Genome-wide association studies of female reproduction in tropically adapted beef cattle
The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI. In addition, correlated traits such as BW, height, serum IGF1 concentration, condition score, and fatness were also examined. In the Brahman and Tropical Composite cattle, 169 [false positive rate (FPR) = 0.262] and 84 (FPR = 0.581) SNP, respectively, were significant (P < 0.001) for AGECL. In Brahman, 41% of these significant markers mapped to a single chromosomal region on BTA14. In Tropical Composites, 16% of these significant markers were located on BTA5. For PPAI, 66 (FPR = 0.67) and 113 (FPR = 0.432) SNP were significant (P < 0.001) in Brahman and Tropical Composite, respectively, whereas for PW, 68 (FPR = 0.64) and 113 (FPR = 0.432) SNP were significant (P < 0.01). In Tropical Composites, the largest concentration of PPAI markers were located on BTA5 [19% (PPAI) and 23% (PW)], and BTA16 [17% (PPAI) and 18% (PW)]. In Brahman cattle, the largest concentration of markers for postpartum anestrus was located on BTA3 (14% for PPAI and PW) and BTA14 (17% PPAI). Very few of the significant markers for female reproduction traits for the Brahman and Tropical Composite breeds were located in the same chromosomal regions. However, fatness and BW traits as well as serum IGF1 concentration were found to be associated with similar genome regions within and between breeds. Clusters of SNP associated with multiple traits were located on BTA14 in Brahman and BTA5 in Tropical Composites.
Genome wide selection in dairy cattle based on high-density genome-wide SNP analysis: From discovery to application
A genome wide selection (GWS) platform was developed for prediction of genetic merit in dairy cattle. The critical components of the GWS platform included a genome wide SNP analysis assay representing 15,036 SNPs, 1546 progeny tested Holstein Friesian sires with EBV (ABV) for 42 lactation performance traits, and a series of complexity reduction methods with internal and external cross validation. Derived Molecular Breeding Values (MBV) using a fraction of the available SNP information, were shown to have high predictive value for genetic merit (r=0.65-0.87 with ABV) in bulls not used in the training data from which the SNP effects were derived. GWS can be used in the absence of SNP location and pedigree to make potentially highly accurate predictions of genetic merit at an early age from DNA analyses.
Beef cattle breeding in Australia with genomics: opportunities and needs
Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.
Fine mapping QTL with haplotypes determined from dense single nucleotide polymorphic markers
We use publicly available methods to impute missing genotypes, infer haplotypes and partition haplotypes into blocks for large numbers of single nucleotide polymorphic data on two sections of chromosomes. Haplotype trend regression was used to associate these haplotype blocks with a continuously distributed trait. A number of significant regions of chromosomes, that were not found when tested with single-marker tests, were identified. This study demonstrated a feasible framework to fine-mapping QTL using haplotypes of SNP markers.
Use of the numerator relationship matrix in genetic analysis of autopolyploid species
Mixed models incorporating the inverse of a numerator relationship matrix (NRM) are widely used to estimate genetic parameters and to predict breeding values in animal breeding. A simple and quick method to directly calculate the inverse of the NRM has been historically developed for diploid animal species. Mixed models are less used in plant breeding partly because the existing method for diploids is not applicable to autopolyploid species. This is because of the phenomenon of double reduction and the possibility that gametes carry alleles which are identical by descent. This paper generalises the NRM and its inverse for autopolyploid species, so it can be easily incorporated into their genetic analysis. The technique proposed is to first calculate the kinship coefficient matrix and its inverse as a precursor to calculating the NRM and its inverse. This allows the NRM to be calculated for populations containing individuals of mixed ploidy levels. This generalization can also accommodate uncertain parentage by generating the "average" relationship matrix. The possibility that non-inbred parents can produce inbred progeny (double reduction) is also discussed. Rules are outlined that are applicable for any level of ploidy.
Editorial - Genomic selection: promises and propriety
Since the 1990s many promises have been made about economic benefits available from genome scanning. We were assured that we would be able to look at an animal's genes and determine its genetic merit directly. Of course this included the assumption, generally implied, that we would have already determined the effects of all the (important) genes, at all times and under all circumstances. However, despite numerous 'in silico' proofs, it seems that ascertaining these effects is proving much more elusive than originally assumed. We did not comprehend how much data we would need. By mating the best to the best, we have been 'improving' domesticated species for millenia. We keep getting more sophisticated about determining what is 'best' but genetic improvement was achieved long before the mechanism of inheritance was understood. Despite shortcomings in our understanding of quantitative genetic variation (e.g. the search for the missing heritability), these methods are highly effective. Furthermore, these methods of genetic evaluation use no explicit knowledge of individual gene action. The question remains: 'Will such knowledge make predictions more accurate?'
