M. Kelly

Genome-wide association analyses for growth and feed efficiency traits in beef cattle.

A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Genome-wide association study for inhibin, luteinizing hormone, insulin-like growth factor 1, testicular size and semen traits in bovine species.

The fertility of young bulls impacts on reproduction rates, farm profit and the rate of genetic progress in beef herds. Cattle researchers and industry therefore routinely collect data on the reproductive performance of bulls. Genome‐wide association studies were carried out to identify genomic regions and genes associated with reproductive traits measured during the pubertal development of Tropical Composite bulls, from 4 to 24 months of age. Data from 1 085 bulls were collected for seven traits: blood hormone levels of inhibin at 4 months (IN), luteinizing hormone following a gonadotropin releasing hormone challenge at 4 months (LH), insulin‐like growth factor 1 at 6 months (IGF1), scrotal circumference at 12 months (SC), sperm motility at 18 months (MOT), percentage of normal spermatozoa at 24 months (PNS) and age at a scrotal circumference of 26 cm (AGE26, or pubertal age). Data from 729 068 single‐nucleotide polymorphisms were used in the association analysis. Significant polymorphism associations were discovered for IN, IGF1, SC, AGE26 and PNS. Based on these associations, INHBE, INHBC and HELB are proposed as candidate genes for IN regulation. Polymorphisms associated with IGF1 mapped to the PLAG1 gene region, validating a reported quantitative trait locus on chromosome 14 for IGF1. The X chromosome contained most of the significant associations found for SC, AGE26 and PNS. These findings will contribute to the identification of diagnostic genetic markers and informed genomic selection strategies to assist breeding of cattle with improved fertility. Furthermore, this work provides evidence contributing to gene function annotation in the context of male fertility.

Genomic predictions in Angus cattle: comparisons of sample size, response variables, and clustering methods for cross-validation.

Advances in genomics, molecular biology, and statistical genetics have created a paradigm shift in the way livestock producers pursue genetic improvement in their herds. The nexus of these technologies has resulted in combining genotypic and phenotypic information to compute genomically enhanced measures of genetic merit of individual animals. However, large numbers of genotyped and phenotyped animals are required to produce robust estimates of the effects of SNP that are summed together to generate direct genomic breeding values (DGV). Data on 11,756 Angus animals genotyped with the Illumina BovineSNP50 Beadchip were used to develop genomic predictions for 17 traits reported by the American Angus Association through Angus Genetics Inc. in their National Cattle Evaluation program. Marker effects were computed using a 5-fold cross-validation approach and a Bayesian model averaging algorithm. The accuracies were examined with EBV and deregressed EBV (DEBV) response variables and with K-means and identical by state (IBS)-based cross-validation methodologies. The cross-validation accuracies obtained using EBV response variables were consistently greater than those obtained using DEBV (average correlations were 0.64 vs. 0.57). The accuracies obtained using K-means cross-validation were consistently smaller than accuracies obtained with the IBS-based cross-validation approach (average correlations were 0.58 vs. 0.64 with EBV used as a response variable). Comparing the results from the current study with the results from a similar study consisting of only 2,253 records indicated that larger training population size resulted in higher accuracies in validation animals and explained on average 18% (69% improvement) additional genetic variance across all traits.

A marker-derived gene network reveals the regulatory role of PPARGC1A, HNF4G, and FOXP3 in intramuscular fat deposition of beef cattle

High intramuscular fat (IMF) awards price premiums to beef producers and is associated with meat quality and flavor. Studying gene interactions and pathways that affect IMF might unveil causative physiological mechanisms and inform genomic selection, leading to increased accuracy of predictions of breeding value. To study gene interactions and pathways, a gene network was derived from genetic markers associated with direct measures of IMF, other fat phenotypes, feedlot performance, and a number of meat quality traits relating to body conformation, development, and metabolism that might be plausibly expected to interact with IMF biology. Marker associations were inferred from genomewide association studies (GWAS) based on high density genotypes and 29 traits measured on 10,181 beef cattle animals from 3 breed types. For the network inference, SNP pairs were assessed according to the strength of the correlation between their additive association effects across the 29 traits. The co-association inferred network was formed by 2,434 genes connected by 28,283 edges. Topological network parameters suggested a highly cohesive network, in which the genes are strongly functionally interconnected. Pathway and network analyses pointed towards a trio of transcription factors (TF) as key regulators of carcass IMF: PPARGC1A, HNF4G, and FOXP3. Importantly, none of these genes would have been deemed as significantly associated with IMF from the GWAS. Instead, a total of 313 network genes show significant co-association with the 3 TF. These genes belong to a wide variety of biological functions, canonical pathways, and genetic networks linked to IMF-related phenotypes. In summary, our GWAS and network predictions are supported by the current literature and suggest a cooperative role for the 3 TF and other interacting genes including CAPN6, STC2, MAP2K4, EYA1, COPS5, XKR4, NR2E1, TOX, ATF1, ASPH, TGS1, and TTPA as modulators of carcass and meat quality traits in beef cattle.

