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Forutan M, Engle BN, Chamberlain AJ, Ross EM, Nguyen LT, D'Occhio MJ, Snr AC, Kho EA, Fordyce G, Speight S, Goddard ME, Hayes BJ. Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility. Commun Biol 2024; 7:724. [PMID: 38866948 PMCID: PMC11169601 DOI: 10.1038/s42003-024-06403-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals.
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Affiliation(s)
- Mehrnush Forutan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Bailey N Engle
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- USDA,ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Amanda J Chamberlain
- Agriculture Victoria, Centre for AgriBiosciences, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Loan T Nguyen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Michael J D'Occhio
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia
| | - Alf Collins Snr
- Collins Belah Valley Brahman Stud, Marlborough, 4705, QLD, Australia
| | - Elise A Kho
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Geoffry Fordyce
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | | | - Michael E Goddard
- Agriculture Victoria, Centre for AgriBiosciences, Bundoora, VIC, Australia
- University of Melbourne, Melbourne, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
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Lamb HJ, Nguyen LT, Copley JP, Engle BN, Hayes BJ, Ross EM. Imputation strategies for genomic prediction using nanopore sequencing. BMC Biol 2023; 21:286. [PMID: 38066581 PMCID: PMC10709982 DOI: 10.1186/s12915-023-01782-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies' (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. RESULTS SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays. CONCLUSIONS Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.
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Affiliation(s)
- H J Lamb
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia.
| | - L T Nguyen
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - J P Copley
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - B N Engle
- USDA, ARS, U.S. Meat Animal Research Centre, Clay Centre, NE, 68933, USA
| | - B J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - E M Ross
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
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Costilla R, Zeng J, Al Kalaldeh M, Swaminathan M, Gibson JP, Ducrocq V, Hayes BJ. Developing flexible models for genetic evaluations in smallholder crossbred dairy farms. J Dairy Sci 2023; 106:9125-9135. [PMID: 37678792 PMCID: PMC10772325 DOI: 10.3168/jds.2022-23135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 07/07/2023] [Indexed: 09/09/2023]
Abstract
The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.
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Affiliation(s)
- R Costilla
- AgResearch Limited, Ruakura Research Centre, Hamilton 3214, New Zealand; Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD 4067, Australia.
| | - J Zeng
- Institute for Molecular Biosciences, University of Queensland, St. Lucia, QLD 4067, Australia
| | - M Al Kalaldeh
- Centre for Genetic Analysis and Applications, School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia
| | - M Swaminathan
- BAIF Development Research Foundation, Pune 412 202, Maharashtra, India
| | - J P Gibson
- Centre for Genetic Analysis and Applications, School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia
| | - V Ducrocq
- Universite Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France
| | - B J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD 4067, Australia
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Warburton CL, Costilla R, Engle BN, Moore SS, Corbet NJ, Fordyce G, McGowan MR, Burns BM, Hayes BJ. Concurrently mapping quantitative trait loci associations from multiple subspecies within hybrid populations. Heredity (Edinb) 2023; 131:350-360. [PMID: 37798326 PMCID: PMC10673866 DOI: 10.1038/s41437-023-00651-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 10/07/2023] Open
Abstract
Many of the world's agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies, Bos taurus indicus (Bos indicus) and Bos taurus taurus (Bos taurus). Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.
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Affiliation(s)
- Christie L Warburton
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia.
| | - Roy Costilla
- Agresearch Limited, Ruakura Research Centre, Hamilton, 3214, New Zealand
| | - Bailey N Engle
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Stephen S Moore
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Nicholas J Corbet
- Formerly Central Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, QLD, Australia
| | - Geoffry Fordyce
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Michael R McGowan
- The University of Queensland, School of Veterinary Science, St Lucia, QLD, Australia
| | - Brian M Burns
- Formerly Department of Agriculture and Fisheries, Rockhampton, QLD, Australia
| | - Ben J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
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Hayes BJ, Copley J, Dodd E, Ross EM, Speight S, Fordyce G. Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available. Genet Sel Evol 2023; 55:71. [PMID: 37845626 PMCID: PMC10578004 DOI: 10.1186/s12711-023-00847-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 10/04/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. RESULTS Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. CONCLUSIONS When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.
