Skip to main content
NCBI home page
Frontiers in Genetics logoLink to Frontiers in Genetics
. 2021 Mar 23;12:617128. doi: 10.3389/fgene.2021.617128

Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

Baoshun Shao 1,†, Hui Sun 1,†, Muhammad Jamil Ahmad 1,†, Nasser Ghanem 2, Hamdy Abdel-Shafy 2, Chao Du 1, Tingxian Deng 1,3, Shahid Mansoor 4, Yang Zhou 1,5,6, Yifen Yang 1,2, Shujun Zhang 1,5,6, Liguo Yang 1,5,6, Guohua Hua 1,5,6,*
PMCID: PMC8021858  PMID: 33833774

Abstract

Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis.

Keywords: reproduction, breeding, genetic improvement, heritability, GWAS

Introduction

Reproductive traits are economically important for sustainable food production, especially for monotocous livestock, such as cattle and buffalo. Low reproductive capacity or infertility can be described as an extended duration between two calvings. This problem requires additional inseminations, more veterinary attention, and hormonal treatments, which consequently alters the current and subsequent lactations (Boichard, 1990). Also, additional costs are raised due to culling and replacing animals with fertility problems (Roxström and Strandberg, 2002). Enhancing fertility is the best choice not only to reduce the culling cost but also to save important genetic materials and increase farm profit (Dekkers, 1991). Several countries have included female reproductive traits in the breeding goals to emphasize the genetic aspects of reducing fertility costs (FCOST) in dairy cattle (Kadarmideen and Simm, 2002). Herein, we emphasize the recent literature about genetic parameters, genome-wide association study (GWAS), and genomic selection (GS) for reproductive traits in cattle and buffalo over the past 20 years for researchers, who can integrate these traits in cattle and buffalo breeding programs and achieve optimum fertility.

In the previous study, reproductive traits were divided into binary, interval, and continuous traits with respect to statistical distribution (Berry and Evans, 2014). To better understand and utilize reproductive traits in livestock and breeding programs, they are reclassified as ovulation, mating, and calving-related traits from the physiological viewpoint (Cammack et al., 2009; Table 1).

TABLE 1.

Physiological classification and description of reproductive traits.

Trait category Parameter Description
Ovulation Ovulation rate Corpus luteum (CL) number during mid-luteal phase of the estrous cycle
Superovulation response The biological potentiality of the cow in terms of total number of ova (TNO), transferable embryos (NTE), unfertilized ova (NUO) and degenerated embryos (NDE); total number of embryos (NE) and number of viable embryos (VE)
Twinning rate The proportion of cows giving birth to two or more calves in one pregnancy
Mating Age of puberty (AOP) Male: the age when a bull scrotal circumference reaches 26–29 cm (AGESC)*, or the age at which a bull first produces an ejaculate containing at least 50 million sperm with a minimum of 10% motility Female: the appearance of the first corpus luteum (AGECL), age at first behavioral estrus (AFO) or standardized age at first behavioral estrus (SFO) and plasma progesterone concentration
Age at first calving (AFC) The interval between the date of first calving and the date of birth of the cow
Non-return rate (NRR) The proportion of cows that are not subsequently rebred
pregnancy rate (PR) The percentage of cows to become pregnant
Calving Calving interval (CI) The period of time (days or months) between the birth of a calf and the birth of a subsequent calf, both from the same cow
Days open (DO) The period between calving and conception
Calving difficulty (CD) Dystocia, which is categorized into three degrees, including easy calving, slight problems, and difficult calving
Length of production life (LPL) Mainly focused on dairy cattle, length of service, tenure, etc. Such as fertility-/mastitis-/production-/determined PL (FPL/MPL/PPL)
Open in a new tab

*Most of the heritability studies for bulls’ puberty employed the AGESC 26–29 cm.

Heritability Estimates of Reproductive Traits

Genetic variation, which is a variability in breeding values within a population for a trait under selection, significantly affects the accuracy of genetic selection. Heritability measures how much of the phenotypic variation is attributed to genetic variation, and affects the rate of genetic improvement for a trait over generations. Over the past 20 years, several studies were conducted to estimate the heritability of different reproductive traits in dairy cattle (Table 2), beef cattle (Table 3), and buffalo cows (Table 4).

TABLE 2.

Heritability estimates of reproduction traits in dairy cattle.

Category Trait Heritability Breeds (Numbers/records) References
Ovulation Superovulation responses 0.231 ± 0.091 Holstein (2,489) König et al., 2007
0.27 ± 0.08 Holstein (926) Gaddis et al., 2017
0.234 ± 0.046(CL) 0.159 ± 0.087(EM) Holstein–Friesian (56) Bényei et al., 2004
0.15 ± 0.01 Holstein (150,971) Jaton et al., 2020
0.15 ± 0.01/0.17 ± 0.01(NE) 0.14 ± 0.01/0.14 ± 0.01(VE) (Log/Ans) Holstein (137,446) Jaton et al., 2016a
0.145 ± 0.007/0.188 ± 0.033(NE) 0.136 ± 0.007/0.187 ± 0.034(VE) (in vivo/vitro) Holstein (145661/5310 records) (in vivo/vitro) Jaton et al., 2016b
Twinning rate 0.11 ± 0.01(parity1) 0.16 ± 0.01(parity2) 0.14 ± 0.01(parity3) Japanese Holsteins (1,323,946) (1053469) (750600) Yutaka et al., 2015
0.0192 ± 0.0009/0.142 ± 0.007 (LM/TLM) Holsteins (658436 cows/1440540 records) Lett and Kirkpatrick, 2018
0.1 12 multiple breeds (9272 females) Allan et al., 2007
0.013(parity1) 0.022(parity2) 0.024(parity3) 0.026(parity4) 0.031(parity5) Israeli Holstein (671,361) (460940) (304213) (188077) (104434) Weller et al., 2008
Mating Age of puberty 0.38 Friesian × Ethiopian Boran (399) Jersey × Ethiopian Boran (151) Effa et al., 2011
Age at first calving 0.4 Friesian × Ethiopian Boran (399) Jersey × Ethiopian Boran (151) Effa et al., 2011
0.26 ± 0.02 South African Holstein (20419) Makgahlela et al., 2008
0.20 ± 0.03/0.21 ± 0.03(uni-trait/bi-trait analysis) Brazilian Girolando (10,900) Canaza-Cayo et al., 2018
0.219 ± 0.162 multiple dairy cows (224) Ali et al., 2019
0.17 ± 0.01 0.093 ± 0.037 Holstein–Friesian Other dairy breeds Berry and Evans, 2014
0.15 ± 0.04 (PM)/0.16 ± 0.04 (GPM) 7 breeds (9,106) Konkruea et al., 2019
0.111 Holstein (276,573) Changhee et al., 2013
0.103 ± 0.025 German Holstein heifers (721919) Heise et al., 2017
0.01 ± 0.07 Dairy Overo Colorado breed (2,043) Montaldo et al., 2017
Non-return rate 0.1292 (NRR45) 0.1460 (NRR90) Holstein (21,405) Ansari-Mahyari et al., 2019
0.02 (Paternal NRR90) 0.02 (Maternal NRR90) German Holstein (1193) (1283) Kaupe et al., 2007
0.012 (heifer NRR56) 0.015 (cow NRR56) Holstein (2,527) Müller et al., 2017
0.011 ± 0.001(NRR56) Holstein (386869) Zhang et al., 2019
0.027 ± 0.0004 0.020 ± 0.001 Holstein–Friesian Other dairy breeds Berry et al., 2014
Pregnancy rate 0.04/0.02/0.01 (DPR/CCR/HCR) Holstein (2,107) Gaddis et al., 2016
0.04 Spanish Holstein (113375 records) Gonzálezrecio and Alenda, 2005
Calving Calving interval 0.17 Friesian × Ethiopian Boran (847) Jersey × Ethiopian Boran (559) Effa et al., 2011
0.16 ± 0.12 0.00 ± 0.09 Holstein (624) Swedish Red (460) Tarekegn et al., 2019
0.14 ± 0.211 multiple dairy cow (224) Ali et al., 2019
0.106 ± 0.015 (linear sire model) 0.103 ± 0.013 (linear animal model) 0.059 ± 0.006 (repeatability animal model) Iranian Holstein (22,269) Chegini et al., 2019a
0.07 ± 0.013 Holstein (11674 records) Toghiani, 2012
0.044 ± 0.01 Holstein (20544) Chegini et al., 2019b
0.04 ± 0.003 Iranian Holstein (129199) Hossein Salimi et al., 2017
0.04 Spanish Holstein (96346 records) Gonzálezrecio and Alenda, 2005
0.034 ± 0.001 0.029 ± 0.004 Holstein–Friesian Other dairy breeds Berry et al., 2014
0.002 ± 0.02 Dairy Overo Colorado breed (3,488) Montaldo et al., 2017
0.01 ± 0.02 (CI1) 0.00 ± 0.04 (CI2) 0.08 ± 0.07 (CI3) Brazilian Girolando (5327) (3444) (2229) Canaza-Cayo et al., 2018
0.03 ± 0.01(CI1) 0.04 ± 0.01(CI2) 0.04 ± 0.01(CI3) 0.03 ± 0.01(CI4) South African Holstein (20419) (18589) (10681) (15529) Makgahlela et al., 2008
0.088 (CI1) 0.142(CI2) Holstein (167996 records) (128080 records) Changhee et al., 2013
Days open/calving to conception interval 0.102 Canadian Holstein (3,729) Nayeri et al., 2016
0.09 ± 0.121 multiple dairy cows (224) Ali et al., 2019
0.06 ± 0.03 Holstein (3,682) Saowaphak et al., 2017
0.06 ± 0.008 Holstein (15895) Toghiani, 2012
0.04 Spanish Holstein (113375 records) Gonzálezrecio and Alenda, 2005
0.04 ± 0.003 Iranian Holstein (129199) Hossein Salimi et al., 2017
0.033/0.024 (Model1/2) Korean Holstein (14,188) Lee and Han, 2004
0.026 Holstein (2,527) Müller et al., 2017
0.038 ± 0.002 0.030 ± 0.001 Holstein–Friesian Other dairy breeds Berry et al., 2014
Calving difficulty 0.132 ± 0.003 Holstein (734) Maryam et al., 2016
0.121 ± 0.024 (LM) 0.074 ± 0.012 (TM) Walloon Holstein Vanderick et al., 2015
0.05 (paternal CE) 0.05 (maternal CE) German Holstein (1267) (1287) Kaupe et al., 2007
0.048 (paternal CE) 0.039 (maternal CE) Holstein (2,527) Müller et al., 2017
0.043 ± 0.0031/0.010 ± 0.0016 (LM1) 0.041 ± 0.0030/0.010 ± 0.0015 (LM2) 0.046 ± 0.0032/0.011 ± 0.0016 (LM3) 0.086 ± 0.0091/0.023 ± 0.0037 (TM) (direct/maternal CE) Portuguese dairy cattle (320,953 records) Silvestre et al., 2019
0.02 ± 0.002 Iranian Holstein (132831) Hossein Salimi et al., 2017
0.015/0.030 (Model1/2) Korean Holstein (14,188) Lee and Han, 2004
Length of productive life 0.16 German Holstein (1,286) Kaupe et al., 2007
0.12 Pinzgau Cattle Egger-Danner et al., 2005
0.102 Holstein (276,573) Changhee et al., 2013
0.10 ± 0.03 Holstein (4,739) Saowaphak et al., 2017
0.06/0.10/0.18/0.25 (LPL/FPL/MPL/PPL) Swedish Red and White dairy cattle (538783) Roxström and Strandberg, 2002
0.04 Hungarian Holstein (1403747) van der Linde et al., 2006
Open in a new tab

TABLE 3.

