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

Genomic selection refers to the use of dense markers covering the whole genome to estimate the breeding value of selection candidates for a quantitative trait. This paper considers prediction of breeding value based on a linear combination of the markers. In this case the best estimate of each marker’s effect is the expectation of the effect conditional on the data. To calculate this requires a prior distribution of marker effects. If the marker effects are normally distributed with constant variance, BLUP can be used to calculate the estimated effects of the markers and hence the estimated breeding value (EBV). In this case the model is equivalent to a conventional animal model in which the relationship matrix among the animals is estimated from the markers instead of the pedigree. The accuracy of the EBV can approach 1.0 but a very large amount of data is required. An alternative model was investigated in which only some markers have non-zero effects and these effects follow a reflected exponential distribution. In this case the expected effect of a marker is a non-linear function of the data such that apparently small effects are regressed back almost to zero and consequently these markers can be deleted from the model. The accuracy in this case is considerably higher than when marker effects are normally distributed. If genomic selection is practiced for several generations the response declines in a manner that can be predicted from the marker allele frequencies. Genomic selection is likely to lead to a more rapid decline in the selection response than phenotypic selection unless new markers are continually added to the prediction of breeding value. A method to find the optimum index to maximise long term selection response is derived. This index varies the weight given to a marker according to its frequency such that markers where the favourable allele has low frequency receive more weight in the index.

http://www.adhis.com.au/v2/downv2.nsf/(ContentByKey)/234526a2c35feedfca257b5c003a9b09/$file/genomic%20selection%20prediction%20of%20accuracy%20and%20maximisation%20of%20long%20term%20response%20(goddard).pdf?open

Genomic selection: prediction of accuracy and maximisation of long term response

Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the genome for associations, and most reported associations reflect real biologic causation. But, David Goldstein asks, do they matter?

Common Genetic Variation and Human Traits

The mathematical theory of quantitative genetics

BLUP (best linear unbiased prediction) is widely used to predict complex traits in plant and animal breeding, and increasingly in human genetics. The BLUP mathematical model, which consists of a single random effect term, was adequate when kinships were measured from pedigrees. However, when genome-wide SNPs are used to measure kinships, the BLUP model implicitly assumes that all SNPs have the same effect-size distribution, which is a severe and unnecessary limitation. We propose MultiBLUP, which extends the BLUP model to include multiple random effects, allowing greatly improved prediction when the random effects correspond to classes of SNPs with distinct effect-size variances. The SNP classes can be specified in advance, for example, based on SNP functional annotations, and we also provide an adaptive procedure for determining a suitable partition of SNPs. We apply MultiBLUP to genome-wide association data from the Wellcome Trust Case Control Consortium (seven diseases), and from much larger studies of celiac disease and inflammatory bowel disease, finding that it consistently provides better prediction than alternative methods. Moreover, MultiBLUP is computationally very efficient; for the largest data set, which includes 12,678 individuals and 1.5 M SNPs, the total analysis can be run on a single desktop PC in less than a day and can be parallelized to run even faster. Tools to perform MultiBLUP are freely available in our software LDAK.

/pdf/multiblup-improved-snp-based-prediction-for-complex-traits-1qq5mzoqzn.pdf

MultiBLUP: improved SNP-based prediction for complex traits

Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1–5 and has evolutionary consequences6–8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes. A century after being predicted by theory, the authors detect and quantify the genomic signature of assortative mating in ~400,000 contemporary human genomes, and report new genetic evidence for assortative mating on height and educational attainment.

Imprint of assortative mating on the human genome.

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fisher9s classical theory1 to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height (θ=3.2%) and educational attainment (θ=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

/pdf/imprint-of-assortative-mating-on-the-human-genome-a4rp3q7wy6.pdf

Imprint of Assortative Mating on the Human Genome

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association studies Prediction of individual genetic risk to disease from genome-wide

/pdf/a-simple-model-of-worm-adaption-to-livestock-bred-for-4m7jgl0eg1.pdf

A simple model of worm adaption to livestock bred for improved worm resistance

Please see attached 'FAETH.v1.1.data.descrip.docx' for how to use this dataset. For citation: Xiang, R., Van Den Berg, I., MacLeod, I. M., Hayes, B. J., Prowse-Wilkins, C. P., Wang, M., ... Goddard, M.E. (2019). Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits. PNAS (2019), In Press.

Michael E. Goddard

Papers

Imprint of assortative mating on the human genome.

Imprint of Assortative Mating on the Human Genome

association studies Prediction of individual genetic risk to disease from genome-wide

A simple model of worm adaption to livestock bred for improved worm resistance

The Functional And Evolutionary Trait Heritability (FAETH) score of over 17 million cattle sequence variants