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Affiliation(s)
- Ben J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia.
| | - James Copley
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia
| | - Elsie Dodd
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia
| | - Elizabeth M Ross
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia
| | - Shannon Speight
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia
- BlackBox Co, Mareeba, QLD, 4880, Australia
| | - Geoffry Fordyce
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4067, Australia
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Tan WLA, Neto LRP, Reverter A, McGowan M, Fortes MRS. Sequence level genome-wide associations for bull production and fertility traits in tropically adapted bulls. BMC Genomics 2023; 24:365. [PMID: 37386436 DOI: 10.1186/s12864-023-09475-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 06/21/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND The genetics of male fertility is complex and not fully understood. Male subfertility can adversely affect the economics of livestock production. For example, inadvertently mating bulls with poor fertility can result in reduced annual liveweight production and suboptimal husbandry management. Fertility traits, such as scrotal circumference and semen quality are commonly used to select bulls before mating and can be targeted in genomic studies. In this study, we conducted genome-wide association analyses using sequence-level data targeting seven bull production and fertility traits measured in a multi-breed population of 6,422 tropically adapted bulls. The beef bull production and fertility traits included body weight (Weight), body condition score (CS), scrotal circumference (SC), sheath score (Sheath), percentage of normal spermatozoa (PNS), percentage of spermatozoa with mid-piece abnormalities (MP) and percentage of spermatozoa with proximal droplets (PD). RESULTS After quality control, 13,398,171 polymorphisms were tested for their associations with each trait in a mixed-model approach, fitting a multi-breed genomic relationship matrix. A Bonferroni genome-wide significance threshold of 5 × 10- 8 was imposed. This effort led to identifying genetic variants and candidate genes underpinning bull fertility and production traits. Genetic variants in Bos taurus autosome (BTA) 5 were associated with SC, Sheath, PNS, PD and MP. Whereas chromosome X was significant for SC, PNS, and PD. The traits we studied are highly polygenic and had significant results across the genome (BTA 1, 2, 4, 6, 7, 8, 11, 12, 14, 16, 18, 19, 23, 28, and 29). We also highlighted potential high-impact variants and candidate genes associated with Scrotal Circumference (SC) and Sheath Score (Sheath), which warrants further investigation in future studies. CONCLUSION The work presented here is a step closer to identifying molecular mechanisms that underpin bull fertility and production. Our work also emphasises the importance of including the X chromosome in genomic analyses. Future research aims to investigate potential causative variants and genes in downstream analyses.
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Affiliation(s)
- Wei Liang Andre Tan
- School of Chemistry and Molecular Biosciences, The University of Queensland, Chemistry Bld, 68 Cooper Rd, Brisbane City, QLD, 4072, Australia.
| | | | - Antonio Reverter
- CSIRO Agriculture and Food, 306 Carmody Road, St Lucia, QLD, 4067, Australia
| | - Michael McGowan
- School of Veterinary Science, The University of Queensland, Gatton, QLD, 4343, Australia
| | - Marina Rufino Salinas Fortes
- School of Chemistry and Molecular Biosciences, The University of Queensland, Chemistry Bld, 68 Cooper Rd, Brisbane City, QLD, 4072, Australia
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Esrafili Taze Kand Mohammaddiyeh M, Rafat SA, Shodja J, Javanmard A, Esfandyari H. Selective genotyping to implement genomic selection in beef cattle breeding. Front Genet 2023; 14:1083106. [PMID: 37007975 PMCID: PMC10064214 DOI: 10.3389/fgene.2023.1083106] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped (i.e., 3% of total animals in genetic evaluation). The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes.