Heritability estimates of reproduction traits in beef cattle.

Category Trait Heritability Breeds (Numbers/Records) References
Ovulation Ovulation rate 0.12 MARC twinning herd (16,035) Allan et al., 2014
0.08 MARC 12 breeds of cattle (29485 records) Allan et al., 2007
0.02 multiple breeds Piper et al., 2017
Superovulation responses (VE) 0.56–0.65 (1 flush) 0.20–0.26 (3 flushes) Nellore (405) (858) Peixoto et al., 2004
Twinning 0.1 MARC twinning herd (16,035) Allan et al., 2014
0.1 MARC 12 breeds of cattle (9272 records) Allan et al., 2007
0.062 ± 0.093 (RThM) 0.014 ± 0.018 (RLM) Maremmana cattle (1,260) Moioli et al., 2017
Mating Age of puberty 0.31 ± 0.05 (AFO) 0.27 ± 0.04 (SFO) 0.56 ± 0.11 (AGECL) 0.78 ± 0.10 (AGE26) Angus cattle (1513 records) (1588 records) Brahman heifers (1007) Brahman bulls (1118) Morris et al., 2000 Fortes et al., 2012
0.57 ± 0.12 0.52 ± 0.12 (AGECL) Brahman heifers (1007) Tropical Composite heifers (1108) Johnston et al., 2009
0.35/0.22/0.11 0.22/0.33 0.24/0.32 (AGECL) Brahman (397/371/206) Santa Gertrudis (1022/776) Droughtmaster (222/688) Engle et al., 2019
0.42–0.44 Nelore cattle (12964) Forni and Albuquerque, 2005
0.26 ± 0.03 Heifer Angus (629) Morris et al., 2011
0.221 ± 0.08 (univariate) 0.198 ± 0.06 (multivariate) 50% Red Angus, 25%Charolais and 25%Tarentaise (890) Toghiani et al., 2017
0.310 ± 0.050 (AFO) Beef cattle Berry and Evans, 2014
0.16–0.20 1828 Beef CRC (868 Brahman and 960 Tropical Composite) 3695 SMF (979 Brahman,1802 Santa Gertrudis and 914 Droughtmaster) Warburton et al., 2020
Scrotal circumference 0.37 ± 0.06(SC-8 month) 0.44 ± 0.06 (SC-10 month) 0.42 ± 0.06 (SC-12 month) Angus cattle (1702 records) (1691 records) (1671 records) Morris et al., 2000
0.48 ± 0.02 (AGE365) 0.52 ± 0.02 (AGE450) Brazilian Nellore (27567 records) Kluska et al., 2018
0.397 ± 0.011 (AGE365) Nelore (135862 records) Schmidt et al., 2019
0.33 ± 0.07 (AGE365) 0.41 ± 0.07 (AGE450) Guzera beef cattle (1773) Guzera beef cattle (2091) Tramonte et al., 2019
0.29 (AGE365) Nelore cattle (66986 records) Costa et al., 2020
0.18 ± 0.02 (AGE365) Charolais, Charbray, and Charolais-Zebu crosses (18,972) Martínez-Velázquez et al., 2020
Age at first calving 0.31 ± 0.016 Crossbred Bos taurus (64380 records) Berry et al., 2014
0.27 ± 0.12 Asturiana de los Valles (1226 records) Goyache and Gutiérrez, 2001
0.24 ± 0.04 Brazilian Nelore cattle (762) Mota et al., 2017
0.235 ± 0.018 Asturiana de los Valles (2533 records) Gutiérrez et al., 2002
0.220 ± 0.11 Jersey × Red Sindhi (313) Vinothraj et al., 2016
0.215 ± 0.026 Japanese Black Cows (24595 records) Oyama et al., 2002
0.20 Nelore cattle (1853) Costa et al., 2019
0.20–0.22 Simmental (3,063) Amaya-Martínez et al., 2020
0.17 ± 0.04 Brahman-Angus (909) Elzo et al., 2018
0.158 ± 0.039 Japanese Black cows (2,078) Setiaji and Oikawa, 2019
0.137 ± 0.008 beef cattle Berry et al., 2014
0.13 ± 0.130 Crossbred heifers (538 records) Akanno et al., 2015
0.11 ± 0.01 Brazilian Nellore (18526 records) Kluska et al., 2018
0.10 ± 0.01 (multi-trait model) 0.08 ± 0.01 (single-trait model) Hanwoo cows (15,355) Lopez et al., 2019
0.10 ± 0.01 Nelore beef cattle (25,594) Boligon and Albuquerque, 2011
0.20/0.19/0.18/0.09 (LM/SM/PM/TLcens) Brazilian Brahman cattle (53703 records) Lázaro et al., 2019
0.08 Nelore cattle (374665 records) Costa et al., 2020
0.06/0–0.15 0.13/0.06–0.13 (AMl/MHNRHOP1) Limousine (18,500) Charolais (4,330) de Rezende et al., 2020
0.06–0.08 Nelore cattle (18615) Forni and Albuquerque, 2005
0.039 ± 0.039 (univariate) 0.031 ± 0.01 (multivariate) 50% Red Angus, 25%Charolais and 25%Tarentaise (1117) Toghiani et al., 2017
Non-return rate 0.020 ± 0.029 (1st parity) 0.014 ± 0.022 (2nd parity) 0.013 ± 0.034 (3rd parity) 0.013 ± 0.017 (repeatability model) Japanese Black cows (2,078) Setiaji and Oikawa, 2019
Pregnancy rate 0.21 ± 0.009 Angus (1,299) Doyle et al., 2000
0.14 ± 0.099 Crossbred heifers (734 records) Akanno et al., 2015
0.12 ± 0.05 (yearlings) 0.08 ± 0.064 (2-year-olds) Angus cattle (1190 records) (711 records) Morris et al., 2000
0.027 ± 0.38 (1st parity) 0.023 ± 0.034 (2nd parity) 0.021 ± 0.036 (3rd parity) 0.022 ± 0.007 (repeatability model) Japanese Black cows (2,078) Setiaji and Oikawa, 2019
0.025/0.014/0.023/0.014 (model 1/2/3/4) Sistani beef cattle (1489 records) Faraji-Arough and Rokouei, 2016
Calving Calving interval 0.222 ± 0.101 Jersey × Red Sindhi (522) Vinothraj et al., 2016
0.125 ± 0.020 Asturiana de los Valles (2007 records) Gutiérrez et al., 2002
0.12 ± 0.03 Asturiana de los Valles (1851 records) Goyache and Gutiérrez, 2001
0.105 ± 0.008 Nelore (33735 records) Schmidt et al., 2019
0.09 ± 0.02 (CI1) Brahman-Angus (447) Elzo et al., 2018
0.02 ± 0.02 (CI1) 0.02 ± 0.04 (CI2) 0.06 ± 0.03 (mean CI) Nelore (2642) (1437) (2888) do Amaral Grossi et al., 2016
0.049 ± 0.048 (CI1) 0.043 ± 0.045 (CI2) 0.048 ± 0.042 (CI3) 0.047 ± 0.009 (repeatability model) Japanese Black cows (2,078) Setiaji and Oikawa, 2019
0.047 ± 0.009 Japanese Black Cows (72740 records) Oyama et al., 2002
0.032 ± 0.004 beef cattle Berry et al., 2014
0.056/0.040/0.033/0.032 (model 1/2/3/4) Sistani beef cattle (1489 records) Faraji-Arough and Rokouei, 2016
0.01 ± 0.05 (CI1) 0.04 ± 0.02 (CI2) 0.07 ± 0.03 (CI3) 0.03 ± 0.01 (multi-trait model) Hanwoo cows (1936) (11144) (8201) (32599) Lopez et al., 2019
0.02 ± 0.004 Crossbred Bos taurus (101864 records) Berry and Evans, 2014
Days open/calving to conception interval 0.192 (model 1) 0.091 (model 2) 0.168/0.197/0.170/0.091 (model3) 0.154/0.132 (model4) 0.135/0.090/0.086 (model5) Asturiana de los Valles (21349 records) (3250/3416/13783/900 records) (6666/14683 records) (21349 records) Goyache et al., 2005
0.110 ± 0.04 beef cattle Berry et al., 2014
0.110 ± 0.04 Angus (1680 records) Morris et al., 2000
0.09/0.045/0.096/0.049 (model 1/2/3/4) Sistani beef cattle (1489 records) Faraji-Arough and Rokouei, 2016
0.047 ± 0.009 Japanese Black cows (72740 records) Oyama et al., 2002
0.042 ± 0.044 (1st parity) 0.034 ± 0.052 (2nd parity) 0.034 ± 0.033 (3rd parity) 0.036 ± 0.021 (repeatability model) Japanese Black cows (2,078) Setiaji and Oikawa, 2019
0.02 ± 0.05 (1st parity) 0.09 ± 0.02 (2nd parity) 0.08 ± 0.03 (3rd parity) 0.03 ± 0.01(multi-trait model) Hanwoo cows (1726) (7308) (5888) (32465) Lopez et al., 2019
Calving difficulty 0.42 Asturiana de los Valles (7298 records) Goyache and Gutiérrez, 2001
0.325 ± 0.022 Asturiana de los Valles (35,395 records) Cervantes et al., 2010
0.32 ± 0.174 Crossbred heifers (543 records) Akanno et al., 2015
0.29 ± 0.10 multi breeds (5,795) Ahlberg, 2014
0.250 ± 0.018 Crossbred Bos taurus (100445 records) Berry and Evans, 2014
Length of productive life 0.096 ± 0.001 Multiple breeds (21,895) Brzáková et al., 2019
Open in a new tab

TABLE 4.

Heritability estimates of reproduction traits in buffalo.

