simulation with a complex pedigree structure. The pedigree was simulated while keeping the present Danish Holstein population pedigree in view. A total of 15 quantitative trait loci (QTL) with varying effect sizes (10%, 5% and 2% of total genetic variance) were simulated. We compared the single‐marker test, haplotype‐based analysis, mixed model approach, and Bayesian analysis. The methods were compared for power, precision of location estimates, and type I error rates. Results found the best performance in a Bayesian method that included genetic background effects and simultaneously fitted all single‐nucleotide polymorphisms (SNPs) with a variable selection method. A mixed model analysis that fitted genetic background effects and tested one SNP at a time performed nearly as well as the Bayesian method. For the Bayesian method, it proved necessary to collect SNP signals in intervals, to avoid the scattering of a QTL signal over multiple neighboring SNPs. Methods not accounting for genetic background (full pedigree information) performed worse, and methods using haplotypes were considerably worse with a high false‐positive rate, probably due to the presence of low‐frequency haplotypes. It was necessary to account for full relationships among individuals to avoid excess false discovery. Although the methods were tested on a cattle pedigree, the results are applicable to any population with a complex pedigree structure. Genet. Epidemiol. 34: 455–462, 2010.

Bivariate analysis for one continuous and one threshold dichotomous trait with unequal design matrices and an application to birth weight and calving difficulty

Abstract A bivariate analysis is described for one continuous and one discrete trait to estimate sire effects in a progeny test. Unequal design matrices, in terms of both missing data as well as different fixed effects are allowed for. Three independent sub data sets are formed, for which the corresponding log-likelihoods are expressed. The parameters are estimated as their maxima a posteriori by maximizing the log-posterior density using a Newton Raphson algorithm; equations are given in a mixed model form. A simulation study is presented showing the benefits of this bivariate analysis for the evaluation of beef bulls for calving difficulty, using birth weight as a correlated trait. Standard errors obtained from the information matrix were close to empirical standard errors, and it was shown that bivariate analysis with unequal design matrices can correct for a bias which may occur when birth weight records are not missing at random.