Genomic selection for female reproduction in ... - CSIRO Publishing

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Animal Production Science, 2014, 54, 16–24 http://dx.doi.org/10.1071/AN13016

Genomic selection for female reproduction in Australian tropically adapted beef cattle Y. D. Zhang A,B,F, D. J. Johnston A,B, S. Bolormaa A,C, R. J. Hawken A,D,E and B. Tier A,B A

Cooperative Research Centre for Beef Genetic Technologies. Animal Genetics and Breeding Unit,1 University of New England, Armidale, NSW 2351, Australia. C Victorian Department of Primary Industries, Bundoora, Vic. 3083, Australia. D CSIRO Livestock Industries, Queensland Bioscience Precinct, Brisbane, Qld 4067, Australia. E Present address: Cobb-Vantress, Siloam Springs, Arkansas, 72762, USA. F Corresponding author. Email: [email protected] B

Abstract. The usefulness of genomic selection was assessed for female reproduction in tropically adapted breeds in northern Australia. Records from experimental populations of Brahman (996) and Tropical Composite (1097) cattle that had had six calving opportunities were used to derive genomic predictions for several measures of female fertility. These measures included age at first corpus luteum (AGECL), at first calving and subsequent postpartum anoestrous interval and measures of early and lifetime numbers of calves born or weaned. In a second population, data on pregnancy and following status (anoestrous or pregnancy) were collected from 27 commercial herds from northern Australia to validate genomic predictions. Cows were genotyped with a variety of single nucleotide polymorphism (SNP) panels and, where necessary, genotypes imputed to the highest density (729 068 SNPs). Genetic parameters of subsets of the complete data were estimated. These subsets were used to validate genomic predictions using genomic best linear unbiased prediction using both univariate cross-validation and bivariate analyses. Estimated heritability ranged from 0.56 for AGECL to 0.03 for lifetime average calving rate in the experimental cows, and from 0.09 to 0.25 for early life reproduction traits in the commercial cows. Accuracies of predictions were generally low, reflecting the limited number of data in the experimental populations. For AGECL and postpartum anoestrous interval, the highest accuracy was 0.35 for experimental Brahman cows using five-fold univariate cross-validation. Greater genetic complexity in the Tropical Composite cows resulted in the corresponding accuracy of 0.23 for AGECL. Similar level of accuracies (from univariate and bivariate analyses) were found for some of the early measures of female reproduction in commercial cows, indicating that there is potential for genomic selection but it is limited by the number of animals with phenotypes. Received 17 January 2013, accepted 7 May 2013, published online 20 August 2013

Introduction Improvement of female reproduction remains an important issue to the northern Australian beef industry. This involves increasing the number of calves produced over the lifetime of breeding females, through reducing the age at puberty of heifers and increasing the possibility of re-conception in postpartum lactating females. Early puberty of heifers can increase calving rates (Taylor and Rudder 1986) and delayed pubertal age can increase an animal’s age at first calving by 1 year. Extended lactational anoestrous following calving can delay re-conception or reduce pregnancy rate. Age at puberty (AGECL) is moderately heritable in tropical breeds (Johnston et al. 2009). Early reproduction and lifetime reproduction were shown to be also heritable in tropical genotypes (Johnston et al. 2014). Previously, Hawken et al. (2012) carried out a whole-genome association study for AGECL and postpartum lactational anoestrous interval (PPAI) in tropical cows and found that several genetic markers

and genomic regions significantly affected these traits. These data indicated that there are elements in the bovine genome affecting several fertility-related traits. The availability of dense markers across the genome makes genomic selection a feasible approach for animal breeders. Several methods have been suggested for developing genomic prediction equations (Hayes et al. 2009a; Moser et al. 2009). Two common methods are genomic best linear unbiased prediction (GBLUP), where the genomic data determine relationships among individuals rather than the pedigree, and a variety of Bayesian methods, where single nucleotide polymorphisms (SNPs) are treated as random effects with unequal variance. Both types of methods yield similarly accurate predictions (Moser et al. 2009). The accuracy of genomic estimated breeding value (GEBV) depends on the accuracy and heritability of phenotypes and the relationships between the training and test populations. Habier

1

AGBU is a joint venture of The NSW Department of Primary Industries and University of New England.