Trait Heritability Breeds (Numbers/records) References
Age at first calving 0.28 ± 0.03 Murrah buffalo (827) Kumar et al., 2015
0.226 ± 0.154 0.16 Surti buffalo (48) Murrah water buffalo (2290 records) Rathod et al., 2018 de Araujo Neto et al., 2020
0.16 ± 0.04 Murrah buffalo (2389 records) Barros et al., 2016
0.16 ± 0.12 Murrah buffalo (167) Thiruvenkadan et al., 2010
0.17 ± 0.02 Murrah buffaloes (3,431 records) Camargo et al., 2015
0.135 ± 0.035 Indian Murrah buffalo (1,456 records) Gupta et al., 2015
0.11 ± 0.06 Egyptian buffalo (1911 records) El-Bramony, 2011
0.07 ± 0.05 Murrah buffalo (1,578) Seno et al., 2010
calving interval 0.55 ± 0.131 Surti buffalo (158) Rathod et al., 2018
0.234 ± 0.175 Indian Murrah buffalo (1,456 records) Gupta et al., 2015
0.14 ± 0.07 (CI1) Murrah buffalo (1,578) Seno et al., 2010
0.09 ± 0.13 Murrah buffalo (506) Thiruvenkadan et al., 2010
0.085 ± 0.134 Iranian Khuzestan buffalo (146 records) Morammazi et al., 2007
0.07 ± 0.05 Egyptian buffalo (1911 records) El-Bramony, 2011
0.06 ± 0.01 Egyptian buffalo (2,066) El-Bramony and Reclamation, 2014
0.06 ± 0.01 Murrah buffaloes (4729 records) Camargo et al., 2015
0.05 ± 0.08 Mehsana buffalo (812 records) Galsar et al., 2016
0.05 ± 0.01 Murrah buffalo (5672 records) Barros et al., 2016
0.03(CI1) Murrah water buffalo (765 records) de Araujo Neto et al., 2020
Days open 0.14 ± 0.03 Murrah buffaloes (6894 records) Camargo et al., 2015
Calving difficulty 0.16/0.19/0.06/0.08/0.09/0.04/0.11 (parity1–7) Iraqi Buffalo (360) Al-Khuzai et al., 2019
Open in a new tab

In dairy cattle, all ovulation-related traits range from low to moderate heritabilities (Table 2). The heritability estimate of the superovulation response was about 0.15 in Holstein cows (Jaton et al., 2020). Regarding mating-related traits, heritability estimates for age of puberty (AOP) and age at first calving (AFC) were moderate in most cattle populations, except for AFC in the Chile population (h2 = 0.01) (Montaldo et al., 2017). Likewise, the heritabilities of non-return rate (NRR) and pregnancy rate (PR) of Holstein dairy cows and Brown Swiss cattle were low (Gaddis et al., 2016; Tiezzi et al., 2018; Ansari-Mahyari et al., 2019; Zhang et al., 2019). Regarding the superovulation response and twinning rate, heritability was higher for superovulation, indicating a response to hormone treatment is more heritable than natural ovulation in dairy cows. Non-return and PR are directly related to reproductive outcomes. Unfortunately, the heritability estimates for these two traits were remarkably low. Besides, dairy cows’ calving-related traits, including calving interval, days open, calving difficulty (CD), and the length of the productive life, were all of low heritability. Therefore, management practices (reproductive management, balanced nutrition, etc.) and/or environmental factors are of significant importance for improving reproductive efficiency and preventing reproductive disorders in dairy cows. Thus, selection on dairy cows’ AOP, first calving, and superovulation response may gain more progression than other traits.

In beef cattle, the superovulation response had higher heritability than those of ovulation rate, and twinning rate was similar to those reported in dairy cattle (Table 3). Regarding mating-related traits, AOP had moderate to high heritability estimates in most beef populations; for example, the estimate reached 0.78 in the Brahman bull population (Fortes et al., 2012). The h2 for scrotal circumference was also reported to have moderate to high estimates. Excluding the Angus population (0.2) (Doyle et al., 2000) in beef cattle, the NRR and PR of heritability were low, as reported in dairy cattle. The heritabilities for calving difficulties in beef cattle had moderate to high estimates, unlike those reported in dairy cattle with low heritabilities. In comparison, other mating-related reproductive traits, such as DO, NRR, CI, and length of productive life had low heritabilities similar to dairy cattle. Taken together, selections on beef cow’s AOP, calving difficulties, DO, NRR, and CI traits may gain more progression due to the moderate to high estimates of heritabilities compared with other traits (Cassell, 2009).

The excellent milk quality and the limitation of buffalo milk yield contribute to the breeding selection focusing more on milk production traits in buffalo compared with reproductive traits. Currently, there are limited studies for estimating genetic parameters for reproductive traits in buffalo species, mainly for AFC and CI (Table 4). The heritability estimates of AFC in the buffalo population is close to Holstein cattle (Gupta et al., 2015; Kumar et al., 2015; Barros et al., 2016; Rathod et al., 2018). Most studies showed that the heritability of CI is low, mostly below 0.1 (Morammazi et al., 2007; Thiruvenkadan et al., 2010; El-Bramony and Reclamation, 2014; Camargo et al., 2015). However, the highest record for CI was 0.55 in Surti buffalo, which may be due to the limited numbers of lactation records and/or number of parities per sire monitored (Rathod et al., 2018). The heritabilities of DO (Camargo et al., 2015) and CD (Al-Khuzai et al., 2019) were similar to those reported in dairy cattle.

Comparing heritabilities between different traits in dairy and beef cattle along with buffalo, we found that:

  • (1)

    Most of the reproductive traits had low habitabilities, but not all. In the dairy and beef cattle, AOP showed high heritabilities. The heritability estimates for scrotal circumference of the beef bull were medium to high. Also, the superovulation response in dairy and beef cattle was worthy of notice. These moderate to high heritability traits could be applied to the selection and breeding system.

  • (2)

    The heritability estimates for calving intervals, NRR, days open, and length of reproductive life in most populations were very low, which indicated that these traits would be influenced and improved by proper management practices. The application of synchronization-timed AI protocol (Goodling et al., 2005), body composition control, reproductive disorder treatment, and culling on time would benefit the related performance.

  • (3)

    The heritability of the same trait varies greatly among different breeds. For instance, the heritability of age at first calving was as high as 0.4 in a crossbreed of dairy cows (Effa et al., 2011), while the Dairy Overo Colorado breed was as low as 0.01 (Montaldo et al., 2017). The heritability of CI reported in Surti buffalo is 0.55 (Rathod et al., 2018) compared to the Murrah buffalo cows near to 0.1 (Thiruvenkadan et al., 2010). Although heritability was estimated using paternal half-sib correlation methods in both studies, lactation records, number of buffaloes, and sired by bulls were higher for Murrah buffaloes. Even in the same breed, the different populations showed varied values, which may related to different management and performance.

  • (4)

    For most of the reproductive traits, beef cattle had higher heritability estimates compared to those estimated in dairy cattle for the AOP and CD (Tables 2, 3). Either the genetic makeup or the fact that dairy cows are more susceptible to reproductive diseases, such as endometritis, vaginitis, ovarian cyst, and mastitis, due to high energy consumption for milk production may be the reason for this difference.

  • (5)

    The breeding progress of buffalo is slow compared to dairy and beef cattle, as a few studies have reported during the last decade. Further large-scale studies are required to accurately estimate the genetic parameters for different reproductive traits in buffalo populations.

Marker-Associated Studies for Bovine and Buffalo Reproductive Traits

Concerning the disadvantages of the long cycle and not up-to-mark efficiency of traditional breeding, several association analyses were performed to identify genomic loci associated with the trait variation to find the possible candidate genes or to detect causative mutations. This section summarized the GWAS and candidate gene studies for bovine and buffalo reproductive traits published in the past 20 years (2000–2020) (Supplementary Tables 1–3).

At present, there are few marker-assisted selection (MAS) studies on the reproductive traits of buffalo. In this regard, FSHR, INHA, LHCGR, and OPN were reported to have significant effects on the buffalo superovulation responses. So far, few GWAS have been performed on buffalo reproductive traits (Camargo et al., 2015; Li et al., 2018a, b; de Araujo Neto et al., 2020). Previous GWASs for reproductive traits (Camargo et al., 2015; Li et al., 2018a) were conducted using the bovine reference genome assembly, and the results are expressed for bovine autosomes (BTA). Camargo et al. (2015) reported some candidate genes (TPCN1, SCG5, and Fig 4) associated with reproductive traits such as AFC, CI, and DO in buffalo. Also, Li et al. (2018a; 2018b) found 25 SNPs in 13 genes related to reproductive traits by integrating RNA-seq and GWAS methods. They also described significant SNPs on BBU 6, 9, and 15 [corresponding to bovine chromosomes 3, 7, 14, and 8: equivalence presented by Cribiu et al. (2001)]. Recently, ssGBLUP was employed to identify genomic regions affecting AFC and first calving interval (FCI) in buffalo cows and select candidate loci and gene expression (de Araujo Neto et al., 2020). They reported that the observed candidate regions for both traits (CI, AFC; explaining a large proportion of variance for both traits) were located on BBU 3, 12, 21, and 22. Also, candidate regions were found on BBU 6, 7, 8, 9, and 15 for age at first calving and on BBU 4, 14, and 19 for FCI. The ROCK2, PMVK, ADCY2, MAP2K6, BMP10, and GFPT1 genes are the main candidates for reproductive traits in water dairy buffaloes, which may be used in the future for animal breeding programs or for gene expression studies of the species (de Araujo Neto et al., 2020). The GFPT1 and BMP10 genes are interesting because they have been detected for both traits, which may be related to a possible pleiotropic effect.

The candidate gene studies for bovine reproductive traits mostly used genes of hormones and/or growth factors and their receptors as candidates (Tang et al., 2011; Yang et al., 2013; Arslan et al., 2017). For example, polymorphisms in the GnRH, GnRHR, LEP, and LHCGR were studied for association with reproductive traits of buffalo bulls. Notably, genes involved in IGF1 and LEP pathways were reported to affect multiple reproductive traits. For example, IGF1 could affect a variety of ovulation- and mating-related traits. LEP and LEPR showed significant effects on both breeding- and calving-related traits. Moreover, long non-coding RNA and ribosomal RNA could be future research directions since non-coding RNAs (U6 spliceosomal RNA) were reported to affect reproductive traits (Fortes et al., 2013; Nascimento et al., 2016; Buzanskas et al., 2017). The combination of GWAS and other omics studies are becoming more useful, as they provide a broad space for exploring candidate gene functions and related mechanisms.

Further, we visualized the chromosomal distribution of quantitative trait loci (QTL) in cattle related to each reproductive trait using the Cattle Quantitative Trait Locus Database (Cattle QTLdb) (Hu et al., 2019) (Supplementary Figures 1–3). Only 11 QTL related to ovulation-related traits were identified, and four of these were located on chromosome 5, where the IGF1 gene is placed (Miller et al., 1992) (Supplementary Figure 1). The QTL for mating-related traits were spread throughout different chromosomes (Supplementary Figure 1A). The most abundant chromosome is BTX with 10237 QTL (96.4%) related to puberty. BTA2 (21QTLs, 19.6%) and BTA14 (15 QTLs, 14.0%) had the most associated loci for AFC (Supplementary Figure 1B). Most of the QTL for NRR were located on BTA17 (233421 QTLs, 94.7%). However, QTL for PR-related were scattered (Supplementary Figure 2). About 37.1% of QTL related to calving interval were enriched in BTA25 (17.5%) and BTA29 (19.6%). Whereas, BTA 21 enriched the most QTLs (44.8%) related to CD, and BTA18 had 30.7% of QTL related to the length of productive life.

Undoubtedly, these significantly enriched chromosomes (BTX related to puberty, BTA related to NRR, and BTA related to CD) could be directions for future research. Moreover, certain areas that affect multiple traits of different species also deserve further attention. For example, McClure et al. (2010) found one SNP related to CD at 49.1 Mb of BTA 20 in Angus cattle (McClure et al., 2010), while Ke et al. (2014) reported SNP in a similar region in dairy cattle affecting age at first calving. The relationship between these highly enriched chromosomal regions and various traits is worthy of further investigation.