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Affiliation(s)
| | - Seyed Abbas Rafat
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
- *Correspondence: Maryam Esrafili Taze Kand Mohammaddiyeh, ; Seyed Abbas Rafat,
| | - Jalil Shodja
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
| | - Arash Javanmard
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
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Ríos-Utrera Á, Montaño-Bermúdez M, Baeza-Rodríguez JJ, Vega-Murillo VE, Calderón-Chagoya R, Ruiz YG, Martínez-Velázquez G. Genetic relationships among yearling weight, frame score and fertility traits in Charolais heifers. Livest Sci 2023. [DOI: 10.1016/j.livsci.2023.105212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Porto-Neto LR, Alexandre PA, Hudson NJ, Bertram J, McWilliam SM, Tan AWL, Fortes MRS, McGowan MR, Hayes BJ, Reverter A. Multi-breed genomic predictions and functional variants for fertility of tropical bulls. PLoS One 2023; 18:e0279398. [PMID: 36701372 PMCID: PMC9879470 DOI: 10.1371/journal.pone.0279398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 12/07/2022] [Indexed: 01/27/2023] Open
Abstract
Worldwide, most beef breeding herds are naturally mated. As such, the ability to identify and select fertile bulls is critically important for both productivity and genetic improvement. Here, we collected ten fertility-related phenotypes for 6,063 bulls from six tropically adapted breeds. Phenotypes were comprised of four bull conformation traits and six traits directly related to the quality of the bull's semen. We also generated high-density DNA genotypes for all the animals. In total, 680,758 single nucleotide polymorphism (SNP) genotypes were analyzed. The genomic correlation of the same trait observed in different breeds was positive for scrotal circumference and sheath score on most breed comparisons, but close to zero for the percentage of normal sperm, suggesting a divergent genetic background for this trait. We confirmed the importance of a breed being present in the reference population to the generation of accurate genomic estimated breeding values (GEBV) in an across-breed validation scenario. Average GEBV accuracies varied from 0.19 to 0.44 when the breed was not included in the reference population. The range improved to 0.28 to 0.59 when the breed was in the reference population. Variants associated with the gene HDAC4, six genes from the spermatogenesis-associated (SPATA) family of proteins, and 29 transcription factors were identified as candidate genes. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems. This study also improves our understanding of the molecular basis of male fertility in mammals.
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Affiliation(s)
| | | | - Nicholas J. Hudson
- School of Animal Studies, The University of Queensland, Gatton, QLD, Australia
| | - John Bertram
- Agriculture Consultant, Livestock Management and Breeding, Toowoomba, QLD, Australia
| | | | - Andre W. L. Tan
- School of Chemistry and Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Marina R. S. Fortes
- School of Chemistry and Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Michael R. McGowan
- School of Veterinary Sciences, The University of Queensland, Gatton, QLD, Australia
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
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Yan X, Zhang T, Liu L, Yu Y, Yang G, Han Y, Gong G, Wang F, Zhang L, Liu H, Li W, Yan X, Mao H, Li Y, Du C, Li J, Zhang Y, Wang R, Lv Q, Wang Z, Zhang J, Liu Z, Wang Z, Su R. Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study. Front Vet Sci 2022; 9:770539. [PMID: 35372544 PMCID: PMC8966406 DOI: 10.3389/fvets.2022.770539] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.
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Affiliation(s)
- Xiaochun Yan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Tao Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
- Inner Mongolia Bigvet Co., Ltd., Hohhot, China
| | - Lichun Liu
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, China
| | - Yongsheng Yu
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Guang Yang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Yaqian Han
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Gao Gong
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Fenghong Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Lei Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Hongfu Liu
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Wenze Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Xiaomin Yan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Haoyu Mao
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Yaming Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Chen Du
- Department of Obstetrics and Gynaecology, Inner Mongolia Medical University, Hohhot, China
| | - Jinquan Li
- Key Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture, Hohhot, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction in Inner Mongolia Autonomous Region, Hohhot, China
- Engineering Research Centre for Goat Genetics and Breeding, Inner Mongolia Autonomous Region, Hohhot, China
| | - Yanjun Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Ruijun Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Qi Lv
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Zhixin Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Jiaxin Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Zhihong Liu
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Zhiying Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
- *Correspondence: Zhiying Wang
| | - Rui Su
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
- Rui Su
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Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers. Animals (Basel) 2022; 12:ani12020174. [PMID: 35049797 PMCID: PMC8772995 DOI: 10.3390/ani12020174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 12/16/2022] Open
Abstract
Simple Summary Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs. Abstract Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.
