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

Eukaryotic genes are distributed along chromosomes as clusters of highly expressed genes termed RIDGEs (Regions of IncreaseD Gene Expression) and lowly expressed genes termed anti-RIDGEs, interspersed among genes expressed at intermediate levels or not expressed Previous studies based on this observation suggested a dual mechanism of gene regulation, where, in addition to transcription factors, the chromosomal domain influences the expression level of their embedded genes The objectives here were to provide evidence for the existence of chromosomal regional regulation of transcription in the bovine genome, to analyse the genomic features of genes located within RIDGEs versus anti-RIDGEs and tissue-specific genes versus housekeeping and to examine the genomic distribution of genes subject to positive selection in bovines Gene expression analysis of four brain tissues and the anterior pituitary of 28 cows identified 70 RIDGEs and 41 anti-RIDGEs (harbouring 3735 and 1793 bovine genes respectively) across the bovine genome which are significantly higher than expected by chance Housekeeping genes (defined here as genes expressed in all five tissues) were over-represented within RIDGEs but tissue-specific genes (genes expressed in only one of the five tissues) were not Housekeeping genes and genes within RIDGEs had, in general, higher expression levels and GC content but shorter gene lengths and intron lengths than tissue-specific genes and genes within anti-RIDGES Our findings suggest the existence of chromosomal regional regulation of transcription in the bovine genome The genomic features observed for genes within RIDGEs and housekeeping genes in bovines agree with previous studies in several other species further strengthening the hypothesis of selective pressure to keep the highly and widely expressed genes short and compact for transcriptional efficiency Further, positively selected genes were found non-randomly distributed on the genome with a preference for RIDGEs and regions of intermediate gene expression compared to anti-RIDGEs

/pdf/regional-regulation-of-transcription-in-the-bovine-genome-1tkiptmnng.pdf

Regional Regulation of Transcription in the Bovine Genome

Improving predictive performance on survival in dairy cattle using an ensemble learning approach

Rapid changes in farm scale and pressures on costs and farm management together with the negative correlation between production and fitness traits have stimulated the demand for robust cows. We have defined a robust cow as: “a cow that is able to maintain homeostasis in the commonly accepted and sustainable dairy herds of the near future” which clearly contains an element of environmental sensitivity and genotype by environment interaction (GxE). Classical breeding solutions to breed for more robustness are i) avoiding inbreeding, ii) multi-trait selection and iii) allowing more natural selection. Statistical models allow more direct selection for robustness by estimating genetic correlations between environments (discrete macro scale), reaction norms describing genotypes as a function of a continuous environmental parameter (continuous macro scale) and genetic variation in residual variance which is environmental sensitivity for a large number of unidentifiable environmental aspects with a relatively small effect each (unknown micro scale). The application of these complex models is still under development, but first results indicate more GxE for fitness traits than yield traits, albeit evidence for strong reranking of animals is still limited, so it is mainly the magnitude of the variance which is affected by environment. These statistical models will contribute, together with classical tools and new phenotypic and genomic measurement tools, to the breeding of cows that also fit in future dairy systems.

Statistical genetics to improve robustness of dairy cows

Genomic selection is particularly beneficial for dairy cattle breeding programs, because it allows to significantly reduce generation interval, and cheaply increase selection intensity, while the accuracy of selection is only marginally lower compared to progeny testing schemes. It relaxes the requirement of traditional dairy cattle breeding schemes to measure phenotypes from progeny groups for each male selection candidate. Therefore, genomic selection holds the promise to allow selection for new traits, that are difficult or expensive to measure.

/pdf/predicted-response-of-genomic-selection-for-new-traits-using-3fv3x2y14w.pdf

Roel F. Veerkamp

Papers

Regional Regulation of Transcription in the Bovine Genome

Improving predictive performance on survival in dairy cattle using an ensemble learning approach

Statistical genetics to improve robustness of dairy cows

Predicted response of genomic selection for new traits using combined cow and bull reference populations

Genetics of feed efficiency in dairy cattle