Based on morphological and behavioral criteria, the domestic Asian water buffalo has two types (Macgregor, 1941). The two types have different chromosome numbers: river buffalo (Bubalus bubalis, 2n = 50) and swamp buffalo (Bubalus bubalis carabanesis, 2n = 48) (Ulbrich and Fischer, 1966). In addition, the chromosomal karyotype of hybrid buffalo is more complicated. Although presenting different species, buffalo and bovine share highly homologous chromosomes banding, as well as gene mapping (Amaral et al., 2008; Michelizzi et al., 2010; Kale et al., 2014). It is also reported that river buffalo and bovine chromosomes can be matched arm for arm at the cytogenetic level (Williams et al., 2017; Du et al., 2019). Despite the complicated genomic background of buffalo, candidate genes or their chromosome locations identified for the bovine reproductive traits could be considered as a valuable reference for buffalo.

Genomic Selection for Reproductive Traits in Bovine and Buffalo

Phenotypic records for a trait of individuals and their relatives are used to estimate breeding values by employing the best linear unbiased prediction (BLUP) to facilitate animal selection for economically important traits (Henderson, 1984). It is believed for genetic selection that information at the DNA level can quicken the genetic progression compared to phenotypic data alone. The sparse map of genetic markers can be used to detect QTL (Georges et al., 1995). Combining genetic marker information with BLUP (Fernando and Grossman, 1989) showed an increase in the genetic gain by 8–38% (Fernando and Grossman, 1989; Goddard, 1996). The effectiveness of sparse markers in outbreeding species was limited, as an establishment of linkage phase between a marker and QTL is necessary for every family in which the marker is to be used for selection (Meuwissen et al., 2001).

The total number of SNP estimated at millions and the advent of DNA Chip technology made genotyping of many animals for many of these markers feasible and cost-effective. However, a dense marker map improved precision for QTL mapping by traditional linkage analysis (Darvasi et al., 1993). Therefore, a search for a different approach to efficiently use all this marker information remained necessary.

Considering a denser marker map, not only could some markers be close to QTL but also, in linkage disequilibrium with it, it was anticipated that some markers could have a positive effect on the quantitative traits across all families and be used for selection without the need to establish a Linkage phase in each family. Close markers can also be combined into a haplotype. Chromosome bearing the rare marker haplotype is likely to be identical by descent and hence carry the same QTL allele. Meuwissen et al. (2001), estimated the effect of the quantitative trait of the small chromosome segment defined by the haplotype of the allele that they carry. They concluded that it’s possible to accurately estimate the breeding value of animals that have no phenotypic records by estimating a large number of haplotype effects. Using least squares, all haplotype effects could not be estimated simultaneously. Even when only the largest effects were included, they were overestimated and the accuracy of predicting breeding value was low. Methods that assumed prior distribution for the variance associated with each chromosome segment gave a more accurate prediction of breeding values even when the prior was not correct. Selection based on breeding values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval. Selection based on pedigree has played an important role in the selective breeding improvement in domestic animals.

Quantitative traits are usually affected by many genes and, consequently, the benefits from the MAS are limited by the proportion of the genetic variance explained by the QTL. Hence, it is warranted to utilize all the QTL affecting the traits in MAS. Nevertheless, a dense marker map defines a very large number of chromosome segments and so there will be many effects to be estimated, probably more than there are phenotypic data points from which to estimate them (Meuwissen et al., 2001).

With the emergence of high-density SNP chips, such as Illumina chips [BovineHD BeadChip SNP, BovineSNP50 chip, High-Density Bovine SNP chip (777K)] and Axiom® Buffalo Genotyping Array (90K), GS methods are improving livestock genetic evaluation systems. They have the advantages of high accuracy, short interval between generations, and rapid genetic progress.

At present, GS has been applied in cattle on a large scale, but mainly focus on milk production and carcass traits (Silva et al., 2014; Weller et al., 2017). The GS studies on reproductive traits in dairy and beef cattle, including AFC, puberty, NRR, PR, days open, and CD, are listed on Table 5.

TABLE 5.

A summary of genomic selection studies for reproductive traits.

Traits studied Breed (country) Chip size Validation population size Models Response variable Accuracy of prediction Regression coefficients References
Age at first calving Nelore (Brazil) Illumina BovineHD 1,853 GBLUP BAYESCπ IBLASSO dEBV 0.38(GBLUP), 0.39(IBLASSO) 0.42(BAYESCπ) 0.88(GBLUP), 1.14(IBLASSO) 0.81(BAYESC) Costa et al., 2019
Nelore (Brazil) Illumina Bovine 70 K 714 BayesA BayesB BayesCπ BLASSO BRR dEBV 0.24(BayesA) 0.23(BayesB) 0.33(BayesCπ) 0.24(BLASSO) 0.38 0.62 0.63 0.65 0.83 0.65 Mota et al., 2018
Nelore (Brazil) Illumina BovineHD 2,241 BayesC EBVs 0.64 0.9 Boddhireddy et al., 2014
crossbred animals (Thai) GeneSeek 80k chip 8,361 ss GBLUP ssGBLUPS1 ssGBLUPS2 EBV 0.297 0.298 0.264 Laodim et al., 2019
Gyr dairy cattle (Brazil) GeneSeek SGGP-20Ki Illumina BovineSNP50 GeneSeek GGP-75Ki Illumina BovineHD 422 bulls and 1582 cows GBLUP dEBVs 0.380 0.968/0.960 0.966/0.958 0.967/0.959 0.968/0.970 (bulls/bulls and cows) Boison et al., 2017
CGC: 50%Red Angus 25%Charolais 25%Tarentaise BovineSNP50 chip 1117 records BayesA BayesB BayesCπ EBVs 0.148 0.143/0.154/0.146 (π = 0.99/0.95/0.90) 0.150 Toghiani et al., 2017
Scrotal circumference Braford and Hereford (Brazil) Illumina BovineSNP50K Illumina BovineHD 3680 (2997 Braford and 683 Hereford) tsGBLUP/ssGBLUP EBVs/dEBVs 0.28–0.33 0.15–0.17 0.50–1.10 0.55–1.13 Piccoli et al., 2020
Brangus GGP−LDV3 chip (1074) GGP−LDV4 chip (1535) Illumina BovineSNP50 (261) GGP−HDT (295) GGP−UHD (628) Illumina Bovine HD (4) 3,797 tsGBLUP ssGBLUP EBVs/dEBVs 0.717 0.634 Lopes et al., 2018
Nelore cattle (Brazil) Illumina BovineHD (763) Illumina BovineSNP50 (1478) 2,241 BayesC EBVs 0.59/0.59 (AGE365/450) 0.57/0.56 (AGE365/450) 0.95/0.93 (AGE365/450) 0.89/0.86 (AGE365/450) Boddhireddy et al., 2014
Nelore bulls (Brazil) Illumina BovineHD 691 GBLUP Bayes C BLASSO dEBV 0.68(GBLUP0) 0.71(GBLUP20) 0.72(Bayes C) 0.72(BLASSO) 1.27 (GBLUP0) 1.44(GBLUP2) 1.68(BAYESC) 1.65(BLASSO) Neves et al., 2014
Angus’ sires (America) Illumina BovineSNP50 439 BayesC dEBVs 0.487 (K-means)/0.600 (Random) 0.916 (K-means)/ 0.983 (Random) Saatchi et al., 2011
Puberty (age at first corpus luteum) Beef CRC: (882 Brahman and 990 Tropical Composite) Smart Futures: (974 Brahman, 1798 Santa Gertrudis, and 910 Droughtmaster) Illumina BovineSNP50 chip GeneSeek GGP-LD array 1,872 3682 GBLUP EBVs 0.49 ± 0.06 (Tropical Composite) 0.52 ± 0.07 (Brahman) (80% CRC + SF) Engle et al., 2019
50%Red Angus 25%Charolais 25%Tarentaise BovineSNP50 chip 890 BayesA BayesB BayesC EBVs 0.237 0.188/0.235/0.242 (π = 0.99/0.95/0.90) 0.226 Toghiani et al., 2017
CRC(2174) and Validation cows (4286) Illumina BovineHD Illumina 7K Illumina BovineSNP50K 6,460 GBLUP EBVs 0.33 (Brahman) 0.15 (Tropical Composite) Zhang et al., 2014
Non-return rate Holstein (Canada) Illumina Bovine SNP50 317 (first) and 489 (later) ssGBLUP msGBLUP GEBV DGV 0.39/0.33 (first/later) 0.63–0.97 (first) 0.81–1.35 (later) Guarini et al., 2018
Heifer pregnancy rate Angus sires (America) Illumina BovineSNP50 133 BayesC dEBVs 0.269 (K-means)/0.378 (Random) 1.337 (K-means)/1.580 (Random) Saatchi et al., 2011
Nelore (Brazil) Illumina BovineHD (763) Illumina BovineSNP50 (1478) 2,241 BayesC EBVs 0.64 0.64 0.89 0.87 Boddhireddy et al., 2014
Days open Holstein (North America) Illumina Bovine SNP 50 TM Chip 6,515 GBLUP dEBV 0.50 0.9 Forutan et al., 2018
Calving ease direct/maternal (CED/CEM) Brangus (CED/CEM) GGP−LDV3 chip (1074) GGP−LDV4 chip (1535) Illumina BovineSNP50 (261) GGP−HDT (295) GGP−UHD (628) Illumina Bovine HD (4) 3,797 tsGBLUP ssGBLUP EBVs dEBVs 0.451/0.512 0.337/0.266 (CED/CEM) Lopes et al., 2018
Holstein (Canada) (calving ease) Illumina Bovine SNP50 438 (first) and 363 (later) ssGBLUP msGBLUP GEBV DGV 0.76/0.69 (first/later) 0.71–1.09 (first) 0.56–0.82 (later) Guarini et al., 2018
Angus bulls (America) (CED/CEM) Illumina BovineSNP50 BeadChip 3180 BayesC dEBVs CED:0.488/0.617 CEM:0.416/0.571 (K-means/Random) CED:0.942/1.007 CEM:1.181/1.277 (K-means/Random) Saatchi et al., 2011
Norwegian Red bulls (calving ease) Affymetrix 25K MIP-SNP chip 500 GBLUP BayesB MIXTURE GW-EBV 0.406/0.382 0.411/0.392 0.429/0.401 (Cohort//Random masking) 1.192/1.104 0.932/0.953 0.998/0.862 (Cohort//Random masking) Luan et al., 2009
Open in a new tab

For AFC, the accuracy of genomic prediction was varied among different populations and methods. In the Nellore breed, the accuracy of prediction for AFC was 0.64 (Boddhireddy et al., 2014); however, another scholarly journal reported that the accuracy ranged between 0.38 and 0.42 by three different models (Costa et al., 2019). The prediction accuracy is around 0.23–0.33 in another Nellore cow population (Mota et al., 2018). Using the ssGBLUP model, the accuracy of prediction for AFC was 0.299 in the Thai native breed (Laodim et al., 2019), and was 0.56 in the Gyr dairy cattle breed (Boison et al., 2017).