Journal compilation CSIRO 2014

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Genomic selection for cow reproduction

Animal Production Science

et al. (2007, 2010) studied the effect of genetic relationship information on accuracy of predicted GEBVs by using simulated data and in German Holstein cattle. In their studies, the genetic relationship information was measured as the maximum additive genetic relationship (amax) between animals in the training and validation sets, and the accuracy of GEBV decreased with decreasing amax. Reported accuracies in GEBV prediction for dairy-cattle population in several countries ranged from 0.4 to 0.82 (Hayes et al. 2009b); however, large reference populations were required to develop accurate GEBVs. The aims of the present study were to develop genomic prediction equations for cattle reproduction traits measured in beef cattle from northern Australia, and to test their usefulness in an independent population. Materials and methods Animal populations Two populations of cows were involved in the study; one was an experimental population (designated as CRC cows) and the other was an independent population of industry-recorded validation cows. Details of animals in datasets and with genotypes are listed in Table 1. CRC cows The CRC cattle were from the ‘Northern Breeding Project’ resource population bred by the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) in the tropical regions of northern Australia (Burrow et al. 2003; Barwick et al. 2009). A total of 2174 females were phenotyped, of these, 2093 were genotyped with Illumina SNP chips (Illumina, San Diego, CA, USA; Table 1). Cows were derived from two breeds, namely, Brahman (BB) and Tropical Composite (TC). Briefly, TC comprised ~50% tropically adapted breeds and 50% nontropically adapted Bos taurus breeds. The tropically adapted component was on average half derived from Brahmans and the other half from tropically adapted Taurine breeds. The Bos taurus component of composites consisted of various combinations of Hereford, Shorthorn, Red Angus, Red Poll and Charolais. The cows were the progeny of 54 BB and 51 TC sires (details see Barwick et al. 2009).

Within this population, reproduction, growth and body composition of cows were recorded. These phenotypes were previously described by Johnston et al. (2009, 2010), Barwick et al. (2009) and Johnston et al. (2014). For reproduction traits (Table 2), AGECL was measured as the number of days from birth until the first observed corpus luteum (CL) on either ovary determined by regular real-time ultrasound scanning. Up to six calving occurrences were recorded. From these data, the first PPAI, postpartum ovulation status (PPO), early life reproduction and lifetime reproduction were defined (Table 2). Validation cows (VALID) In 2010, records of 4286 cows were collected from 27 herds in four tropically adapted cattle breeds, i.e. 11 Brahman (VBB), seven Santa Gertrudis (VSG), seven Droughtmaster (VDM) and two Belmont Red (VBR) herds (Table 1). Most of the cows (60%) were younger than 4 years. In these herds, bulls had generally been moved into mating paddocks a few weeks after calving and remained in the paddocks for ~4 months on average. Pregnancy status was determined at weaning, about 5 weeks after bulls had been removed. Stage of pregnancy and ovarian activity were assessed in cows at weaning by experienced operators using ultrasound scanning. Stage of pregnancy was scored by approximating fetal age to the nearest month. Any nonpregnant cows were assessed for the presence of a CL or corpus albicans (CA) on either ovary. In the absence of CL and CA, nonpregnant cows were recorded for the size of the largest follicle on either ovary. At the same time, cows were also assessed as lactating (‘wet’) or not (‘dry’). Records were used to define two reproduction traits, including REP3 (reproductive status in three classes: the absence of CL or CA as 0, CL or CA as 1 and pregnant as 2) and lactation and pregnancy status (LP, lactating and pregnant as 1, other as 0). Calving records for these cows were extracted from the herd databases. Two additional traits were defined using these records. The first is the industry number of calvings in the first two reproduction opportunities (ICR12) and the second is the industry lifetime average calving rate (ILACR). For cows with fewer than six records, the actual total numbers of calvings were used to calculate ILACR. Details for these traits are listed in Table 2. These traits correspond to similar traits in the CRC cows,

Table 1. Number of animals with phenotypes and genotypes in datasets and breeds Genotype, the number of animals genotyped in different genotyping platforms; Total, the total number of genotyped animals after imputation to 700K. See text for definition of CRC and validation cows Dataset

No. of animals with the phenotype

7K

BB TC BBTC

1035 1139 2174

49 87 136

VALID VBB VSG VDM VBR

4286 1737 1535 734 291

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No. of animals with the genotype 50K 700K Total CRC cows 821 857 1678

Description

126 153 279

996 1097 2093

CRC Brahman CRC Tropical Composite All CRC cows

Validation cows 2431 194 1124 71 807 58 419 45 81 20

2625 1195 865 464 101

All validation cows Validation Brahman Validation Santa Gertrudis Validation Droughtmaster Validation Belmont Red

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Y. D. Zhang et al.

