Additive Polygenic Effects Research Articles

Characterization of OCA2 cDNA in different porcine breeds and analysis of its potential effect on skin pigmentation in a red Iberian strain

Although the function of the OCA2 gene product has not been totally clarified, variation in OCA2 has been associated with skin and hair pigmentation in human and mouse. However, its contribution to skin colour in domestic species has not been reported. In this study, cDNA and intron 9 sequences of the porcine OCA2 gene have been characterized in several pig populations. The cDNA sequence alignment of 20 animals from eight porcine populations allowed the identification of 10 single nucleotide polymorphisms (SNPs); five of the 10 SNPs were non-synonymous. The intron 9 sequence alignment of 12 animals belonging to four pig populations revealed four additional SNPs. Skin colour variation was analysed in a red strain of Iberian pigs with segregation of three SNPs forming two OCA2 intragenic haplotypes. Results from this study provide evidence of a suggestive dominant effect of haplotypes on colour intensity and indicate an important contribution of additive polygenic effects (h2 = 0.56 +/- 0.21) to the variance of this trait.

QTL mapping for teat number in an Iberian‐by‐Meishan pig intercross

The aim of this study was to investigate chromosomal regions affecting the number of teats in pigs and possible epistatic interactions between the identified quantitative trait loci (QTL). An experimental F2 cross between Iberian and Chinese Meishan lines was used for this purpose. A genomic scan was conducted with 117 markers covering the 18 porcine autosomes. Linkage analyses were performed by interval mapping using an animal model to estimate QTL and additive polygenic effects. Complementary analyses with models fitting two QTL were also carried out. The results showed three genomewide significant QTL mapping on chromosomes 5, 10 and 12, whose joint action control up to 30% of the phenotypic variance of the trait. Meishan alleles had a positive additive effect on teat number, and a positive-additive x additive-epistatic interaction was detected between QTL on chromosomes 10 and 12.

A blocking Gibbs sampling method to detect major genes with phenotypic data from a diallel mating.

Diallel mating is a frequently used design for estimating the additive and dominance genetic (polygenic) effects involved in quantitative traits observed in the half- and full-sib progenies generated in plant breeding programmes. Gibbs sampling has been used for making statistical inferences for a mixed-inheritance model (MIM) that includes both major genes and polygenes. However, using this approach it has not been possible to incorporate the genetic properties of major genes with the additive and dominance polygenic effects in a diallel mating population. A parent block Gibbs sampling method was developed in this study to make statistical inferences about the major gene and polygenic effects on quantitative traits for progenies derived from a half-diallel mating design. Using simulated data sets with different major and polygenic effects, the proposed method accurately estimated the major and polygenic effects of quantitative traits, and possible genotypes of parents and progenies. The impact of specifying different prior distributions was examined and was found to have little effect on inference on the posterior distribution. This approach was applied to an experimental data set of Loblolly pine (Pinus taeda L.) derived from a 6-parent half-diallel mating. The result indicated that there might be a recessive major gene affecting height growth in this diallel population.

Longitudinal variance components models for systolic blood pressure, fitted using Gibbs sampling.

This paper describes an analysis of systolic blood pressure (SBP) in the Genetic Analysis Workshop 13 (GAW13) simulated data. The main aim was to assess evidence for both general and specific genetic effects on the baseline blood pressure and on the rate of change (slope) of blood pressure with time. Generalized linear mixed models were fitted using Gibbs sampling in WinBUGS, and the additive polygenic random effects estimated using these models were then used as continuous phenotypes in a variance components linkage analysis. The first-stage analysis provided evidence for general genetic effects on both the baseline and slope of blood pressure, and the linkage analysis found evidence of several genes, again for both baseline and slope.

Using the mixed model for interval mapping of quantitative trait loci in outbred line crosses.

Interval mapping by simple regression is a powerful method for the detection of quantitative trait loci (QTLs) in line crosses such as F2 populations. Due to the ease of computation of the regression approach, relatively complex models with multiple fixed effects, interactions between QTLs or between QTLs and fixed effects can easily be accommodated. However, polygenic effects, which are not targeted in QTL analysis, cannot be treated as random effects in a least squares analysis. In a cross between true inbred lines this is of no consequence, as the polygenic effect contributes just to the residual variance. In a cross between outbred lines, however, if a trait has high polygenic heritability, the additive polygenic effect has a large influence on variation in the population. Here we extend the fixed model for the regression interval mapping method to a mixed model using an animal model. This makes it possible to use not only the observations from progeny (e.g. F2), but also those from the parents (F1) to evaluate QTLs and polygenic effects. We show how the animal model using parental observations can be applied to an outbred cross and so increase the power and accuracy of QTL analysis. Three estimation methods, i.e. regression and an animal model either with or without parental observations, are applied to simulated data. The animal model using parental observations is shown to have advantages in estimating QTL position and additive genotypic value, especially when the polygenic heritability is large and the number of progeny per parent is small.

Characterization of the genetic elements controlling lipoprotein(a) concentrations in Mexican Americans. Evidence for at least three controlling elements linked to LPA, the locus encoding apolipoprotein(a)

Analyses of 1163 samples from the San Antonio Family Heart Study revealed several elements of genetic control of lipoprotein(a) (Lp(a)) concentrations in Mexican Americans. Apolipoprotein(a) (apo(a)) isoform size variation was inversely related to Lp(a) concentrations and explained about 22% of total phenotypic variation. Segregation analyses suggested the existence of a major gene that influenced an additional 41% of total Lp(a) variation. A G→A polymorphism in the LPA promoter was in strong disequilibrium with apo(a) isoform size, but did not contribute a significant amount of additional information about Lp(a) variation. However, about 25% of variation in Lp(a) concentrations was influenced by additive polygenic effects, which include the effects of null phenotype alleles. Altogether, these genetic components explained 89% of Lp(a) variation, similar to heritability estimates made in several other studies. Apo(a) size variation and the major gene (explaining a total of about 62% of Lp(a) variation) were linked to each other and, as expected, to the plasminogen locus. Thus, together with the well-established null phenotype allele, these different genetic factors represent at least three distinct elements of control exerted at the LPA locus, which encodes the apo(a) protein.

Statistical techniques for detection of major genes in animal breeding data

Statistical techniques for detection of major loci and for making inferences about major locus parameters such as genotypic frequencies, effects and gene action from field-collected data are presented. In field data, major genotypic effects are likely to be masked by a large number of environmental differences in addition to additive and nonadditive polygenic effects. A graphical technique and a procedure for discriminating among genetic hypotheses based on a mixed model accounting for all these factors are proposed. The methods are illustrated by using simulated data.