Customer Publications Throughout December 2019

In advance of the Plant & Animal Genome conference in January in San Diego, CA, it made sense to showcase those working in Agrigenomics in the December Customer Success blog post! The published works this month also perfectly illustrate how SVS software can generate valuable GWAS and Genomic Prediction data for the agricultural industry. We look forward to seeing you all at PAG January 11th thru the 15th, 2020! Look for us at Booth 231!

GBLUP Analysis Predicted Fertility Phenotypes of Crossbred Bulls Using Data from Brahman and Tropical Composite

Work performed by an Australian research team dovetails perfectly with our last webcast entitled “Simplify Your GWAS & Genomic Prediction with SVS”! Using the “Genomic Best Linear Unbiased Predictor” (GBLUP) method in SVS software, the team investigated the use of this genomic selection approach for fertility-related traits in bulls used in Australian beef cattle herds. Bulls from three breed types (Brahman, Tropical Composites and crossbreds) were analyzed for four indicators of bull fertility (Serum levels of Inhibin, scrotal circumference at 18 and 24 months of age & percentage of normal sperm) to predict the genomic estimated breeding values (GEBV) of the crossbred animals. The team is striving to incorporate a multi-breed reference population to enhance the accuracy of GEBV prediction to better understand how to model different populations for varying environments. This is a work in progress so there will likely be more studies that will build on the progress made here.

Marina Fortes & Colleagues, University of Queensland / Presented at the Association for the Advancement of Animal Breeding & Genetics, 2019 Conference

If you were unable to attend the above-mentioned webcast, here is a recording of the live event!

Longitudinal Phenotypes Improve Genotype Association for Hyperketonemia in Dairy Cattle

Dairy cattle differ in their ability to tolerate and adapt to the postpartum phase when the energy demand is greater than the energy consumed, referred to as a period of negative energy balance (NEB). While some cows can make the necessary metabolic adjustments to counteract the condition, others may suffer from metabolic complications, namely Hyperketonemia (HYK). This condition negatively effects the health of the cow as well as effecting the producer’s bottom line. Animal scientists from the US used longitudinal phenotypes to improve the ability to detect genetic associations with complex metabolic diseases like HYK. Using SVS software throughout their analysis, the team first identified highly related animals using genomic identity-by-descent (IBD) estimations. For the GWAS analysis, they employed the Efficient Mixed Model Association eXpedited (EMMAX) algorithm embedded in SVS as well as estimating pseudo-heritability variance. During the haplotype analysis, linkage disequilibrium (LD) & expectation maximization (EM) steps were performed in SVS as well. Ultimately the results of this investigation led to the identification of five novel candidate genes with biological relevance to HYK or other factors related to the condition. As researchers build on this study, the use of genomic information will allow producers to selectively breed for healthier herds.

Heather Huson & Colleagues, Cornell University / Published in MDPI Animals

Genome-Wide Association Studies for Methane Production in Dairy Cattle

Researchers in Mexico and the UK teamed up to identify genomic regions associated with methane (CH4) emissions in dairy cattle located in temperate and tropical areas. With the scientific community focusing more and more on production agriculture as a source of greenhouse gas emissions, particularly focusing on the dairy industry, many strategies have been proposed to mitigate CH4 emissions. This led the group to investigate the use of breeding selection methods to predict the genetic factors that may result in decreased methane production as a possible contribution to a solution. Previous studies had already indicated that CH4 production varied considerably between animals even when they were fed the same diet, suggesting methane production may be related to rumen microbial populations and/or the kinetics of individual animals, and not necessarily a byproduct of the animal’s diet. Choosing to perform a GWAS analysis to detect candidate genes of interest, the team took advantage of SVS software’s ability to apply advanced regression technologies thus enabling them to perform linear and logistic regression, stepwise regression, gene by environment interaction regression, and permutation tests with numeric variables and recorded genotypes. Several SNPs were identified that may be incorporated into genomic selection programs for low CH4 emissions and will most likely lead to more investigations of this nature in the future.

Felipe Ruiz-Lopez & Colleagues, National Institute of Forest Research, National Center for Disciplinary Research in Animal, Agricultural and Livestock Physiology and Improvement-Mexico / Published in MDPI Genes

Genetic Dissection of Maternal Influence on In Vivo Haploid Induction in Maize

Maize is an important economic crop around the world due to its energy density and versatility. As a food source, maize along with wheat and rice, make up roughly 94% of the cereal grains produced globally. Successful maize breeding programs depend upon the development of homozygous inbred lines for use as parental lines for hybrid varieties. Recent advances in doubled haploid (DH) technology has been recognized as a more efficient alternative to traditional inbred line development. The International Maize and Wheat Improvement Center (CIMMYT) in collaboration with the University of Hohenheim in Germany, have been instrumental in introducing in vivo DH technology to tropical breeding programs. The resulting effect has been recognized by improved varieties and genetic gains to crops, such as disease resistance, higher seed quality and drought tolerance to critical environments in the developing world. In this study, the team used SVS software to investigate three specific topics: the variation for haploid inducibility among tropical inbred lines, identifying the best tropical inbreds that respond favorably to haploid induction, and identifying genomic regions in the maternal parent influencing haploid induction rate (HIR) using GWAS analysis. To support their goals, they performed the principal component analysis (PCA), kinship (using GBLUP) and linkage disequilibrium (LD) in SVS. GWAS analysis was also performed in SVS along with genomic prediction. The team’s results indicated they were able to home in on a few genomic regions which are responsible for maternal influence over haploid induction, thus providing a foundation for further genetic investigations to validate their findings.  

Sudha Nair & Colleagues, CIMMYT / Published in The Crop Journal

Medium Density Beadchip Genotype Data Reveals Genomic Structure of South African Merino-Based Breeds

In a poster recently presented at the 23rd Conference of the Association for the Advancement of Animal Breeding and Genetics, South African researchers utilized SVS software to learn more about the population genetic structure and breed relationships between Merino and Merino-based sheep breeds developed in their country. Since the Merino’s introduction to South Africa from Spain in the late 18th Century, the breed has demonstrated its ability to adapt to the conditions of the area and has contributed heavily to many of today’s popular sheep breeds. To prove there is genetic diversity and population substructure among the South African Merino-based breeds, the study sampled five different sheep populations from the Eastern Cape region. The team was able to conclude that there is significant genetic variation today in the South African Merino-type sheep breeds, which could be beneficial for genetic exchange through outbreeding to improve unfavorable traits observed in certain groups.

Edgar Dzomba, University of KwaZulu-Natal & Farai Muchadeyi, Agricultural Research Council, South Africa / Presented at AAABG 23rd Annual Conference

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