Genomic selection studies on puberty (scrotal circumference and age at first corpus luteum) showed that the accuracy performance of different models is above 0.6 (Boddhireddy et al., 2014; Neves et al., 2014; Toghiani et al., 2017; Lopes et al., 2018; Engle et al., 2019). However, the accuracy was decreased dramatically in crossbred populations (Zhang et al., 2014; Piccoli et al., 2020). The limited reference population in the hybrid population and the general traits of the reference population have no direct counterpart in the validation population, which may be the reason for this decrease.

In the PR studies, the accuracy of prediction was 0.269 in the Angus population (Saatchi et al., 2011) and 0.64 in Nelore cattle (Boddhireddy et al., 2014). For CD, the highest accuracy was 0.516 in Brangus using GBLUP models (Lopes et al., 2018), and the prediction accuracy of different beef cattle breeds is around 0.45 among different models (Luan et al., 2009; Saatchi et al., 2011), while the accuracy in dairy cows was lower by 0.24–0.34 (Guarini et al., 2018).

Regarding buffalo studies, genomic evaluation reports are very limited either for productive or reproductive traits. There is only one published study for AFC and CI in buffalo (de Araujo Neto et al., 2020). Genomic evaluation studies in buffalo are still in the developing stage. The main limitation of applying genomic evaluation in buffalo is the lack of a well-structured reference population. Since the number of individuals with both genotypic and phenotypic information in each country is still limited, a multi-breed genomic evaluation would be the best alternative (Liu et al., 2018; Abdel-Shafy et al., 2020a, b).

Conclusion and Perspectives

Reproductive traits were depreciated during selection indexes to improve the genetic potential of livestock. Hence, the recently desired gains are being practiced to ensure that the all TMI (total merit index) traits show a positive response or, at the very least, no negative response. However, the statistical data from the Council on Dairy Cattle Breeding (CDCB)1 indicated that, without severely slowing genetic gain for milk production, the daughter PR has stabilized and the declining trend has been reversing since 2003. A similar trend has also been demonstrated by García-Ruiz et al. (2016). Moreover, several pregnancy-related SNPs with neutral associations with milk production in Holstein bulls were identified (Cochran et al., 2013). It elicits the possibility of increasing fertility without reducing productive performance during selection.

Unlike dairy and beef cattle, few studies have been performed so far for reproductive traits in buffalo. Methods such as GWAS and GS require a large group size, well-structured pedigree, and accurate phenotypic records, which are big challenges for buffalo populations. The first reference for buffalo genome sequencing was released in 2017 (Williams et al., 2017), lacking the sequence in the chromosome and genes annotation, which was completed and updated in 2019 (Low et al., 2019; Mintoo et al., 2019). It will quicken the GS research and be significantly helpful in promoting buffalo breeding.

Dissimilar to dairy production traits, GWAS for reproductive traits seems to be underpowered and has difficulty in finding major QTL. It still provides genetic variability across many genome-wide genes and intragenic regions for complex trait studies, which greatly increases the understanding of complex traits’ molecular genetic mechanisms.

For reproductive traits with low heritability, the genetic gain using GS is improved three to four times per year compared to traditional methods (García-Ruiz et al., 2016). However, GS is also facing some difficulties, especially for buffalo, such as lacking an optimum population structure with record and some species having no dense marker maps yet. Its accuracy is limited by the reference population’s size and SNP marker density, which is obvious in some hybrid populations. In developing countries, there is a lack of complete historical records, and the number of genotyped animals has limited the development of GS. Also, for those traits with low to high heritability (such as puberty, age at first calving, and CD), multivariate GS can performed on multiple traits to improve prediction accuracy. In addition, multi-breed genomic evaluation can be used for populations with limited size. Besides, multi-omics data integration and analysis are gaining more attention from fields such as genomics, transcriptomics, and epigenomics.

Author Contributions

GH contributed to the conception and design of the study. BS wrote the first draft of the manuscript and collected the data. CD, HS, MA, and YY wrote sections of the manuscript. NG, HA, SM, YZ, TD, LY, and SZ revised the manuscript and made profound suggestions. All authors contributed to manuscript revision and read and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This work was supported by the National Natural Science Foundation of China (31872352), Fundamental Research Funds for the Central Universities (2662018PY037), and the Earmarked Fund for Modern Agro-Industry Technology Research System (CARS-36).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2021.617128/full#supplementary-material

Click here for additional data file. (594.8KB, docx)