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Olasege BS, Tahir MS, Gouveia GC, Kour J, Porto-Neto LR, Hayes BJ, Fortes MRS. Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an21097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
Studies have shown that favourable genetic correlations exist between female and male fertility traits. However, investigations regarding these correlations in Australian tropical beef cattle are limited to either pedigree or single-breed analysis.
Aim
The study aims to use genomic information to estimate genetic parameters of six female and seven male fertility traits measured during the first 2 years of life, in two tropical breeds.
Methods
Single-, bivariate and multi-trait models were used to analyse fertility data from Brahman (BB; 996 cows and 1022 bulls); and Tropical Composite (TC; 1091 cows and 998 bulls) cattle genotyped with high-density single-nucleotide polymorphism chip assay.
Key results
Heritability estimates in BB cows ranged from low (0.07 ± 0.04) for days to calving at the first calving opportunity (DC1, days) to high (0.57 ± 0.08) for age at first corpus luteum (AGECL, days). In BB bulls, estimates varied from low (0.09 ± 0.05) for sperm motility (score 1–5) to high (0.64 ± 0.06) for scrotal circumference (SC) measured at 24 months (SC24, cm). Similarly, heritability estimates in TC cows were low (0.04 ± 0.03) for DC1 and high (0.69 ± 0.02) for AGECL. In TC bulls, the heritability was low (0.09 ± 0.05) for sperm motility and high (0.69 ± 0.07) for SC24. Within-sex for both breeds, blood concentrations of insulin growth-factor 1 (IGF1) measured in cows at 18 months (IGF1c) were negatively correlated with female fertility phenotypes. In BB, across-sex, bulls’ blood concentration of IGF1 measured at 6 months (IGF1b) was a good indicator trait for the following four female traits: AGECL, the first postpartum anoestrus interval, age at first calving and DC1. In TC, IGF1b and percentage normal sperm were good predictors of female fertility phenotypes.
Conclusions
The heritability estimates and genomic correlations from the present study generally support and confirmed the earlier estimates from pedigree analyses. The findings suggest that selection for female fertility traits will benefit male fertility, and vice versa.
Implications
Heritability estimates and genomic correlations suggest that we can select for fertility traits measured early in life, with benefits within and across sex. Using traits available through veterinary assessment of bull fertility as selection indicators will enhance bull and cow fertility, which can lead to better breeding rates in tropical herds.
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Warburton CL, Costilla R, Engle BN, Corbet NJ, Allen JM, Fordyce G, McGowan MR, Burns BM, Hayes BJ. Breed-adjusted genomic relationship matrices as a method to account for population stratification in multibreed populations of tropically adapted beef heifers. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an21057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
Beef cattle breeds in Australia can broadly be broken up into two subspecies, namely, Bos indicus and Bos taurus. Due to the time since divergence between the subspecies, it is likely that mutations affecting quantitative traits have developed independently in each.
Aims
We hypothesise that this will affect the prediction accuracy of genomic selection of admixed and composite populations that include both ancestral subspecies. Our study investigates methods to quantify population stratification in a multibreed population of tropically adapted heifers, with the aim of improving prediction accuracy of genomic selection for reproductive maturity score.
