25 research outputs found
Bovine gene polymorphisms related to fat deposition and meat tenderness
Leptin, thyroglobulin and diacylglycerol O-acyltransferase play important roles in fat metabolism. Fat deposition has an influence on meat quality and consumers' choice. The aim of this study was to determine allele and genotype frequencies of polymorphisms of the bovine genes, which encode leptin (LEP), thyroglobulin (TG) and diacylglycerol O-acyltransferase (DGAT1). A further objective was to establish the effects of these polymorphisms on meat characteristics. We genotyped 147 animals belonging to the Nelore (Bos indicus), Canchim (5/8 Bos taurus + 3/8 Bos indicus), Rubia Gallega X Nelore (1/2 Bos taurus + 1/2 Bos indicus), Brangus Three-way cross (9/16 Bos taurus + 7/16 Bos indicus) and Braunvieh Three-way cross (3/4 Bos taurus + 1/4 Bos indicus) breeds. Backfat thickness, total lipids, marbling score, ribeye area and shear force were fitted, using the General Linear Model (GLM) procedure of the SAS software. The least square means of genotypes and genetic groups were compared using Tukey's test. Allele frequencies vary among the genetic groups, depending on Bos indicus versus Bos taurus influence. The LEP polymorphism segregates in pure Bos indicus Nelore animals, which is a new finding. The T allele of TG is fixed in Nelore, and DGAT1 segregates in all groups, but the frequency of allele A is lower in Nelore animals. The results showed no association between the genotypes and traits studied, but a genetic group effect on these traits was found. So, the genetic background remains relevant for fat deposition and meat tenderness, but the gene markers developed for Bos taurus may be insufficient for Bos indicus
Haplotype Analysis Improved Evidence for Candidate Genes for Intramuscular Fat Percentage from a Genome Wide Association Study of Cattle
In genome wide association studies (GWAS), haplotype analyses of SNP data are neglected in favour of single point analysis of associations. In a recent GWAS, we found that none of the known candidate genes for intramuscular fat (IMF) had been identified. In this study, data from the GWAS for these candidate genes were re-analysed as haplotypes. First, we confirmed that the methodology would find evidence for association between haplotypes in candidate genes of the calpain-calpastatin complex and musculus longissimus lumborum peak force (LLPF), because these genes had been confirmed through single point analysis in the GWAS. Then, for intramuscular fat percent (IMF), we found significant partial haplotype substitution effects for the genes ADIPOQ and CXCR4, as well as suggestive associations to the genes CEBPA, FASN, and CAPN1. Haplotypes for these genes explained 80% more of the phenotypic variance compared to the best single SNP. For some genes the analyses suggested that there was more than one causative mutation in some genes, or confirmed that some causative mutations are limited to particular subgroups of a species. Fitting the SNPs and their interactions simultaneously explained a similar amount of the phenotypic variance compared to haplotype analyses. Haplotype analysis is a neglected part of the suite of tools used to analyse GWAS data, would be a useful method to extract more information from these data sets, and may contribute to reducing the missing heritability problem
Linkage mapping bovine EST-based SNP
BACKGROUND: Existing linkage maps of the bovine genome primarily contain anonymous microsatellite markers. These maps have proved valuable for mapping quantitative trait loci (QTL) to broad regions of the genome, but more closely spaced markers are needed to fine-map QTL, and markers associated with genes and annotated sequence are needed to identify genes and sequence variation that may explain QTL. RESULTS: Bovine expressed sequence tag (EST) and bacterial artificial chromosome (BAC)sequence data were used to develop 918 single nucleotide polymorphism (SNP) markers to map genes on the bovine linkage map. DNA of sires from the MARC reference population was used to detect SNPs, and progeny and mates of heterozygous sires were genotyped. Chromosome assignments for 861 SNPs were determined by twopoint analysis, and positions for 735 SNPs were established