References

  1. Abdel-Shafy H., Awad M. A. A., El-Regalaty H., El-Assal S. E.-D., Abou-Bakr S. (2020a). Prospecting genomic regions associated with milk production traits in Egyptian buffalo. J. Dairy Res. 87 389–396. 10.1017/s0022029920000953 [DOI] [PubMed] [Google Scholar]
  2. Abdel-Shafy H., Awad M. A. A., El-Regalaty H., Ismael A., El-Assal S. E.-D., Abou-Bakr S. (2020b). A single-step genomic evaluation for milk production in Egyptian buffalo. Livestock Sci. 234:103977. 10.1016/j.livsci.2020.103977 [DOI] [PubMed] [Google Scholar]
  3. Ahlberg C. (2014). Genetic Parameter Estimates and Breed Effects for Calving Difficulty and Birth Weight in a Multi-Breed Population. Lincoln, NE: University of Nebraska. [DOI] [PubMed] [Google Scholar]
  4. Akanno E. C., Plastow G., Fitzsimmons C., Miller S. P., Baron V., Ominski K., et al. (2015). Genome-wide association for heifer reproduction and calf performance traits in beef cattle. Genome 58 549–557. 10.1139/gen-2015-0031 [DOI] [PubMed] [Google Scholar]
  5. Ali I., Muhammad Suhail S., Shafiq M. (2019). Heritability estimates and genetic correlations of various production and reproductive traits of different grades of dairy cattle reared under subtropical condition. Reprod. Domest. Anim. 54 1026–1033. 10.1111/rda.13458 [DOI] [PubMed] [Google Scholar]
  6. Al-Khuzai H. M., Al-Dulaimi M. K., Zaini Z. E. (2019). Estimation of heritability for reproductive traits and newborn mortality in iraqi buffaloes through the relationship of dams with daughters. Indian J. Public Health Res. Dev. 10 660–663. 10.5958/0976-5506.2019.02508.7 [DOI] [Google Scholar]
  7. Allan M., Kuehn L., Cushman R., Snelling W., Echternkamp S., Thallman R. (2014). Confirmation of quantitative trait loci using a low-density single nucleotide polymorphism map for twinning and ovulation rate on bovine chromosome 5. J. Anim. Sci. 87 46–56. 10.2527/jas.2008-0959 [DOI] [PubMed] [Google Scholar]
  8. Allan M., Thallman R., Cushman R., Echternkamp S., White S., Kuehn L., et al. (2007). Association of a single nucleotide polymorphism in SPP1 with growth traits and twinning in a cattle population selected for twinning rate. J. Anim. Sci. 85 341–347. 10.2527/jas.2006-460 [DOI] [PubMed] [Google Scholar]
  9. Amaral M. E. J., Grant J. R., Riggs P. K., Stafuzza N. B., Rodrigues Filho E. A., Goldammer T., et al. (2008). A first generation whole genome RH map of the river buffalo with comparison to domestic cattle. BMC Genomics 9:631. 10.1186/1471-2164-9-631 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Amaya-Martínez A. A., Martínez R., Cerón-Muñoz M. F. (2020). Genetic parameters for growth and reproduction in Simmental cattle from pedigree and genomic relationship. Rev. MVZ Córdoba 25:1520. 10.21897/rmvz.1520 [DOI] [Google Scholar]
  11. Ansari-Mahyari S., Ojali M. R., Forutan M., Riasi A., Brito L. F. (2019). Investigating the genetic architecture of conception and non-return rates in Holstein cattle under heat stress conditions. Trop. Anim. Health Prod. 51 1847–1853. 10.1007/s11250-019-01875-5 [DOI] [PubMed] [Google Scholar]
  12. Arslan K., Akyüz B., Akçay A., İlgar E. G., Macun H. C., Çınar M. U. (2017). Association of number of artificial inseminations per pregnancy in holstein dairy cows with polymorphism in luteinizing hormone receptor and follicle stimulating hormone receptor genes. Slovenian Vet. Res. 54 91–98. [Google Scholar]
  13. Barros C. D. C., Aspilcueta-Borquis R. R., Fraga A. B., Tonhati H. (2016). Genetic parameter estimates for production and reproduction traits in dairy buffaloes. Rev. Caat. 29 216–221. 10.1590/1983-21252016v29n125rc [DOI] [Google Scholar]
  14. Bényei B., Gáspárdy A., Komlósi I., Pécsi A. (2004). Repeatability and heritability of ovulation number and embryos in dam-daughters pairs in superovulated holstein–friesian cows. Reprod. Domest. Anim. 39 99–102. 10.1111/j.1439-0531.2004.00487.x [DOI] [PubMed] [Google Scholar]
  15. Berry D. P., Evans R. (2014). Genetics of reproductive performance in seasonal calving beef cows and its association with performance traits. J. Anim. Sci. 92 1412–1422. 10.2527/jas.2013-6723 [DOI] [PubMed] [Google Scholar]
  16. Berry D. P., Wall E., Pryce J. (2014). Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8 105–121. 10.1017/s1751731114000743 [DOI] [PubMed] [Google Scholar]
  17. Boddhireddy P., Prayaga K., Barros P., Lôbo R., Denise S. (2014). “Genomic predictions of economically important traits in Nelore cattle of Brazil,” in Proceedings of the 10th World Congr. Genet. Appl. Livest. Prod, Vancouver, BC. [Google Scholar]
  18. Boichard D. (1990). Estimation of the economic value of conception rate in dairy cattle. Livestock Prod. Sci. 24 187–204. 10.1016/0301-6226(90)90001-m [DOI] [Google Scholar]
  19. Boison S., Utsunomiya A., Santos D., Neves H., Carvalheiro R., Mészáros G., et al. (2017). Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle. J. Dairy Sci. 100 5479–5490. 10.3168/jds.2016-11811 [DOI] [PubMed] [Google Scholar]
  20. Boligon A., Albuquerque L. G. D. (2011). Genetic parameters and relationships of heifer pregnancy and age at first calving with weight gain, yearling and mature weight in Nelore cattle. Livestock Sci. 141 12–16. 10.1016/j.livsci.2011.04.009 [DOI] [Google Scholar]
  21. Brzáková M., Svitáková A., Čítek J., Veselá Z., Vostrý L. (2019). Genetic parameters of longevity for improving profitability of beef cattle. J. Anim. Sci. 97 19–28. 10.1093/jas/sky390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Buzanskas M. E., do Amaral Grossi D., Ventura R. V., Schenkel F. S., Chud T. C. S., Stafuzza N. B., et al. (2017). Candidate genes for male and female reproductive traits in Canchim beef cattle. J. Anim. Sci. Biotechnol. 8:67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Camargo G. D., Aspilcueta-Borquis R. R., Fortes M., Porto-Neto R., Cardoso D. F., Santos D., et al. (2015). Prospecting major genes in dairy buffaloes. BMC Genomics 16:872. 10.1186/s12864-015-1986-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Cammack K., Thomas M., Enns R. (2009). Reproductive traits and their heritabilities in beef cattle. Prof. Anim. Sci. 25 517–528. 10.15232/s1080-7446(15)30753-1 [DOI] [Google Scholar]
  25. Canaza-Cayo A. W., Lopes P. S., Cobuci J. A., Martins M. F., Silva M. V. G. B. D. (2018). Genetic parameters of milk production and reproduction traits of Girolando cattle in Brazil. Ital. J. Anim. Sci. 17 22–30. 10.1080/1828051x.2017.1335180 [DOI] [Google Scholar]
  26. Cassell B. G. (2009). Using Heritability for Genetic Improvement. Blacksburg: Virginia Cooperative Extension. [Google Scholar]
  27. Cervantes I., Gutiérrez J. P., Fernández I., Goyache F. (2010). Genetic relationships among calving ease, gestation length, and calf survival to weaning in the Asturiana de los Valles beef cattle breed. J. Anim. Sci. 88 96–101. 10.2527/jas.2009-2066 [DOI] [PubMed] [Google Scholar]
  28. Changhee D., Nidarshani W., Kwanghyun C., Yunho C., Taejeong C., Byungho P., et al. (2013). The effect of age at first calving and calving interval on productive life and lifetime profit in korean holsteins. Asian Austral. J. Anim. Sci. 26 1511–1517. 10.5713/ajas.2013.13105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Chegini A., Hossein-Zadeh N. G., Moghaddam S. H. H., Shadparvar A. A. (2019a). Genetic aspects of some reproductive, udder health and energy status traits in Holstein cows. Theriogenology 130 1–7. 10.1016/j.theriogenology.2019.02.027 [DOI] [PubMed] [Google Scholar]
  30. Chegini A., Shadparvar A. A., Hossein-Zadeh N. G., Mohammad-Nazari B. (2019b). Genetic and environmental relationships among milk yield, persistency of milk yield, somatic cell count and calving interval in Holstein cows. Rev. Colomb. Ciencias Pecuarias 32 81–89. 10.17533/udea.rccp.v32n2a01 [DOI] [Google Scholar]
  31. Cochran S. D., Cole J. B., Null D. J., Hansen P. J. (2013). Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genet. 14:49. 10.1186/1471-2156-14-49 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Costa E. V., Ventura H. T., Veroneze R., Silva F. F., Pereira M. A., Lopes P. S. (2020). Estimated genetic associations among reproductive traits in Nellore cattle using Bayesian analysis. Anim. Reprod. Sci. 214:106305. 10.1016/j.anireprosci.2020.106305 [DOI] [PubMed] [Google Scholar]
  33. Costa R. B., Irano N., Diaz I. D. P. S., Takada L., Da Costa Hermisdorff I., Carvalheiro R., et al. (2019). Prediction of genomic breeding values for reproductive traits in nellore heifers. Theriogenology 125 12–17. 10.1016/j.theriogenology.2018.10.014 [DOI] [PubMed] [Google Scholar]
  34. Cribiu E. P., Di Berardino D., Di Meo G. P., Eggen A., Gallagher D. S., Gustavsson I., et al. (2001). International system for chromosome nomenclature of domestic Bovids (ISCNDB 2000). Cytogenet. Cell Genet. 92 283–299. 10.1159/000056917 [DOI] [PubMed] [Google Scholar]
  35. Darvasi A., Weinreb A., Minke V., Weller J., Soller M. (1993). Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134 943–951. 10.1093/genetics/134.3.943 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. de Araujo Neto F. R., Takada L., Dos Santos D. J. A., Aspilcueta-Borquis R. R., Cardoso D. F., Do Nascimento A. V., et al. (2020). Identification of genomic regions related to age at first calving and first calving interval in water buffalo using single-step GBLUP. Reprod. Domest. Anim. 55 1565–1572. 10.1111/rda.13811 [DOI] [PubMed] [Google Scholar]
  37. de Rezende M. P. G., Malhado C. H. M., Biffani S., Carneiro P. L. S., Carrillo J. A., Bozzi R. (2020). Genotype-environment interaction for age at first calving in Limousine and Charolais cattle raised in Italy, employing reaction norm model. Livestock Sci. 232:103912. 10.1016/j.livsci.2019.103912 [DOI] [Google Scholar]
  38. Dekkers J. (1991). Estimation of economic values for dairy cattle breeding goals: bias due to sub-optimal management policies. Livestock Prod. Sci. 29 131–149. 10.1016/0301-6226(91)90062-u [DOI] [Google Scholar]
  39. do Amaral Grossi D., Berton M. P., Buzanskas M. E., Chud T. C. S., Grupioni N. V., De Paz C. C. P., et al. (2016). Genetic analysis on accumulated productivity and calving intervals in Nelore cattle. Trop. Anim. Health Prod. 48 207–210. 10.1007/s11250-015-0915-3 [DOI] [PubMed] [Google Scholar]
  40. Doyle S., Golden B., Green R., Brinks J. (2000). Additive genetic parameter estimates for heifer pregnancy and subsequent reproduction in Angus females. J. Anim. Sci. 78 2091–2098. 10.2527/2000.7882091x [DOI] [PubMed] [Google Scholar]
  41. Du C., Deng T., Zhou Y., Ye T., Zhou Z., Zhang S., et al. (2019). Systematic analyses for candidate genes of milk production traits in water buffalo (Bubalus Bubalis). Anim. Genet. 50 207–216. 10.1111/age.12739 [DOI] [PubMed] [Google Scholar]
  42. Effa K., Wondatir Z., Dessie T., Haile A. (2011). Genetic and environmental trends in the long-term dairy cattle genetic improvement programmes in the central tropical highlands of Ethiopia. J. Cell Anim. Biol. 5 96–104. [Google Scholar]
  43. Egger-Danner C., Kadlečík O., Fuerst C., Kasarda R. (2005). “Joint genetic evaluation for functional longevity for Pinzgau cattle,” in Proceedings of the 56th Annual Meeting of the European Association for Animal Production, Uppsala. [Google Scholar]
  44. El-Bramony M. M. (2011). Genetic and phenotypic parameters of milk yield and reproductive performance in the first three lactations of Egyptian buffalo. Egypt. J. Anim. Prod. 48 1–10. 10.21608/ejap.2011.94365 [DOI] [Google Scholar]
  45. El-Bramony M. M., Reclamation D. (2014). Estimation of genetic and phenotypic parameters for milk yield, lactation length, calving interval and body weight in the first lactation of Egyptian buffalo. Life Sci. J. 11 1012–1019. [Google Scholar]