Methods
We used genotypes and reproductive maturity phenotypes from 3695 tropically adapted heifers from three purebred populations, namely, Brahman, Santa Gertrudis and Droughtmaster. Two of these breeds, Santa Gertrudis and Droughtmaster, are stabilised composites of varying B. indicus × B. taurus ancestry, and the third breed, Brahman, has predominately B. indicus ancestry. Genotypes were imputed to three marker-panel densities and population stratification was accounted for in genomic relationship matrices by using breed-specific allele frequencies when calculating the genomic relationships among animals. Prediction accuracy and bias were determined using a five-fold cross validation of randomly selected multibreed cohorts.
Key Results
Our results showed that the use of breed-adjusted genomic relationship matrices did not improve either prediction accuracy or bias for a lowly heritable trait such as reproductive maturity score. However, using breed-adjusted genomic relationship matrices allowed the capture of a higher proportion of additive genetic effects when estimating variance components.
Conclusions
These findings suggest that, despite seeing no improvement in prediction accuracy, it may still be beneficial to use breed-adjusted genomic relationship matrices in multibreed populations to improve the estimation of variance components.
Implications
As such, genomic evaluations using breed-adjusted genomic relationship matrices may be beneficial in multibreed populations.
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Rio S, Moreau L, Charcosset A, Mary-Huard T. Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize. Genetics 2020; 216:27-41. [PMID: 32680885 PMCID: PMC7463286 DOI: 10.1534/genetics.120.303278] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/10/2020] [Indexed: 02/01/2023] Open
Abstract
Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.
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Affiliation(s)
- Simon Rio
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
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16
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Costilla R, Kemper KE, Byrne EM, Porto-Neto LR, Carvalheiro R, Purfield DC, Doyle JL, Berry DP, Moore SS, Wray NR, Hayes BJ. Genetic control of temperament traits across species: association of autism spectrum disorder risk genes with cattle temperament. Genet Sel Evol 2020; 52:51. [PMID: 32842956 PMCID: PMC7448488 DOI: 10.1186/s12711-020-00569-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/07/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Temperament traits are of high importance across species. In humans, temperament or personality traits correlate with psychological traits and psychiatric disorders. In cattle, they impact animal welfare, product quality and human safety, and are therefore of direct commercial importance. We hypothesized that genetic factors that contribute to variation in temperament among individuals within a species will be shared between humans and cattle. Using imputed whole-genome sequence data from 9223 beef cattle from three cohorts, a series of genome-wide association studies was undertaken on cattle flight time, a temperament phenotype measured as the time taken for an animal to cover a short-fixed distance after release from an enclosure. We also investigated the association of cattle temperament with polymorphisms in bovine orthologs of risk genes for neuroticism, schizophrenia, autism spectrum disorders (ASD), and developmental delay disorders in humans. RESULTS Variants with the strongest associations were located in the bovine orthologous region that is involved in several behavioural and cognitive disorders in humans. These variants were also partially validated in independent cattle cohorts. Genes in these regions (BARHL2, NDN, SNRPN, MAGEL2, ABCA12, KIFAP3, TOPAZ1, FZD3, UBE3A, and GABRA5) were enriched for the GO term neuron migration and were differentially expressed in brain and pituitary tissues in humans. Moreover, variants within 100 kb of ASD susceptibility genes were associated with cattle temperament and explained 6.5% of the total additive genetic variance in the largest cattle cohort. The ASD genes with the most significant associations were GABRB3 and CUL3. Using the same 100 kb window, a weak association was found with polymorphisms in schizophrenia risk genes and no association with polymorphisms in neuroticism and developmental delay disorders risk genes. CONCLUSIONS Our analysis showed that genes identified in a meta-analysis of cattle temperament contribute to neuron development functions and are differentially expressed in human brain tissues. Furthermore, some ASD susceptibility genes are associated with cattle temperament. These findings provide evidence that genetic control of temperament might be shared between humans and cattle and highlight the potential for future analyses to leverage results between species.