Table 2. Trait symbols and descriptions for CRC and validation cows CA, corpus albicans; CL, corpus luteum. See text for definition of CRC and validation cows Symbol AGECL PPAI PPO CR12 WR12 LP LACR LAWR REP3 LP ICR12 ILACR

Trait description CRC cows Age at first detected CL (days) Postpartum anoestrus interval, number of days from the 1st calving to the 1st ovulation (days), this description is slightly different from that by Johnston et al. (2010) where the PPAI stated at the commencement of scanning. Postpartum ovulation status, a binary trait, defined from PPAI, the first ovulation before weaning as ‘1’ or post weaning as ‘0’ Number of calves born in Matings 1 and 2 Number of calves weaned from Matings 1 and 2 Lactating-pregnancy status, a binary trait scored as ‘1’ for a lactating and pregnant cow, otherwise as ‘0’ Lifetime average calving rate (in the first six opportunities) Lifetime average weaning rate (in the first six opportunities) Validation cows Reproductive status, scored ‘2’ for pregnant, or ‘1’ for having a CL or CA or ‘0’ for no CL or CA Lactating–pregnancy status, a binary trait, defined from REP3, lactating and pregnant as’1’, or ‘0’ otherwise Number of calves born in the 1st and 2nd opportunities Lifetime average calving rate (in the first six opportunities), for cows with fewer than 6 records, the actual total numbers of calvings were used to calculate ILACR

but cannot be considered identical as the data from industry validation herds may be incomplete. Within the validation population, 2750 cows were chosen to be genotyped. To maximise information available for the analysis of categorical traits, the priorities in selection were given to whole contemporary groups (comprised of herd, location, calving season and management group) with relatively low pregnancy rates and young cows because the older cows might have been subjected to culling. In brief, 80% of the animals chosen to be genotyped were young cows (2, 3 or 4 years old) from contemporary groups in which more than 10% of the cows were neither pregnant nor having a CL or CA; the remaining 20% were young cows (3 and 4 years old) chosen from contemporary groups in which more than 10% of the cows were not pregnant. The complete data, as well as the subsets of genotyped cows were analysed jointly across breeds and by breed. Genotypes The SNP genotype data used in the present study was a subset of the Beef CRC genomic dataset which comprised 14 668 animals and involved several breeds, including 4782 Brahman and 1853 Tropical Composite animals. Details on genotyping, editing and imputation of the Beef CRC genomic dataset has been described by Bolormaa et al. (2013). Briefly, the CRC cows were genotyped on one of three Illumina SNP chips: the Illumina 7K panel comprising 6909 SNP, the BovineSNP50K version1 BeadChip (Illumina) comprising 53 798 markers and the HD Bovine SNP chip (www.illumina.com/agriculture, verified 21 May 2013) comprising 777 963 SNP markers. Validation cows were genotyped with the BovineSNP 50K version2 BeadChip (Illumina) comprising 54 609 SNPs or the HD SNP panel. Following a stringent quality control for GenCall score, minor allele frequencies and Hardy–Weinberg disequilibrium test, the 729 068 SNPs of the HD SNP chip were retained (700K). The genotypes for each SNP were encoded in the top/bottom Illumina

A/B format and then genotypes were recoded to 0, 1 and 2 copies of the B allele. Genotypes were imputed in two stages; first, the missing genotypes in each SNP platform were filled using the BEAGLE program (Browning and Browning 2007, 2009); second, the imputations of the 7K to 50K and then 50K to 700K were carried out within breed and using 30 iterations of BEAGLE. All SNPs were mapped to the UMD 3.1 build of the bovine genome sequence assembled by the Center for Bioinformatics and Computational Biology at the University of Maryland (CBCB) (http://www.cbcb.umd.edu/research/ bos_taurus_assembly.shtml, verified 21 May 2013). As a result, 700K genotypes for 2093 CRC cows and 2625 validation cows were available for subsequent analyses (Table 1). Statistical methods Quantitative analyses Genetic parameters (i.e. variances and heritabilities) were estimated for all traits in both datasets, with all phenotypic records using pedigree-based REML. In these analyses, the pedigree, not genomic information, was used to define genetic relationships among animals. Univariate animal models were used to estimate fixed, additive genetic effects (estimated breeding value, EBV) and variance components for all traits. For the CRC cows, the Brahman and Tropical Composite breeds were analysed separately and together. The individual breed data were analysed using the following equation: y ¼ Xb þ Za þ e;

