The efficiency of multi-generation selection on maternal traits, with implications for reindeer

Maternally affected traits, such as juvenile growth and survival, provide resilience in mammal species, in particular for reindeer living in extreme northern habitat. The genetic variation in such traits is caused by direct and maternal genetic effects (DGE and MGE, respectively). We used Willham’s variance-component approach and extended a family index with the focal individual and its full- and half-sibs to an approximated BLUP (pseudo-BLUP) by including the parents’ estimated breeding values. Most of the deviations of the predicted responses from the simulated ones were 4.1% for DGE and 5.3% for MGE. The benefits of index and BLUP selection are high in the case of negative correlation, large full-sib family and in particular, when maternal half-sibs are available. Higher economic value for MGE than for DGE is needed, since with equal heritabilities and economic weights for the effects the maternal response is 40 to 70% of the direct one. With negative correlation, records on collateral relatives beyond sibs are possibly needed. They would support also the prediction of MGE in uniparous reindeer lacking full-sib information.

Simple deterministic identity-by-descent coefficients and estimation of QTL allelic effects in full and half sibs

Accurate and rapid methods for the detection of quantitative trait loci (QTLs) and evaluation of consequent allelic effects are required to implement marker-assisted selection in outbred populations. In this study, we present a simple deterministic method for estimating identity-by-descent (IBD) coefficients in full- and half-sib families that can be used for the detection of QTLs via a variance-component approach. In a simulated dataset, IBD coefficients among sibs estimated by the simple deterministic and Markov chain Monte Carlo (MCMC) methods with three or four alleles at each marker locus exhibited a correlation of greater than 0·99. This high correlation was also found in QTL analyses of data from an outbred pig population. Variance component analysis used both the simple deterministic and MCMC methods to estimate IBD coefficients. Both procedures detected a QTL at the same position and gave similar test statistics and heritabilities. The MCMC method, however, required much longer computation than the simple method. The conversion of estimated QTL genotypic effects into allelic effects for use in marker-assisted selection is also demonstrated.

A quantitative trait locus influencing estrogen levels maps to a region homologous to human chromosome 20

Estrogen, a steroid hormone, regulates reproduction and has been implicated in several diseases. We performed a genome-wide scan using multipoint linkage analysis implemented in a general pedigree-based variance component approach to identify genes with measurable effects on variation in estrogen levels in baboons. A microsatellite polymorphism, D20S171, located on human chromosome 20q13.11, showed strong evidence of linkage with a LOD score of 3.06 ( P = 0.00009). This region contains several potential candidate genes including melanocortin 3 receptor ( MC3R), cytochrome P-450 subfamily XXIV ( CYP24), and breast carcinoma amplified sequence ( BCAS1). This is the first evidence of a quantitative trait locus with a significant effect on estrogen.

Mapping Quantitative Trait Loci in Complex Pedigrees: A Two-Step Variance Component Approach

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.