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

The aim of this study was to investigate genetic gain in dairy cattle breeding programs using international genetic evaluations. Selection of bulls and cows across countries on average EBV, combining EBV of different countries into one average EBV (= joint breeding goal), was compared with selection of bulls and cows across countries on national EBV. Two domestic dairy cattle breeding programs in two different countries were simulated using a deterministic model approximating BLUPselection. Selection on average EBV yielded a higher genetic gain than selection on national EBV, when the genetic correlation was higher than 0.70. Benefits of selection across countries were at maximum an increase in genetic gain of 15%, when the genetic correlation was 1.00. Selection on national EBV was reduced to within-country selection, when the genetic correlation was 0.90 or lower. Trait and breeding goal definition should be harmonized as much as possible to increase genetic gain by selection of bulls and cows across countries. Development of (sub)global rankings (= joint breeding goal, e.g. average EBV) would increase benefits of selection of bulls and cows across countries.

https://journal.interbull.org/index.php/ib/article/download/886/877

Optimizing dairy cattle breeding programs using international genetic evaluations

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated-measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd-scaling factors, which were calculated by accounting for heterogeneity of phenotypic within-herd variances. Models were selected based on cross-validation and prediction accuracy results. Scaling factors were re-estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree-based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re-estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97.

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle.

Method for estimating a breeding value for a first organism without a known phenotype, comprising estimating a plurality of reference breeding values for a plurality of haplotypes of a plurality of second organisms, wherein a phenotype and a genotype of each of the second organisms of the plurality of second organisms are at least partially known, storing the plurality of estimated reference breeding values for the plurality of haplotypes, and estimating the breeding value for the first organism on the basis of at least a part of a genotype of the first organism and the stored plurality of estimated reference breeding values for the plurality of haplotypes.

Method for estimating a breeding value for an organism without a known phenotype

Heritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle

In dit rapport worden de resultaten beschreven van eenonderzoek naar de visie op fokkerij binnen de biologische landbouw in Nederland. Het doel van dit onderzoek was om, door middel van interviews en discussies met biologische veehouders en maatschappelijke organisaties, te komen tot een duidelijke visie op de fokkerij in de biologische landbouw.

/pdf/visie-op-fokkerij-voor-de-biologische-landbouw-2ws15vhicb.pdf

Roel F. Veerkamp

Papers

Optimizing dairy cattle breeding programs using international genetic evaluations

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle.

Method for estimating a breeding value for an organism without a known phenotype

Heritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle

Visie op fokkerij voor de biologische landbouw