ð1Þ

where y represents the vector of observations, X is the incidence matrix relating fixed effects (e.g. cohort, month of birth, herd of origin) in b with observation in y, Z is the incidence matrix relating random additive polygenic effects in a with observation in y, and e is the vector of random residual effects. The random effects in Eqn 1 were assumed to be normally distributed with zero mean and variance as var. (a) = Asa2 and var. (e) = Ise2, where A is the

Genomic selection for cow reproduction


numerator relationship matrix across all cows and derived from the available pedigree information, sa2 is the additive polygenic component of variance; I is an identity matrix; and se2 is the residual component of variance. Solutions to the effects in Eqn 1, as well as variance components, were estimated using the ASReml software (Gilmour et al. 2009). PPO was a binary trait, but was analysed with a linear model. Fixed effects for AGECL, PPAI and other traits of CRC cows included in b have been described previously (Barwick et al. 2009; Johnston et al. 2009, 2010), traits for early and lifetime reproduction have been described by Johnston et al. (2014). For traits of validation cows, the fitted fixed effects have been described by Zhang et al. (2011). For CRC cows, adjusted values for each trait in each breed were calculated as y0 = y – Xb. All data regardless of genotyping were used to determine adjusted values. For analyses of combined two-breed data (across-breed analysis), the adjusted values were obtained as y00 = y0 – Xb, where b was estimated from the equation y0 = Xb + Za + e, where b is breed effect and a is additive genetic effect. Analysis of the complete data is referred to as the acrossbreed analysis. Adjusted values (y00 ) were used for all subsequent across-breed analyses. Genomic best linear unbiased prediction (GBLUP) GEBVs for each trait were estimated for genotyped cows only by using GBLUP. The genomic relationship matrix G was fitted as an additive random effect to estimate GEBVs. The GBLUP model was as follows: y ¼ Xb þ Zg þ e; with var: ðgÞ ¼ Gsg 2 and var: ðeÞ ¼ Ise 2 : It is similar to Eqn 1, except the relationship matrix was based on genomic information (G) rather than pedigree (A) to describe the covariance among animal breeding values (g). G was calculated from SNP genotypes using the method by Yang et al. (2010) and inverted using Wombat software (Meyer 2007). SNPs with very low minor allele frequencies (0 (i.e. 0.22 for AGECL in CRC Brahman, Table 6) were observed from pedigree-based BLUP results, suggesting that common ancestors exist between reference and test populations. Such cases seem associated with traits with high heritability. Furthermore, this situation was less obvious for


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CRC Tropical Composite cows, reflecting their more diverse background. Conclusions Reasonable accuracies of GEBVs for reproduction traits, particularly for age at puberty and early lifetime reproduction traits, were observed in the study. Thus, genomic selection can be useful for improving female fertility in tropically adapted cattle. Although the traits were lowly heritable and there was limited compatibility in traits between reference and validation populations, it is likely that the value of genomic selection for reproduction will improve as larger training datasets become available. Acknowledgements We gratefully acknowledge industry support by Australian Agricultural Company, C & R Briggs, Consolidated Pastoral Company, J & SM Halberstater, S. Kidman & Co, GE & J McCamley, MDH Pty Ltd, North Australian Pastoral Company and Stanbroke Pastoral Company for their substantial cash and in-kind contributions to the project. We also thank the owners of the industry Brahman, Droughtmaster, Santa Gertrudis and Belmond Red herds and these breed societies for their participation and providing the reproduction records and DNA samples in the validation part of this study. Meat and Livestock Australia, the Australian Centre for International Agricultural Research and the CRC for Beef Genetic Technologies also provide generous financial support. The authors also would like to thank all ultrasound operators, technical officers from CSIRO, DEEDI, UQ and AGBU for their contribution to data collection and preliminary editing. The significant efforts of other project scientists and support staff located throughout the CRC’s network who were responsible for field, feedlot, laboratory and abattoir data collection and collation, genotyping the thousands of samples and analysis of project data are also acknowledged. In particular, Paul Williams, Nicholas Corbet, Geoffrey Fordyce, Debra Corbet, Brian Burns, Richard Holroyd and Jim Cook and made invaluable contributions to phenotype collection and database management throughout the project. Efficient, reliable DNA extraction and curation as well as genotyping, imputation and data management underpinned this project were provided by Eliza Collis, Russell McCulloch, Blair Harrison, Coralie Reich, Keith Savin, Bolormaa Sunduimijid and Mike Goddard.

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