  46. Elzo M., Mateescu R., Rae D., Carr C., Scheffler T., Scheffler J., et al. (2018). “Genomic-polygenic EBV for reproduction, ultrasound-carcass, and tenderness traits in the Florida multibreed Brahman-Angus population,” in Proceedings of the World Congress on Genetics Applied to Livestock Production, Rome: 3–7. [Google Scholar]
  47. Engle B. N., Corbet N. J., Allen J. M., Laing A. R., Fordyce G., Mcgowan M. R., et al. (2019). Multivariate genomic predictions for age at puberty in tropically adapted beef heifers. J. Anim. Sci. 97 90–100. 10.1093/jas/sky428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Faraji-Arough H., Rokouei M. (2016). Bayesian inference of genetic parameters for reproductive traits in Sistani native cows using Gibbs sampling. J. Livestock Sci. Technol. 4 39–49. [Google Scholar]
  49. Fernando R., Grossman M. (1989). Marker assisted selection using best linear unbiased prediction. Genet. Select. Evol. 21 1–11. [Google Scholar]
  50. Forni S., Albuquerque L. (2005). Estimates of genetic correlations between days to calving and reproductive and weight traits in Nelore cattle. J. Anim. Sci. 83 1511–1515. 10.2527/2005.8371511x [DOI] [PubMed] [Google Scholar]
  51. Fortes M., Lehnert S., Bolormaa S., Reich C., Fordyce G., Corbet N., et al. (2012). Finding genes for economically important traits: Brahman cattle puberty. Anim. Product. Sci. 52 143–150. 10.1071/an11165 [DOI] [Google Scholar]
  52. Fortes M. R., Reverter A., Kelly M., Mcculloch R., Lehnert S. A. (2013). Genome-wide association study for inhibin, luteinizing hormone, insulin-like growth factor 1, testicular size and semen traits in bovine species. Andrology 1 644–650. 10.1111/j.2047-2927.2013.00101.x [DOI] [PubMed] [Google Scholar]
  53. Forutan M., Mahyari S., Schenkel F., Sargolzaei M. (2018). Improving genomic evaluation of Holstein cattle using a haplotype-based relationship matrix. Iran. J. Anim. Sci. Res. 10 393–402. [Google Scholar]
  54. Gaddis K. L. P., Null D. J., Cole J. B. (2016). Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits. J. Dairy Sci. 99 6420–6435. 10.3168/jds.2015-10444 [DOI] [PubMed] [Google Scholar]
  55. Gaddis K. P., Dikmen S., Null D., Cole J., Hansen P. (2017). Evaluation of genetic components in traits related to superovulation, in vitro fertilization, and embryo transfer in Holstein cattle. J. Dairy Sci. 100 2877–2891. 10.3168/jds.2016-11907 [DOI] [PubMed] [Google Scholar]
  56. Galsar N. S., Shah R., Pandey J. P. D., Prajapati K. (2016). 7. ANALYSIS OF FIRST PRODUCTION AND REPRODUCTION TRAITS OF MEHSANA BUFFALOES MAINTAINED AT TROPICAL AND SEMI-ARID REGION OF GUJARAT, INDIA by NIRALI S. GALSAR 1, RR SHAH 2, JAY PRAKASH GUPTA 3, DP PANDEY 4, K. B. Life Sci. Leaflets 77 65–75. [Google Scholar]
  57. García-Ruiz A., Cole J. B., Vanraden P. M., Wiggans G. R., Ruiz-López F. J., Van Tassell C. P. (2016). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc. Natl. Acad. Sci. U.S.A. 113 E3995–E4004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Georges M., Nielsen D., Mackinnon M., Mishra A., Okimoto R., Pasquino A. T., et al. (1995). Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139 907–920. 10.1093/genetics/139.2.907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Goddard M. (1996). The use of marker haplotypes in animal breeding schemes. Genet. Select. Evol. 28 161–176. 10.1186/1297-9686-28-2-161 [DOI] [Google Scholar]
  60. Gonzálezrecio O., Alenda R. (2005). Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle. J. Dairy Sci. 88 3282–3289. 10.3168/jds.s0022-0302(05)73011-3 [DOI] [PubMed] [Google Scholar]
  61. Goodling R., Jr., Shook G., Weigel K., Zwald N. (2005). The effect of synchronization on genetic parameters of reproductive traits in dairy cattle. J. Dairy Sci. 88 2217–2225. 10.3168/jds.s0022-0302(05)72897-6 [DOI] [PubMed] [Google Scholar]
  62. Goyache F., Gutiérrez J. P. (2001). Heritability of reproduetive traits in Asturiana de los Valles beef cattle breed. Arch. Anim. Breed. 44 489–496. 10.5194/aab-44-489-2001 [DOI] [Google Scholar]
  63. Goyache F., Gutiérrez J. P., Fernández I., Royo L., Álvarez I. (2005). Genetic analysis of days open in beef cattle. Livestock Prod. Sci. 93 283–289. 10.1016/j.livprodsci.2004.10.002 [DOI] [Google Scholar]
  64. Guarini A., Lourenco D., Brito L., Sargolzaei M., Baes C. F., Miglior F., et al. (2018). Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle. J. Dairy Sci. 101 8076–8086. 10.3168/jds.2017-14193 [DOI] [PubMed] [Google Scholar]
  65. Gupta J. P., Sachdeva G. K., Gandhi R., Chakaravarty A. (2015). Developing multiple-trait prediction models using growth and production traits in Murrah buffalo. Buffalo Bull. 34 347–355. [Google Scholar]
  66. Gutiérrez J. P., Álvarez I., Fernández I., Royo L., Dıez J., Goyache F. (2002). Genetic relationships between calving date, calving interval, age at first calving and type traits in beef cattle. Livestock Prod. Sci. 78 215–222. 10.1016/s0301-6226(02)00100-8 [DOI] [Google Scholar]
  67. Heise J., Stock K. F., Reinhardt F., Ha N. T., Simianer H. (2017). Phenotypic and genetic relationships between age at first calving, its component traits, and survival of heifers up to second calving. J. Dairy Sci. 101:S0022030217309967. [DOI] [PubMed] [Google Scholar]
  68. Henderson C. R. (1984). Applications of Linear Models in Animal Breeding. University of Guelph. [Google Scholar]
  69. Hossein Salimi M., Hossein-Zadeh N. G., Shadparvar A. A., Eghbal A. R. (2017). Genetic evaluation of dystocia and its relationship with productive and reproductive traits in Holstein cows. Rev. Colomb. Ciencias Pecuarias 30 126–137. 10.17533/udea.rccp.v30n2a04 [DOI] [Google Scholar]
  70. Hu Z.-L., Park C. A., Reecy J. M. (2019). Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Res. 47 D701–D710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Jaton C., Koeck A., Sargolzaei M., Malchiodi F., Price C., Schenkel F., et al. (2016a). Genetic analysis of superovulatory response of Holstein cows in Canada. J. Dairy Sci. 99 3612–3623. 10.3168/jds.2015-10349 [DOI] [PubMed] [Google Scholar]
  72. Jaton C., Koeck A., Sargolzaei M., Price C., Baes C., Schenkel F., et al. (2016b). Genetic correlations between number of embryos produced using in vivo and in vitro techniques in heifer and cow donors. J. Dairy Sci. 99 8222–8226. 10.3168/jds.2016-11356 [DOI] [PubMed] [Google Scholar]
  73. Jaton C., Schenkel F., Chud T., Malchiodi F., Sargolzaei M., Price C., et al. (2020). Genetic and genomic analyses of embryo production in dairy cattle. Reprod. Fertil. Dev. 32 50–55. 10.1071/rd19275 [DOI] [PubMed] [Google Scholar]
  74. Johnston D., Barwick S., Corbet N., Fordyce G., Holroyd R., Williams P., et al. (2009). Genetics of heifer puberty in two tropical beef genotypes in northern Australia and associations with heifer-and steer-production traits. Anim. Prod. Sci. 49 399–412. 10.1071/ea08276 [DOI] [Google Scholar]
  75. Kadarmideen H., Simm G. (2002). “Selection responses expected from index selection including disease resistance, fertility and longevity in dairy cattle,” in Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier: 01–19. [Google Scholar]
  76. Kale D., Yadav B., Prasad J. (2014). DNA polymorphisms at candidate gene loci and their relation with milk production traits in Murrah Buffalo (Bubalus bubalis) Iran. J. Appl. Anim. Sci. 4 39–43. [Google Scholar]
  77. Kaupe B., Brandt H., Prinzenberg E., Erhardt G. (2007). Joint analysis of the influence of CYP11B1 and DGAT1 genetic variation on milk production, somatic cell score, conformation, reproduction, and productive lifespan in German Holstein cattle. J. Anim. Sci. 85 11–21. 10.2527/jas.2005-753 [DOI] [PubMed] [Google Scholar]
  78. Ke H., Iqbal A., Jj K. (2014). A genome wide association study on age at first calving using high density single nucleotide polymorphism chips in Hanwoo (Bos taurus coreanae). Asian Austral. J. Anim. Sci. 27 1406–1410. 10.5713/ajas.2014.14273 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Kluska S., Olivieri B. F., Bonamy M., Chiaia H. L. J., Feitosa F. L. B., Berton M. P., et al. (2018). Estimates of genetic parameters for growth, reproductive, and carcass traits in Nelore cattle using the single step genomic BLUP procedure. Livestock Sci. 216 203–209. 10.1016/j.livsci.2018.08.015 [DOI] [Google Scholar]
  80. König S., Bosselmann F., Von Borstel U. H. S. (2007). Genetic analysis of traits affecting the success of embryo transfer in dairy cattle. J. Dairy Sci. 90 3945–3954. 10.3168/jds.2007-0089 [DOI] [PubMed] [Google Scholar]
  81. Konkruea T., Koonawootrittriron S., Elzo M. A., Suwanasopee T. (2019). Accuracy of genomic-polygenic and polygenic breeding values for age at first calving and milk yield in thai multibreed dairy cattle. Ann. Anim. Sci. 19 633–645. 10.2478/aoas-2019-0032 [DOI] [Google Scholar]
  82. Kumar V., Chakravarty A., Patil C., Valsalan J., Mahajan A. (2015). Estimate of genetic and non-genetic parameters for age at first calving in Murrah buffalo. Indian J. Anim. Sci. 85 84–85. [Google Scholar]
  83. Laodim T., Elzo M. A., Koonawootrittriron S., Suwanasopee T., Jattawa D. (2019). Genomic-polygenic and polygenic predictions for milk yield, fat yield, and age at first calving in Thai multibreed dairy population using genic and functional sets of genotypes. Livestock Sci. 219 17–24. 10.1016/j.livsci.2018.11.008 [DOI] [Google Scholar]
  84. Lázaro S. F., Varona L., Silva F. F., Ventura H. T., Veroneze R., Brito L. C., et al. (2019). Censored Bayesian models for genetic evaluation of age at first calving in Brazilian Brahman cattle. Livestock Sci. 221 177–180. 10.1016/j.livsci.2018.11.014 [DOI] [Google Scholar]
  85. Lee D., Han K. (2004). Genetic relationship between milk production, calving ease and days open at first parity in Holstein cows. Asian Austral. J. Anim. Sci. 17 153–158. 10.5713/ajas.2004.153 [DOI] [Google Scholar]
  86. Lett B. M., Kirkpatrick B. W. (2018). Short communication: heritability of twinning rate in Holstein cattle. J. Dairy Sci. 101 4307–4311. [DOI] [PubMed] [Google Scholar]
  87. Li J., Liu J., Campanile G., Plastow G., Zhang C., Wang Z., et al. (2018a). Novel insights into the genetic basis of buffalo reproductive performance. BMC Genomics 19:814. 10.1186/s12864-018-5208-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Li J., Liu J., Liu S., Plastow G., Zhang C., Wang Z., et al. (2018b). Integrating RNA-seq and GWAS reveals novel genetic mutations for buffalo reproductive traits. Anim. Reprod. Sci. 197 290–295. 10.1016/j.anireprosci.2018.08.041 [DOI] [PubMed] [Google Scholar]
  89. Liu J. J., Liang A. X., Campanile G., Plastow G., Zhang C., Wang Z., et al. (2018). Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo. J. Dairy Sci. 101 433–444. 10.3168/jds.2017-13246 [DOI] [PubMed] [Google Scholar]
  90. Lopes F., Wu X. L., Li H., Xu J., Perkins T., Genho J., et al. (2018). Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes. J. Anim. Breed. Genet. 135 14–27. 10.1111/jbg.12312 [DOI] [PubMed] [Google Scholar]
  91. Lopez B. I., Son J.-H., Seo K., Lim D. (2019). Estimation of genetic parameters for reproductive traits in Hanwoo (Korean Cattle). Animals 9:715. 10.3390/ani9100715 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Low W. Y., Tearle R., Bickhart D. M., Rosen B. D., Kingan S. B., Swale T., et al. (2019). Chromosome-level assembly of the water buffalo genome surpasses human and goat genomes in sequence contiguity. Nat. Commun. 10 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Luan T., Woolliams J. A., Lien S., Kent M., Svendsen M., Meuwissen T. H. (2009). The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183 1119–1126. 10.1534/genetics.109.107391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Macgregor R. (1941). The domestic buffalo. Vet. Rec. 53 443–450. 10.7313/upo9781907284991.034 [DOI] [PubMed] [Google Scholar]
