scholarly journals Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1147
Author(s):  
Asha M. Miles ◽  
Christian J. Posbergh ◽  
Heather J. Huson
Keyword(s):  
Principal Component Analysis ◽  
Association Studies ◽  
Principal Component ◽  
Component Analysis ◽  
Pleiotropic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Type Traits ◽  
Genomic Regions

Our objectives were to robustly characterize a cohort of Holstein cows for udder and teat type traits and perform high-density genome-wide association studies for those traits within the same group of animals, thereby improving the accuracy of the phenotypic measurements and genomic association study. Additionally, we sought to identify a novel udder and teat trait composite risk index to determine loci with potential pleiotropic effects related to mastitis. This approach was aimed at improving the biological understanding of the genetic factors influencing mastitis. Cows (N = 471) were genotyped on the Illumina BovineHD777k beadchip and scored for front and rear teat length, width, end shape, and placement; fore udder attachment; udder cleft; udder depth; rear udder height; and rear udder width. We used principal component analysis to create a single composite measure describing type traits previously linked to high odds of developing mastitis within our cohort of cows. Genome-wide associations were performed, and 28 genomic regions were significantly associated (Bonferroni-corrected p < 0.05). Interrogation of these genomic regions revealed a number of biologically plausible genes whicht may contribute to the development of mastitis and whose functions range from regulating cell proliferation to immune system signaling, including ZNF683, DHX9, CUX1, TNNT1, and SPRY1. Genetic investigation of the risk composite trait implicated a novel locus and candidate genes that have potentially pleiotropic effects related to mastitis.

scholarly journals Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies

2014 ◽  
Vol 94 (5) ◽  
pp. 662-676 ◽  
Author(s):  
Hugues Aschard ◽  
Bjarni J. Vilhjálmsson ◽  
Nicolas Greliche ◽  
Pierre-Emmanuel Morange ◽  
David-Alexandre Trégouët ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Association Studies ◽  
Principal Component ◽  
Component Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects

2019 ◽  
Vol 35 (17) ◽  
pp. 3046-3054 ◽  
Author(s):  
Anastasia Gurinovich ◽  
Harold Bae ◽  
John J Farrell ◽  
Stacy L Andersen ◽  
Stefano Monti ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
False Positive Rate ◽  
Principal Component ◽  
True Positive Rate ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Positive Rate

Abstract Motivation Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery. Results In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects’ ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype. Availability and implementation PopCluster was implemented using the R programming language, PLINK and Eigensoft software, and can be found at the following GitHub repository: https://github.com/gurinovich/PopCluster with instructions on its installation and usage. Supplementary information Supplementary data are available at Bioinformatics online.

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