It’s all in the gene: cows

Written by: Stephen Hsu

Primary Source: Information Processing

Some years ago a German driver took me from the Perimeter Institute to the Toronto airport. He was an immigrant to Canada and had a background in dairy farming. During the ride he told me all about driving German farmers to buy units of semen produced by highly prized Canadian bulls. The use of linear polygenic models in cattle breeding is already widespread, and the review article below gives some idea as to the accuracy.

See also Genomic Prediction: No Bull and Plenty of room at the top.

Invited Review: Reliability of genomic predictions for North American Holstein bulls

Journal of Dairy Science Volume 92, Issue 1, Pages 16–24, January 2009.
DOI: http://dx.doi.org/10.3168/jds.2008-1514

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R2) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R2 values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R2 values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.

Results and Discussion: … Marker effects for most other traits were evenly distributed across all chromosomes with only a few regions having larger effects, which may explain why the infinitesimal model and standard quantitative genetic theories have worked well. The distribution of marker effects indicates primarily polygenic rather than simple inheritance and suggests that the favorable alleles will not become homozygous quickly, and genetic variation will remain even after intense selection. Thus, dairy cattle breeders may expect genetic progress to continue for many generations.

… Most animal breeders will conclude that these gains in reliability are sufficient to make genotyping profitable before breeders invest in progeny testing or embryo transfer. Rates of genetic progress should increase substantially as breeders take advantage of these new tools for improving animals (Schaeffer, 2008). Further increases in number of genotyped bulls, revisions to the statistical methods, and additional edits should increase the precision of future genomic predictions.

Table 3

Trait Parent average Genomic prediction Gain from nonlinear genomic prediction compared with published parent average
Published Observed Expected Linear Nonlinear
Net merit 30 14 67 53 53 23
Milk yield 35 32 69 56 58 23
Fat yield 35 17 69 65 68 33
Protein yield 35 31 69 58 57 22
Fat percentage 35 29 69 69 78 43
Protein percentage 35 32 69 62 69 34
Productive life 27 28 55 42 45 18

“Horses ain’t like people, man. They can’t make themselves better than they’re born. See, with a horse, it’s all in the gene. It’s the fucking gene that does the running. The horse has got absolutely nothing to do with it.” — Paulie (Eric Roberts) in The Pope of Greenwich Village

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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