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Affiliation(s)
- Roy Costilla
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Enda M. Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Laercio R. Porto-Neto
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food, Brisbane, Australia
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, Sao Paulo State University, Sao Paolo, Brazil
| | | | - Jennifer L. Doyle
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork Ireland
| | - Donagh P. Berry
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork Ireland
| | - Stephen S. Moore
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
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17
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Naval-Sánchez M, Porto-Neto LR, Cardoso DF, Hayes BJ, Daetwyler HD, Kijas J, Reverter A. Selection signatures in tropical cattle are enriched for promoter and coding regions and reveal missense mutations in the damage response gene HELB. Genet Sel Evol 2020; 52:27. [PMID: 32460767 PMCID: PMC7251699 DOI: 10.1186/s12711-020-00546-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 05/11/2020] [Indexed: 01/14/2023] Open
Abstract
Background Distinct domestication events, adaptation to different climatic zones, and divergent selection in productive traits have shaped the genomic differences between taurine and indicine cattle. In this study, we assessed the impact of artificial selection and environmental adaptation by comparing whole-genome sequences from European taurine and Asian indicine breeds and from African cattle. Next, we studied the impact of divergent selection by exploiting predicted and experimental functional annotation of the bovine genome. Results We identified selective sweeps in beef cattle taurine and indicine populations, including a 430-kb selective sweep on indicine cattle chromosome 5 that is located between 47,670,001 and 48,100,000 bp and spans five genes, i.e. HELB, IRAK3, ENSBTAG00000026993, GRIP1 and part of HMGA2. Regions under selection in indicine cattle display significant enrichment for promoters and coding genes. At the nucleotide level, sites that show a strong divergence in allele frequency between European taurine and Asian indicine are enriched for the same functional categories. We identified nine single nucleotide polymorphisms (SNPs) in coding regions that are fixed for different alleles between subspecies, eight of which were located within the DNA helicase B (HELB) gene. By mining information from the 1000 Bull Genomes Project, we found that HELB carries mutations that are specific to indicine cattle but also found in taurine cattle, which are known to have been subject to indicine introgression from breeds, such as N’Dama, Anatolian Red, Marchigiana, Chianina, and Piedmontese. Based on in-house genome sequences, we proved that mutations in HELB segregate independently of the copy number variation HMGA2-CNV, which is located in the same region. Conclusions Major genomic sequence differences between Bos taurus and Bos indicus are enriched for promoter and coding regions. We identified a 430-kb selective sweep in Asian indicine cattle located on chromosome 5, which carries SNPs that are fixed in indicine populations and located in the coding sequences of the HELB gene. HELB is involved in the response to DNA damage including exposure to ultra-violet light and is associated with reproductive traits and yearling weight in tropical cattle. Thus, HELB likely contributed to the adaptation of tropical cattle to their harsh environment.
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Affiliation(s)
- Marina Naval-Sánchez
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia. .,Institute of Molecular Biosciences, The University of Queensland, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4067, Australia.
| | - Laercio R Porto-Neto
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
| | - Diercles F Cardoso
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia.,Department of Animal Science, School of Agricultural and Veterinarian Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil.,Centre for Genetic Improvement of Livestock, University of Guelph, 50 Stone Road East, Guelph, ON, N1G2W1, Canada
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - James Kijas
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
| | - Antonio Reverter
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
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18
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Warburton CL, Engle BN, Ross EM, Costilla R, Moore SS, Corbet NJ, Allen JM, Laing AR, Fordyce G, Lyons RE, McGowan MR, Burns BM, Hayes BJ. Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers. Genet Sel Evol 2020; 52:28. [PMID: 32460805 PMCID: PMC7251835 DOI: 10.1186/s12711-020-00547-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 05/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Methods Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. Results In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. Conclusions While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.
