Genome-wide association study to identify chromosomal regions associated with antibody response to Mycobacterium avium subspecies paratuberculosis in milk of Dutch Holstein-Friesians

Abstract

Heritability of susceptibility to Johne's disease in cattle has been shown to vary from 0.041 to 0.159. Although the presence of genetic variation involved in susceptibility to Johne's disease has been demonstrated, the understanding of genes contributing to the genetic variance is far from complete. The objective of this study was to contribute to further understanding of genetic variation involved in susceptibility to Johne's disease by identifying associated chromosomal regions using a genome-wide association approach. Log-transformed ELISA test results of 265,290 individual Holstein-Friesian cows from 3,927 herds from the Netherlands were analyzed to obtain sire estimated breeding values for Mycobacterium avium subspecies paratuberculosis (MAP)-specific antibody response in milk using a sire-maternal grandsire model with fixed effects for parity, year of birth, lactation stage, and herd; a covariate for milk yield on test day; and random effects for sire, maternal grandsire, and error. For 192 sires with estimated breeding values with a minimum reliability of 70%, single nucleotide polymorphism (SNP) typing was conducted by a multiple SNP analysis with a random polygenic effect fitting 37,869 SNP simultaneously. Five SNP associated with MAP-specific antibody response in milk were identified distributed over 4 chromosomal regions (chromosome 4, 15, 18, and 28). Thirteen putative SNP associated with MAP-specific antibody response in milk were identified distributed over 10 chromosomes (chromosome 4, 14, 16, 18, 19, 20, 21, 26, 27, and 29). This knowledge contributes to the current understanding of genetic variation involved in Johne's disease susceptibility and facilitates control of Johne's disease and improvement of health status by breeding.

Phenotype definition is a main point in genome-wide association studies for bovine Mycobacterium avium ssp. paratuberculosis infection status

“…None of these genes were found in previous published GWA studies (summarized in Supplementary Table S1). Also there is no functional candidate gene described in the literature within 1 Mbp region of the SNPs in the present study (Settles et al, 2009;Minozzi et al, 2010;Pant et al, 2010;Kirkpatrick et al, 2011;Minozzi et al, 2012;van Hulzen et al, 2012). This supports once more the conclusion that paratuberculosis is probably affected by a large number of genes.…”

“…In this study, regions on BTA 3 and 9 were identified with a high significance to MAP status tested by MAP culture in feces and tissue in US Holsteins. In different GWA studies several associations were found on different chromosomes (Minozzi et al, 2010;Pant et al, 2010;Kirkpatrick et al, 2011;Minozzi et al, 2012;van Hulzen et al, 2012), summarized in Supplementary Table S1. These studies provide in general evidence for the existence of genomic regions associated with MAP infection in dairy cows.…”

Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens

“…The plot is dominated by a few large regions, with all the other regions representing much smaller variances ≤2.5%. Methods like BayesB are strongly influenced by priors (Van Hulzen et al, 2012), and particularly by the percentage of SNPs assumed to have null effect (π). Studies on the number of genes influencing a quantitative effect estimate the number of <500 (Otto and Jones, 2000; Hayes and Goddard, 2001).…”

“…Part of the reason that CGWAS accounts for less genetic variance than ssGWAS may be because CGWAS does not take into account all relationships among subjects but only for genotyped individuals, which might lead to detection of spurious associations due to incompleteness (Kang et al, 2010). BayesB and CGWAS are also dependent on the choice of parameters and accuracy of deregression (Garrick et al, 2009; Van Hulzen et al, 2012), while ssGWAS1 or ssGWAS2 include all available relationships, and deregression is not necessary. Zeng et al (2012) and Wang et al (2012) examined a few methods for GWAS using simulated data sets, and both indicated that all methods were able to identify the same top few regions.…”

First insights in the genetics of caseous lymphadenitis in goats

“…From an economical and epidemiological point of view it would be important to select for disease resistant animals in order to decrease steadily the presence of CLA in goat herds. Although the application of genomic technologies, mainly applied in dairy cattle, can decrease the generation interval and boost genetic gain compared with traditional phenotypic selection, it is still important to perform genome wide association studies for infectious diseases to pinpoint and localise the genetic variations that have an effect on health traits, in cases where no effective therapy or prophylaxis are available, as in the case of bovine tuberculosis (Allen et al 2010;Finlay et al 2012;Bermingham et al 2014;Zare et al 2014;Richardson et al 2016) and bovine paratuberculosis (Settles et al 2009;Kirkpatrick et al 2011;Minozzi et al 2012;van Hulzen et al 2012;Alpay et al 2014;Pauciullo et al 2015), allowing the possibility to include the genetic approach as a complementary aid for disease incidence reduction. Recent developments in the animal genome sequencing technologies have allowed the application of a series of methods designed to identify regions of the genome linked or that affect certain characters of economic interest in cattle (e.g., production and/or resistance to diseases in dairy cattle) (Minozzi et al 2013) and in small ruminants, suitable when pedigree information is not available or unreliable.…”