Genetic variation in fatty acid composition of subcutaneous fat in cattle

Genetic parameters were estimated for fatty acid composition of subcutaneous beef fat of 1573 animals which were the progeny of 157 sires across seven breeds grown out on pasture and then finished on either grain or grass in northern New South Wales or in central Queensland. There was genetic variation in individual fatty acids with estimates of heritability for the proportions of C14 : 0, C14 : 1c9, C16 : 0, C16 : 1c9, C18 : 0 and C18 : 1c9 fatty acids in subcutaneous beef fat of the order of 0.4 or above. Also substantial correlations between some fatty acids were observed. Genetic correlations between fatty acids and fat depth at the P8 site suggested that much of the genetic variation in fatty acid composition was related to changes in fatness. Selection for decreased fatness resulted in decreased proportions of C18 : 1c9 with concomitant increases in C18 : 0, C14 : 0 and C16 : 0. This suggested that selection for decreased fatness at a given weight will result in a decrease in the proportions of monounsaturated fatty acids in the subcutaneous fat in the carcass with a corresponding increase in the proportions of saturated fatty acids.

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle.

Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate genes emerging from this GWAS may inform the search for causative mutations that could underpin genomic breeding for improved growth rates.

Whole-genome association study of fatty acid composition in a diverse range of beef cattle breeds

Fatty acid composition of adipose tissue associated with meat is an important factor for the beef industry because of its implications for human health, processing, meat quality, and palatability. Individual fatty acid composition is a trait under genetic control, so improvement via selective breeding of cattle is possible. The objective of this study was to investigate the genetic architecture of fatty acid composition and identify genes associated with this trait in 3 breed types: Bos indicus (Brahman), Bos taurus (4 breeds), and tropically adapted composites (2 breeds). Using high-density data, regions on chromosomes 1, 9, 14, 16, 19, 23, 26, 29, and X were associated with fat composition and quantity traits. Known candidate genes, such as fatty acid synthase (FASN; chromosome 19) and stearoyl-CoA desaturase (SCD; chromosome 26), were confirmed in our results. Other candidate genes and regions represent novel association results, requiring further validation.

Accuracy of genomic selection for age at puberty in a multi-breed population of tropically adapted beef cattle

Genomic selection is becoming a standard tool in livestock breeding programs, particularly for traits that are hard to measure. Accuracy of genomic selection can be improved by increasing the quantity and quality of data and potentially by improving analytical methods. Adding genotypes and phenotypes from additional breeds or crosses often improves the accuracy of genomic predictions but requires specific methodology. A model was developed to incorporate breed composition estimated from genotypes into genomic selection models. This method was applied to age at puberty data in female beef cattle (as estimated from age at first observation of a corpus luteum) from a mix of Brahman and Tropical Composite beef cattle. In this dataset, the new model incorporating breed composition did not increase the accuracy of genomic selection. However, the breeding values exhibited slightly less bias (as assessed by deviation of regression of phenotype on genomic breeding values from the expected value of 1). Adding additional Brahman animals to the Tropical Composite analysis increased the accuracy of genomic predictions and did not affect the accuracy of the Brahman predictions.

Genome‐wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle

We performed a genome-wide mapping for the age at first calving (AFC) with the goal of annotating candidate genes that regulate fertility in Nellore cattle. Phenotypic data from 762 cows and 777k SNP genotypes from 2,992 bulls and cows were used. Single nucleotide polymorphism (SNP) effects based on the single-step GBLUP methodology were blocked into adjacent windows of 1 Megabase (Mb) to explain the genetic variance. SNP windows explaining more than 0.40% of the AFC genetic variance were identified on chromosomes 2, 8, 9, 14, 16 and 17. From these windows, we identified 123 coding protein genes that were used to build gene networks. From the association study and derived gene networks, putative candidate genes (e.g., PAPPA, PREP, FER1L6, TPR, NMNAT1, ACAD10, PCMTD1, CRH, OPKR1, NPBWR1 and NCOA2) and transcription factors (TF) (STAT1, STAT3, RELA, E2F1 and EGR1) were strongly associated with female fertility (e.g., negative regulation of luteinizing hormone secretion, folliculogenesis and establishment of uterine receptivity). Evidence suggests that AFC inheritance is complex and controlled by multiple loci across the genome. As several windows explaining higher proportion of the genetic variance were identified on chromosome 14, further studies investigating the interaction across haplotypes to better understand the molecular architecture behind AFC in Nellore cattle should be undertaken.

Supervised independent component analysis as an alternative method for genomic selection in pigs

The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR-BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.