Fine-mapping the POLL locus in Brahman cattle yields the diagnostic marker CSAFG29
The POLL locus has been mapped to the centromeric region of bovine chromosome 1 (BTA1) in both taurine breeds and taurine–indicine crosses in an interval of approximately 1 Mb. It has not yet been mapped in pure-bred zebu cattle. Despite several efforts, neither causative mutations in candidate genes nor a singular diagnostic DNA marker has been identified. In this study, we genotyped a total of 68 Brahman cattle and 20 Hereford cattle informative for the POLL locus for 33 DNA micro satellites, 16 of which we identified de novo from the bovine genome sequence, mapping the POLL locus to the region of the genes IFNAR2 and SYNJ1. The 303-bp allele of the new micro satellite, CSAFG29, showed strong association with the POLL allele. We then genotyped 855 Brahman cattle for CSAFG29 and confirmed the association between the 303-bp allele and POLL. To determine whether the same association was found in taurine breeds, we genotyped 334 animals of the Angus, Hereford and Limousin breeds and 376 animals of the Brangus, Drought master and Santa Gertrudis composite taurine–zebu breeds. The association between the 303-bp allele and POLL was confirmed in these breeds; however, an additional allele (305 bp) was also associated but not fully predictive of POLL. Across the data, CSAFG29 was in sufficient linkage disequilibrium to the POLL allele in Australian Brahman cattle that it could potentially be used as a diagnostic marker in that breed, but this may not be the case in other breeds. Further, we provide confirmatory evidence that the scur phenotype generally occurs in animals that are heterozygous for the POLL allele.
Combining two markov chain monte carlo approaches for linkage and association studies with a complex pedigree and multi marker loci
In QTL mapping using linkage and/or linkage disequilibrium, an important process is to find the pattern of inheritance states and haplotype configurations, a process known as haplotype reconstruction. Haplotype reconstruction is routinely based upon observed pedigree information and marker genotypes for individuals in the pedigree. It is not feasible for the exact methods to use all such information for large complex pedigree especially when there are many missing genotypes. Markov Chain Monte Carlo (MCMC) approaches have been widely used to handle a complex pedigree with sparse genotypic data. However they often have reducibility problems or are slow to converge. Combining two different MCMC approaches results in improvement of computational speed and mixing properties. It allows obtaining reliable estimates such as identity by descent coefficients between individuals within a reasonable time.
Population stratification, not genotype error, causes some SNPs to depart from Hardy-Weinberg Equilibrium
Large scale whole genome scans generate massive amounts of genotype data. It is essential to check genotype integrity and identify genotype errors prior to association analysis. Departure from Hardy-Weinberg Equilibrium has been adopted as one of the main methods to identify genotype errors. However population stratification also causes departure from Hardy-Weinberg Equilibrium, which is a disadvantage of this approach. This study used 2 sets of SNP genotypes to show that after basic editing using Call Rate and minor allele frequency, up to 13% of SNPs departed from Hardy-Weinberg Equilibrium (HWD) and about one third of these HWD SNPs could be falsely identified as genotype errors, were attributable to population subdivision (eg herd of origin, cohort) for one dataset and corresponding numbers for the second dataset are 21% and 16%, respectively. This approach can avoid improper culling of a considerable proportion of SNPs.
Single nucleotide polymorphisms in suppressor of cytokine signalling-2 gene and association with feed conversion ratio and growth in pigs
The Suppressor of Cytokine Signalling-2 (SOCS2) is the main negative regulator of somatic growth through the mediation of growth hormone signalling (GH/IGF-1). Knock-out and naturally mutant mice have high growth phenotypes. We have mapped the porcine SOCS2 gene to chromosome 5q, located closely to a reported QTL for food conversion ratio (Lee et al., 2003). Here we report five single nucleotide polymorphisms identified by sequencing of the promoter region and exon 1. One PCR-RFLP assay was designed for genotyping the polymorphism at position 1667(A/G). Association analyses were performed in an Australian mapping resource pedigree (PRDC-US43) for a number of traits (feed conversion ratio, backfat, IGF-1 level and growth traits) and showed significant effects on average daily gain on test (ADG2) (p<0.01) and marginal association with feed conversion ratio (FCR) (p<0.08).