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

Genomic prediction for new traits combining cow and bull reference populations

Gedurende de lactatie verloopt het celgetal volgens een bepaalde curve Afwijkingen daarvan kunnen veroorzaakt worden door mastitis S aureus, E coli en S uberis zijn van elkaar te onderscheiden door specifieke piekpatronen

/pdf/pieken-in-het-celgetal-mastitisverwekker-te-herkennen-aan-4pduqcjw5g.pdf

Pieken in het celgetal: Mastitisverwekker te herkennen aan specifiek piekpatroon

Selection for feed efficiency is of increasing interest for cattle breeders, and countries are combining data to develop genomic breeding values to be used across feeding systems. Also a classical question is if we should breed cows with a higher feed intake (capacity) so they can convert more roughage into milk, or should we breed cows that are efficient and have a lower feed intake for a given yield, i.e. that are more profitable (milk yield feed intake). To answer this question genotype by environment (GxE) was investigated. Experiments with a total of 1,602 cows recorded for dry matter intake (DMI), fat-protein corrected milk (FPCM) and liveweight) (LW) were grouped into a high, medium and low nutritional environment to estimate genetic parameters. Heritability for DMI and LW were constant across the environments, for milk yield there was a decrease in h2 from high to low environment. Genetic correlations between the environments ranged between 0.68 to 0.90 for DMI, and were slightly lower for FPCM, and higher for LW. Selection index calculations demonstrated that selection for higher intake gave heavier cows, and when selecting for a higher intake relatively to milk yield on a higher density diet there was no benefit in terms of profit on lower density diets. Therefore, the breeding goals should always be for profit (FPCM – DMI) independent of the nutritional environment.

/pdf/selection-for-low-or-high-feed-intake-cows-genotype-by-dsiexn7n3j.pdf

Selection for low or high feed intake cows: genotype by environment interaction for milk yield, live weight and dry matter intake in dairy cows

Falling prices and increasing capacity of DNA sequencing technologies create opportunities to use whole-genome sequence data for the prediction of genetic values of individuals for quantitative traits. The accuracy of these genomic predictions is important in plant and animal breeding. The polymorphisms (called SNPs here for simplicity) causal to the genetic differences between the individuals are among those being analyzed for whole-genome sequence data. In Bayesian variable selection methods different prior distributions are assigned to the set of (causal) SNPs with moderate to large effects and to the set of SNPs with virtually no effect on the prediction of the trait. Alternatively, regression methods exploiting a genomic relationship matrix omit the distinction between causal and non-causal SNPs by weighting each SNP equally. Here, the Bayesian Stochastic Search Variable Selection (BayesSSVS) and the Genome-enabled Best Linear Unbiased Prediction (GBLUP) were used, respectively. It was anticipated that BayesSSVS outperforms G-BLUP as the latter does not take full advantage of sequence data.

Roel F. Veerkamp

Papers

Genomic prediction for new traits combining cow and bull reference populations

Pieken in het celgetal: Mastitisverwekker te herkennen aan specifiek piekpatroon

Selection for low or high feed intake cows: genotype by environment interaction for milk yield, live weight and dry matter intake in dairy cows

Added value of whole-genome sequence data to genomic predictions in dairy cattle

Signatures of selection in Holstein Friesian dairy cattle