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

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Advanced Statistical Methods in Biometric Research.

Low density chips are appealing alternative tools in order to reduce genotyping costs. Such a chip is already commercially available. The best way to use low density chips is to impute data to a more dense coverage such as the standard 50K genotype. Two alternative in silico chips are presented and include markers selected to optimise Minor Allele Frequency and spacing. The objective of this study was to compare imputation accuracy of these custom low density chips with the commercially available 3K chip. Three French dairy and beef breeds were studied: Holstein, Montbeliarde and Blonde d’Aquitaine with respectively 4,037, 1,219 and 991 50K-genotypes. Markers were masked for the validation population in order to mimic a low density genotype. Imputation was realised with Beagle software. 95% to 99% of alleles were correctly imputed depending on the breed and the low density chip. Custom low density chip gave better results. The gain to use 6K chip was found to be even higher for beef breeds such as Blonde d’Aquitaine. A low density chip with 6,000 markers is a valuable genotyping tool that is suitable for both dairy and beef breeds. Such a tool can be used for pre-selection of young animals or large screening of the female population

https://journal.interbull.org/index.php/ib/article/download/1190/1258

Imputation Efficiency with Different Low Density Chips in French Dairy and Beef Breeds

De nombreuses methodes ont ete proposees pour cartographier finement des locus impliques dans la variabilite des caracteres quantitatifs (Quantitative Trait Loci ou QTL). La diversite de ces methodes s'explique notamment par les differences qui existent entre les especes, par exemple du fait des intervalles entre generations, des couts de production, ou de la variabilite des couts de phenotypage des caracteres. Nous proposons ici une revue des methodes permettant d'acceder a une cartographie fine de ces locus, decrivant au prealable les conditions d'une reduction de l'intervalle de localisation de ces QTL. Le parametre cle permettant de reduire cet intervalle est le nombre de recombinaisons utiles. C'est pourquoi nous decrivons, dans les parties suivantes, les methodes de cartographie fine en distinguant celles qui necessitent la procreation d'individus ou de generations supplementaires pour augmenter le nombre de recombinaisons, de celles qui ont recours a des modeles statistiques pour inferer des recombinaisons ayant eu lieu au cours des generations passees.

/pdf/comment-affiner-la-localisation-d-un-qtl-dq1av4gofg.pdf

Comment affiner la localisation d'un QTL?

Segregation analysis of fat content data in Holstein X European Friesian crossbred cattle.

Summary

A Bayesian approach to map Quantitative Trait Loci (QTL) is compared to the flanking markers regression method (asymptotically equivalent to the traditional Interval Mapping method) using simulated backcross data. The main part consists of a comparison of the properties for a one QTL model. The Bayesian approach gives less biases and more accurate estimates of the genetic effect and significance thresholds close to χ2 quantiles when the number of individuals and markers become large. The behaviour of both approaches in the case of two linked QTL is mentioned.



Zusammenfassung

Eigenschaften eines BAYES Ansatz zur QTL Identifikation im Vergleich zur flankierende Marker Regressions-Methode.



Der Bayes Ansatz zur Kartierung von Quantitativen Merkmals Loci (QTL) wurde mittels simulierter Ruckkreuzungsdaten mit der Flankierenden Marker Regressionsmethode (asymptotisch aquivalent mit der traditionellen Intervall Markierung) verglichen. Es werden hauptsachlich die Eigenschaften fur ein ein-QTL Modell gepruft. Der Bayes Ansatz ergibt weniger verzerrte und genauere Schatz-werte der genetischen Wirkung und Signifikanz Schwellen nahe den χ2 Quantilen bei grosen Individuen und Marker Zahlen. Die Eigenschaften beider Ansatze im Falle von zwei gekoppelten QTL werden erwahnt.

Properties of a Bayesian approach to detect QTL compared to the flanking markers regression method

INTRODUCTION The November 2001 official international genetic evaluation of dairy bulls for production traits has seen 25 countries sending and receiving data from Interbull. The current method used for international comparisons of dairy bulls was developed by Schaeffer in 1993 and it is known as Multi-trait Across Country Evaluation (MACE). Bull proofs computed at the national level are provided to Interbull every four months. They are de-regressed and used as elementary observations in a multiple trait BLUP sire model considering production in different countries as different traits. At the end of the process, each country receives a list of EBVs on its own scale for all bulls evaluated in any of the participating countries. Over time, the method has been improved but has been found highly sensitive to changes in sire variance (within country as well as over time) to quality of national proofs and to changes in genetic correlations. As the number of Interbull countries is growing, the precise estimation of the about 350 genetic correlations for each trait between countries for Holsteins is becoming a formidable task. The present approach only evaluates bulls and with the increasing international exchange of female embryos and live cows, an international genetic evaluation of elite females is highly desirable. In order to overcome potential biases resulting from the partial use of basic information, (summarised in the national sire proofs), several attempts to run a global animal model (GAM) using raw data (i.e. records and pedigree on all cows within each population and their related sires) have been made (Weigel et al., 1999, 2001). While this alternative is very appealing, it has several limitations. Production systems, management and data collection conditions widely vary across countries. National evaluations have been developed to account for the most appropriate factors affecting production (Jorjani, 2000). It is unrealistic to include in GAM the exhaustive list of fixed effects considered in at least one country. But any selection of a subset of these fixed effects may lead to biased comparisons. Moreover, putting together raw data from many different countries raises questions about the feasibility to properly estimate genetic parameters for all of them, and the use of multiple lactations or test-day records will increase the difficulties to be faced. Following the theory behind the concept of borderless genetic evaluations (Lohuis,1997), it has been suggested that differences among countries in MACE are sometimes imposed by national borders, and not by real differences at the genetic level. Clustering of countries (or herds across countries) with similar characteristics has thus been advocated as a tool to simplify the computations in order to make feasible a GAM evaluation across countries (Zwald et al., 2001): the number of traits to evaluate would be equal to the number of distinct clusters. This approach brings up new questions: which are the most

Didier Boichard

Papers

Imputation Efficiency with Different Low Density Chips in French Dairy and Beef Breeds

Comment affiner la localisation d'un QTL?

Segregation analysis of fat content data in Holstein X European Friesian crossbred cattle.

Properties of a Bayesian approach to detect QTL compared to the flanking markers regression method

An alternative procedure for international evaluations: production traits european joint evaluation