by multipoint analyses. Linkage maps of bovine autosomes with these SNPs represent 4585 markers in 2475 positions spanning 3058 cM . Markers include 3612 microsatellites, 913 SNPs and 60 other markers. Mean separation between marker positions is 1.2 cM. New SNP markers appear in 511 positions, with mean separation of 4.7 cM. Multi-allelic markers, mostly microsatellites, had a mean (maximum) of 216 (366) informative meioses, and a mean 3-lod confidence interval of 3.6 cM Bi-allelic markers, including SNP and other marker types, had a mean (maximum) of 55 (191) informative meioses, and were placed within a mean 8.5 cM 3-lod confidence interval. Homologous human sequences were identified for 1159 markers, including 582 newly developed and mapped SNP. CONCLUSION: Addition of these EST- and BAC-based SNPs to the bovine linkage map not only increases marker density, but provides connections to gene-rich physical maps, including annotated human sequence. The map provides a resource for fine-mapping quantitative trait loci and identification of positional candidate genes, and can be integrated with other data to guide and refine assembly of bovine genome sequence. Even after the bovine genome is completely sequenced, the map will continue to be a useful tool to link observable phenotypes and animal genotypes to underlying genes and molecular mechanisms influencing economically important beef and dairy traits
Impact of measurement error on testing genetic association with quantitative traits
10.1371/journal.pone.0087044PLoS ONE91-POLN
Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies
Development and Validation of a Cardiovascular Risk Assessment Model in Patients With Established Coronary Artery Disease
Development and Validation of a Cardiovascular Risk Assessment Model in Patients With Established Coronary Artery Disease
none13could contribute to the prevention of recurrent cardiovascular events. The purpose of the
present study was to develop and validate risk prediction models for various cardiovascular
end points in the EURopean trial On reduction of cardiac events with Perindopril in stable
coronary Artery disease (EUROPA) database, consisting of 12,218 patients with established
coronary artery disease, with a median follow-up of 4.1 years. Cox proportional hazards
models were used for model development. The end points examined were cardiovascular
mortality, noncardiovascular mortality, nonfatal myocardial infarction, coronary artery
bypass grafting, percutaneous coronary intervention, resuscitated cardiac arrest, and
combinations of these end points. The performance measures included Nagelkerke’s R2,
time-dependent area under the receiver operating characteristic curves, and calibration
plots. Backward selection resulted in a prediction model for cardiovascular mortality
(464 events) containing age, current smoking, diabetes mellitus, total cholesterol, body mass
index, previous myocardial infarction, history of congestive heart failure, peripheral vessel
disease, previous revascularization, and previous stroke. The model performance was
adequate for this end point, with a Nagelkerke R2 of 12%, and an area under the receiver
operating characteristic curve of 0.73. However, the performance of models constructed for
nonfatal and combined end points was considerably worse, with an area under the receiver
operating characteristic curve of about 0.6. In conclusion, in patients with established
coronary artery disease, the risk of cardiovascular mortality during longer term follow-up
can be adequately predicted using the clinical characteristics available at baseline. However,
the prediction of nonfatal outcomes, both separately and combined with fatal outcomes,
poses major challenges for clinicians and model developers. 2013 Elsevier Inc. All
rights reserved.noneLinda Battes; Rogier Barendse; Ewout W. Steyerberg;
Maarten L. Simoons; Jaap W. Deckers; Daan Nieboer;
Michel Bertrand; Roberto Ferrari; Willem J. Remme;
Kim Fox; Johanna J.M. Takkenberg; Eric Boersma;
and Isabella Kardys;Linda, Battes; Rogier, Barendse; Ewout W., Steyerberg; Maarten L., Simoons; Jaap W., Deckers; Daan, Nieboer; Michel, Bertrand; Ferrari, Roberto; Willem J., Remme; Kim, Fox; Johanna J. M., Takkenberg; Eric, Boersma; Isabella, Kardy