So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

/pdf/the-ensembl-variant-effect-predictor-3d9pptyrw2.pdf

The Ensembl Variant Effect Predictor.

http://genomics.cimmyt.org/SAGPDB/Slides%20Paulino/2.%20Gmatrix/VanRaden.pdf

Efficient Methods to Compute Genomic Predictions

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Genetics and analysis of quantitative traits

Background
Genomic selection is particularly beneficial for difficult or expensive to measure traits. Since multi-trait selection is an important tool to deal with such cases, an important question is what the added value is of multi-trait genomic selection.

/pdf/estimating-genomic-breeding-values-and-detecting-qtl-using-2pz43viaen.pdf

Estimating genomic breeding values and detecting QTL using univariate and bivariate models.

Proceedings of the 8th World Congress of Genetics Applied to Livestock Production

Gene action underlying selection responses has been studied using crossbreeding. Maximum likelihood based segregation analysis has been presented for analysing backcross data for the presence of genes with a large effect. Two sets of divergently selected lines (P-lines for body weight and F-lines for fat content) were reciprocally crossed and the F 1 s were crossed to the high and low lines to produce all possible backcrosses. Earlier analysis had shown that the difference in body weight at 10 weeks ( n = 595) between the high and low P-lines was largely (75–80%) explained by autosomal, additive genes with the remainder explained by additive genes on the X chromosome. Maximum likelihood segregation analysis suggested the presence of a major effect on the X chromosome, but as there was only one round of recombination between the X chromosomes in the forming of the backcrosses, linked genes on the X chromosome could have acted together to give the appearance of a single major gene. The difference in fat content between the F-lines ( n = 578) could be explained by autosomal genes of largely additive effect. Segregation analysis suggested the presence of a major gene with complete dominance, but this was attributed to a relationship between the mean and the variance: transformation of the data resulted in only polygenic additive genes being of importance. This study concluded that maximum likelihood based analysis and crosses between selected lines provide a powerful means for studying the gene action underlying responses to selection.

The genetic basis of response in mouse lines divergently selected for body weight or fat content. II. The contribution of genes with a large effect.

Tail docking was a regular practice on sheep farms in the Netherlands. The main purpose was to reduce the accumulation of faeces and urine stain in order to prevent myiasis (Scobie and O' Connell, (2002)). As such, it is an example of a management practice which causes a temporary decrease in animal welfare in order to avoid larger problems later in life. Behavioural and endocrinological studies demonstrate pain caused by docking (Mellor and Murray, (1989a and 1989b), Kent et al. (1993); Graham et al. (1997)), and it is considered a unwanted mutilation by policymakers in the Netherlands. Therefore, there is an active policy to promote alternatives for such management practices. With better management i.e. cleaning tails and (preventive) medical treatment, myiasis can be prevented. Consequently tail docking is prohibited in The Netherlands since January 1 2008. Three English breeds (Suffolk, Clun Forest and Hampshire Down) have too long tails for efficient prevention of myiasis and are temporary exempted from this prohibition on the provision that a breeding programme is started to breed for shorter tails. Moderate to high heritabilities for tail length have been found for the Finnish Landrace with h2 = 0.77 (Branford Oltenacu and Boylan (1974)), for the Rambouillet h2 = 0.39 (Shelton (1977)) and for the Suffolk h2 = 0.41 (De Haas and Veerkamp (2004)). The objective of this study was to quantify genetic variation for tail length at birth for Clun Forest and Hampshire Down sheep in The Netherlands as a starting point for a breeding programme.

/pdf/genetic-selection-as-alternative-to-tail-docking-in-2si1zfskc6.pdf

Genetic Selection as Alternative to Tail Docking in Hampshire Down and Clun Forest

Energy balance is a function of dry-matter intake (DMI), live weight and milk yield over a certain time period. To investigate potential strategies to use genetic selection for the improvement of the negative energy balance, genetic co-variances were estimated among DMI, live weight and milk yield during the first 15 weeks of lactation (no.=628). Rather than estimating the full 45 by 45 matrix a random regression model was used to estimate a second order covariance functions for the additive genetic and permanent environmental effects. Fixed effects were test-day, a group effect and week in lactation. Estimates for the genetic covariance function demonstrated that a high level of milk yield is only moderately correlated with a high level of DMI (0.21) but very strongly correlated to an increase of intake (0.97) and a loss of live weight (-0.46) during the first 15 weeks of lactation. Levels of weight and intake were correlated strongly (0.81). Estimates for the genetic correlations between weeks 1 and 15 were 0.79, 0.34 and 0.83 for milk yield, DMI and live weight respectively. DMI during early lactation was negatively correlated with milk yield but DMI during the later weeks was positively correlated with milk yield. The implication is that when selection is for a linear combination of milk yield, DMI and live weight (i.e. energy balance or efficiency) the moment in lactation of measuring each trait on the cow is of importance

Roel F. Veerkamp

Papers

Estimating genomic breeding values and detecting QTL using univariate and bivariate models.

Proceedings of the 8th World Congress of Genetics Applied to Livestock Production

The genetic basis of response in mouse lines divergently selected for body weight or fat content. II. The contribution of genes with a large effect.

Genetic Selection as Alternative to Tail Docking in Hampshire Down and Clun Forest

Multi-trait covariance functions to estimate genetic correlations between milk yield, dry matter intake and live weight during lactation