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Affiliation(s)
- Christie L Warburton
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia.
| | - Bailey N Engle
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Elizabeth M Ross
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Roy Costilla
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Stephen S Moore
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Nicholas J Corbet
- School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia
| | - Jack M Allen
- Agricultural Business Research Institute, University of New England, Armidale, NSW, Australia
| | - Alan R Laing
- Formerly Department of Agriculture and Fisheries, Ayr, QLD, Australia
| | - Geoffry Fordyce
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Russell E Lyons
- School of Veterinary Science, The University of Queensland, St Lucia, QLD, Australia.,Neogen, University of Queensland, Gatton, QLD, Australia
| | - Michael R McGowan
- School of Veterinary Science, The University of Queensland, St Lucia, QLD, Australia
| | - Brian M Burns
- Formerly Department of Agriculture and Fisheries, Rockhampton, QLD, Australia
| | - Ben J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
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19
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Genetic parameters for growth and cow productivity traits in Angus, Hereford and crossbred cattle. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.103952] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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20
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Zhang H, Yin L, Wang M, Yuan X, Liu X. Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations. Front Genet 2019; 10:189. [PMID: 30923535 PMCID: PMC6426750 DOI: 10.3389/fgene.2019.00189] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 02/21/2019] [Indexed: 11/20/2022] Open
Abstract
Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs.
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Affiliation(s)
- Haohao Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Meiyue Wang
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Xiaohui Yuan
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
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21
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Engle BN, Corbet NJ, Allen JM, Laing AR, Fordyce G, McGowan MR, Burns BM, Lyons RE, Hayes BJ. Multivariate genomic predictions for age at puberty in tropically adapted beef heifers. J Anim Sci 2019; 97:90-100. [PMID: 30481306 DOI: 10.1093/jas/sky428] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 11/06/2018] [Indexed: 11/14/2022] Open
Abstract
Heifers that have an earlier age at puberty often have greater lifetime productivity. Age at puberty is moderately heritable so selection should effectively reduce the number of days to puberty, and improve heifer productivity and profitability as a result. However, recording age at puberty is intensive, requiring repeat ovarian scanning to determine age at first corpus luteum (AGECL). Genomic selection has been proposed as a strategy to select for earlier age at puberty; however, large reference populations of cows with AGECL records and genotypes would be required to generate accurate GEBV for this trait. Reproductive maturity score (RMS) is a proxy trait for age at puberty for implementation in northern Australia beef herds, where large scale recording of AGECL is not feasible. RMS assigns a score of 0 to 5 from a single ovarian scan to describe ovarian maturity at ~600 d. Here we use multivariate genomic prediction to evaluate the value of a large RMS data set to improve accuracy of GEBV for age at puberty (AGECL). There were 882 Brahman and 990 Tropical Composite heifers with AGECL phenotypes, and an independent set of 974 Brahman, 1,798 Santa Gertrudis, and 910 Droughtmaster heifers with RMS phenotypes. All animals had 728,785 real or imputed SNP genotypes. The correlation of AGECL and RMS (h2 = 0.23) was estimated as -0.83 using the genomic information. This result also demonstrates that using genomic information it is possible to estimate genetic correlations between traits collected on different animals in different herds, with minimal or unknown pedigree linkage between them. Inclusion of heifers with RMS in the multi-trait model improved the accuracy of genomic evaluations for AGECL. Accuracy of RMS GEBV generally did not improve by adding heifers with AGECL phenotypes into the reference population. These results suggest that RMS and AGECL may be used together in a multi-trait prediction model to increase the accuracy of prediction for age at puberty in tropically adapted beef cattle.
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Affiliation(s)
- Bailey N Engle
- Department of Animal Science, Texas A&M University, College Station, TX
| | - Nicholas J Corbet
- Central Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, QLD, Australia
| | - Jamie M Allen
- Agricultural Business Research Institute, University of New England, Armidale, NSW, Australia
| | - Alan R Laing
- Department of Agriculture and Fisheries, Ayr, QLD, Australia
| | - Geoffry Fordyce
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, QLD, Australia
| | - Michael R McGowan
- University of Queensland, School of Veterinary Science, Gatton, QLD, Australia
| | - Brian M Burns
- Department of Agriculture and Fisheries, Ayr, QLD, Australia
| | - Russell E Lyons
- NeoGen, University of Queensland, School of Veterinary Science, Gatton, QLD, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, QLD, Australia
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