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

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

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A User’s Guide

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

https://www.genetics.org/content/genetics/193/2/327.full.pdf

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels

The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have compared the statistical performance of two methods (the least absolute shrinkage and selection operator—lasso and the elastic net) on two simulated data sets and one real data set from a 50 K genome-wide single nucleotide polymorphism (SNP) panel of 5570 Fleckvieh bulls. The first simulated data set displays moderate to high linkage disequilibrium between SNPs, whereas the second simulated data set from the QTLMAS 2010 workshop is biologically more complex. We used cross-validation to find the optimal value of regularization parameter λ with both minimum MSE and minimum MSE + 1SE of minimum MSE. The optimal λ values were used for variable selection. Based on the first simulated data, we found that the minMSE in general picked up too many SNPs. At minMSE + 1SE, the lasso didn't acquire any false positives, but selected too few correct SNPs. The elastic net provided the best compromise between few false positives and many correct selections when the penalty weight α was around 0.1. However, in our simulation setting, this α value didn't result in the lowest minMSE + 1SE. The number of selected SNPs from the QTLMAS 2010 data was after correction for population structure 82 and 161 for the lasso and the elastic net, respectively. In the Fleckvieh data set after population structure correction lasso and the elastic net identified from 1291 to 1966 important SNPs for milk fat content, with major peaks on chromosomes 5, 14, 15, and 20. Hence, we can conclude that it is important to analyze GWAS data with both the lasso and the elastic net and an alternative tuning criterion to minimum MSE is needed for variable selection.

/pdf/evaluation-of-the-lasso-and-the-elastic-net-in-genome-wide-496nfwwf4o.pdf

Evaluation of the lasso and the elastic net in genome-wide association studies.

Short communication: Genomic selection using a multi-breed, across-country reference population.

Genetic variants underlying reduced male reproductive performance have been identified in humans and model organisms, most of them compromising semen quality. Occasionally, male fertility is severely compromised although semen analysis remains without any apparent pathological findings (i.e., idiopathic subfertility). Artificial insemination (AI) in most cattle populations requires close examination of all ejaculates before insemination. Although anomalous ejaculates are rejected, insemination success varies considerably among AI bulls. In an attempt to identify genetic causes of such variation, we undertook a genome-wide association study (GWAS). Imputed genotypes of 652,856 SNPs were available for 7962 AI bulls of the Fleckvieh (FV) population. Male reproductive ability (MRA) was assessed based on 15.3 million artificial inseminations. The GWAS uncovered a strong association signal on bovine chromosome 19 (P = 4.08 × 10(-59)). Subsequent autozygosity mapping revealed a common 1386 kb segment of extended homozygosity in 40 bulls with exceptionally poor reproductive performance. Only 1.7% of 35,671 inseminations with semen samples of those bulls were successful. None of the bulls with normal reproductive performance was homozygous, indicating recessive inheritance. Exploiting whole-genome re-sequencing data of 43 animals revealed a candidate causal nonsense mutation (rs378652941, c.483C>A, p.Cys161X) in the transmembrane protein 95 encoding gene TMEM95 which was subsequently validated in 1990 AI bulls. Immunohistochemical investigations evidenced that TMEM95 is located at the surface of spermatozoa of fertile animals whereas it is absent in spermatozoa of subfertile animals. These findings imply that integrity of TMEM95 is required for an undisturbed fertilisation. Our results demonstrate that deficiency of TMEM95 severely compromises male reproductive performance in cattle and reveal for the first time a phenotypic effect associated with genomic variation in TMEM95.

/pdf/a-nonsense-mutation-in-tmem95-encoding-a-nondescript-1t14kigfqu.pdf

Christian Fuerst

Papers

Evaluation of the lasso and the elastic net in genome-wide association studies.

Short communication: Genomic selection using a multi-breed, across-country reference population.

A nonsense mutation in TMEM95 encoding a nondescript transmembrane protein causes idiopathic male subfertility in cattle.

Additive and Nonadditive Genetic Variances for Milk Yield, Fertility, and Lifetime Performance Traits of Dairy Cattle

Recording of direct health traits in Austria--experience report with emphasis on aspects of availability for breeding purposes.