  95. Makgahlela M., Banga C., Norris D., Dzama K., Ngambi J. (2008). Genetic analysis of age at first calving and calving interval in South African Holstein cattle. Asian J. Anim. Vet. Adv. 3 197–205. 10.3923/ajava.2008.197.205 [DOI] [Google Scholar]
  96. Martínez-Velázquez G., Ríos-Utrera A., Román-Ponce S., Baeza-Rodríguez J., Arechavaleta-Velasco M., Montaño-Bermúdez M., et al. (2020). Genetic correlations between scrotal circumference, heifer fertility and stayability in Charolais–Charbray cattle. Livestock Sci. 232:103914. 10.1016/j.livsci.2019.103914 [DOI] [Google Scholar]
  97. Maryam K., Beiginassiri M., Nejad A. N., Chaji M., Roshanfekr H., Nazari B. M. (2016). Genetic parameter estimation of dystocia variable in iranian holstein dairy cattle. Basrah J. Vet. Res. 15:4. [Google Scholar]
  98. McClure M., Morsci N., Schnabel R., Kim J., Yao P., Rolf M., et al. (2010). A genome scan for quantitative trait loci influencing carcass, post-natal growth and reproductive traits in commercial Angus cattle. Anim. Genet. 41 597–607. 10.1111/j.1365-2052.2010.02063.x [DOI] [PubMed] [Google Scholar]
  99. Meuwissen T. H., Hayes B. J., Goddard M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157 1819–1829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Michelizzi V. N., Dodson M. V., Pan Z., Amaral M. E. J., Michal J. J., Mclean D. J., et al. (2010). Water buffalo genome science comes of age. Int. J. Biol. Sci. 6:333. 10.7150/ijbs.6.333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Miller J., Thomsen P., Dixon S., Tucker E., Konfortov B., Harbitz I. (1992). Synteny mapping of the bovine IGHG2, CRC and IGF1 genes. Anim. Genet. 23 51–58. 10.1111/j.1365-2052.1992.tb00017.x [DOI] [PubMed] [Google Scholar]
  102. Mintoo A. A., Zhang H., Chen C., Moniruzzaman M., Deng T., Anam M., et al. (2019). Draft genome of the river water buffalo. Ecol. Evol. 9 3378–3388. 10.1002/ece3.4965 [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Moioli B., Steri R., Marchitelli C., Catillo G., Buttazzoni L. (2017). Genetic parameters and genome-wide associations of twinning rate in a local breed, the Maremmana cattle. Animal 11 1660–1666. 10.1017/s1751731117000283 [DOI] [PubMed] [Google Scholar]
  104. Montaldo H., Trejo C., Lizana C. (2017). Genetic parameters for milk yield and reproduction traits in the Chilean Dairy Overo Colorado cattle breed. Ciencia Investig. Agraria 44 24–34. [Google Scholar]
  105. Morammazi S., Torshizi R., Rouzbehan Y., Sayyadnejad M. (2007). PosterEstimates of genetic parameters for production and reproduction traits in Khuzestan buffalos of Iran. Italian J. Anim. Sci. 6 421–424. 10.4081/ijas.2007.s2.421 [DOI] [Google Scholar]
  106. Morris C., Amyes N., Hickey S. (2011). Responses of prolactin and hair growth to selection for age at puberty in Angus cattle. Animal 5 198–201. 10.1017/s1751731110001825 [DOI] [PubMed] [Google Scholar]
  107. Morris C., Wilson J., Bennett G., Cullen N., Hickey S., Hunter J. (2000). Genetic parameters for growth, puberty, and beef cow reproductive traits in a puberty selection experiment. N. Zeal. J. Agric. Res. 43 83–91. 10.1080/00288233.2000.9513411 [DOI] [Google Scholar]
  108. Mota R., Guimarães S., Fortes M., Hayes B., Silva F., Verardo L., et al. (2017). Genome-wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle. J. Anim. Breed. Genet. 134 484–492. 10.1111/jbg.12299 [DOI] [PubMed] [Google Scholar]
  109. Mota R. R., E Silva F. F., Guimarães S. E. F., Hayes B., Fortes M. R. S., Kelly M. J., et al. (2018). Benchmarking Bayesian genome enabled-prediction models for age at first calving in Nellore cows. Livestock Sci. 211 75–79. 10.1016/j.livsci.2018.03.009 [DOI] [Google Scholar]
  110. Müller M. P., Rothammer S., Seichter D., Russ I., Hinrichs D., Tetens J., et al. (2017). Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18. J. Dairy Sci. 100 1987–2006. 10.3168/jds.2016-11506 [DOI] [PubMed] [Google Scholar]
  111. Nascimento A. V., Matos M. C., Seno L. O., Romero A. R., Garcia J. F., Grisolia A. B. (2016). Genome wide association study on early puberty in Bos indicus. Genet. Mol. Res. 15 1–6. [DOI] [PubMed] [Google Scholar]
  112. Nayeri S., Sargolzaei M., Abo-Ismail M. K., May N., Miller S. P., Schenkel F., et al. (2016). Genome-wide association for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genet. 17:75. 10.1186/s12863-016-0386-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Neves H. H., Carvalheiro R., O’brien A. M. P., Utsunomiya Y. T., Do Carmo A. S., Schenkel F. S., et al. (2014). Accuracy of genomic predictions in Bos indicus (Nellore) cattle. Genet. Select. Evol. 46:17. 10.1186/1297-9686-46-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Oyama K., Katsuta T., Anada K., Mukai F. (2002). Heritability and repeatability estimates for reproductive traits of Japanese Black cows. Asian Austral. J. Anim. Sci. 15 1680–1685. 10.5713/ajas.2002.1680 [DOI] [Google Scholar]
  115. Peixoto M., Pereira C., Bergmann J., Penna V., Fonseca C. (2004). Genetic parameters of multiple ovulation traits in Nellore females. Theriogenology 62 1459–1464. 10.1016/j.theriogenology.2004.02.019 [DOI] [PubMed] [Google Scholar]
  116. Piccoli M. L., Brito L. F., Braccini J., Oliveira H. R., Cardoso F. F., Roso V. M., et al. (2020). Comparison of genomic prediction methods for evaluation of adaptation and productive efficiency traits in Braford and Hereford cattle. Livestock Sci. 231:103864. 10.1016/j.livsci.2019.103864 [DOI] [Google Scholar]
  117. Piper L., Bindon B., Swan A., Brewer H. (2017). Genetic selection for litter size in cattle. Proc. Assoc. Advmt. Breed. Genet. 21 101–105. [Google Scholar]
  118. Rathod A., Vaidya M., Ali S. S. (2018). Genetic studies of productive and reproductive attributes of surti buffalo in Maharashtra. Int. J. Livestock Res. 8 309–314. 10.5455/ijlr.20171016061752 [DOI] [Google Scholar]
  119. Roxström A., Strandberg E. (2002). Genetic analysis of functional, fertility-, mastitis-, and production-determined length of productive life in Swedish dairy cattle. Livestock Prod. Sci. 74 125–135. 10.1016/s0301-6226(01)00300-1 [DOI] [Google Scholar]
  120. Saatchi M., McClure M. C., Mckay S. D., Rolf M. M., Kim J., Decker J. E., et al. (2011). Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet. Select. Evol. 43:40. 10.1186/1297-9686-43-40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Saowaphak P., Duangjinda M., Plaengkaeo S., Suwannasing R., Boonkum W. (2017). Genetic correlation and genome-wide association study (GWAS) of the length of productive life, days open, and 305-days milk yield in crossbred Holstein dairy cattle. Genet. Mol. Res. 16:16029091. [DOI] [PubMed] [Google Scholar]
  122. Schmidt P., Campos G., Roso V., Souza F., Boligon A. (2019). Genetic analysis of female reproductive efficiency, scrotal circumference and growth traits in Nelore cattle. Theriogenology 128 47–53. 10.1016/j.theriogenology.2019.01.032 [DOI] [PubMed] [Google Scholar]
  123. Seno L. O., Cardoso V. L., El-Faro L., Sesana R. C., Aspilcueta-Borquis R. R., Camargo G. M. F. D., et al. (2010). Genetic parameters for milk yield, age at first calving and interval between first and second calving in milk buffaloes. Ital. J. Anim. Sci. 6 397–400. 10.4081/ijas.2007.s2.397 [DOI] [Google Scholar]
  124. Setiaji A., Oikawa T. (2019). Genetic parameters of reproductive traits from artificial insemination records of Japanese Black cows. Livestock Sci. 229 85–89. 10.1016/j.livsci.2019.09.018 [DOI] [Google Scholar]
  125. Silva M. V., Dos Santos D. J., Boison S. A., Utsunomiya A. T., Carmo A. S., Sonstegard T. S., et al. (2014). The development of genomics applied to dairy breeding. Livestock Sci. 166 66–75. [Google Scholar]
  126. Silvestre A., Martins Â, Santos V., Colaço J. (2019). Genetic parameters of calving ease in dairy cattle using threshold and linear models. Ital. J. Anim. Sci. 18 80–87. 10.1080/1828051x.2018.1482801 [DOI] [Google Scholar]
  127. Tang K.-Q., Li S.-J., Yang W.-C., Yu J.-N., Han L., Li X., et al. (2011). An MspI polymorphism in the inhibin alpha gene and its associations with superovulation traits in Chinese Holstein cows. Mol. Biol. Rep. 38 17–21. 10.1007/s11033-010-0072-8 [DOI] [PubMed] [Google Scholar]
  128. Tarekegn G., Gullstrand P., Strandberg E., Båge R., Rius-Vilarrasa E., Christensen J., et al. (2019). Genetic parameters of endocrine fertility traits based on in-line milk progesterone profiles in Swedish Red and Holstein dairy cows. J. Dairy Sci. 102 11207–11216. 10.3168/jds.2019-16691 [DOI] [PubMed] [Google Scholar]
  129. Thiruvenkadan A., Panneerselvam S., Rajendran R., Murali N. (2010). Analysis on the productive and reproductive traits of Murrah buffalo cows maintained in the coastal region of India. Appl. Anim. Husbandry Rural Dev. 3 1–5. 10.21608/jpd.2006.45174 [DOI] [Google Scholar]
  130. Tiezzi F., Arceo M. E., Cole J. B., Maltecca C. (2018). Including gene networks to predict calving difficulty in holstein, brown swiss and jersey cattle. BMC Genet. 19:20. 10.1186/s12863-018-0606-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  131. Toghiani S. (2012). Genetic relationships between production traits and reproductive performance in Holstein dairy cows. Arch. Anim. Breed. 55 458–468. 10.5194/aab-55-458-2012 [DOI] [Google Scholar]
  132. Toghiani S., Hay E., Sumreddee P., Geary T., Rekaya R., Roberts A. (2017). Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed. J. Anim. Sci. 95 4787–4795. 10.2527/jas2017.1944 [DOI] [PMC free article] [PubMed] [Google Scholar]
  133. Tramonte N. C., Grupioni N. V., Stafuzza N. B., Guidolin D. G. F., Savegnago R. P., Bezerra L. A. F., et al. (2019). Genetic parameters, genetic trends, and principal component analysis for productive and reproductive traits of Guzera beef cattle. Rev. Bras. Zootecnia 48:34. [Google Scholar]
  134. Ulbrich F., Fischer H. (1966). The Chromosomes of the Asiatic Buffalo (Bubalus bubalis) and the African Buffalo (Cyncerus caffer). Hoboken, NJ: Wiley Online Library. [Google Scholar]
  135. van der Linde C., De Jong G., Simai S., Gombacsi P., Wellisch P. (2006). Genetic evaluation for longevity in Hungary. Interbull. Bull. 3:35. [Google Scholar]
  136. Vanderick S., Troch T., Gillon A., Glorieux G., Gengler N. (2015). Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models. J. Anim. Breed. Genet. 131 513–521. 10.1111/jbg.12105 [DOI] [PubMed] [Google Scholar]
  137. Vinothraj S., Subramaniyan A., Venkataramanan R., Joseph C., Sivaselvam S. (2016). Genetic evaluation of reproduction performance of Jersey× Red Sindhi crossbred cows. Vet. World 9:1012. 10.14202/vetworld.2016.1012-1017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  138. Warburton C. L., Engle B. N., Ross E. M., Costilla R., Moore S. S., Corbet N. J., et al. (2020). Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers. Genet. Select. Evol. 52 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  139. Weller J., Ezra E., Ron M. (2017). Invited review: A perspective on the future of genomic selection in dairy cattle. J. Dairy Sci. 100 8633–8644. 10.3168/jds.2017-12879 [DOI] [PubMed] [Google Scholar]
  140. Weller J. I., Golik M., Seroussi E., Ron M., Ezra E. (2008). Detection of quantitative trait loci affecting twinning rate in Israeli Holsteins by the daughter design. J. Dairy Sci. 91 2469–2474. 10.3168/jds.2007-0915 [DOI] [PubMed] [Google Scholar]
  141. Williams J. L., Iamartino D., Pruitt K. D., Sonstegard T., Smith T. P., Low W. Y., et al. (2017). Genome assembly and transcriptome resource for river buffalo, Bubalus bubalis (2 n= 50). Gigascience 6:gix088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  142. Yang W. C., Yang L. G., Riaz H., Tang K. Q., Chen L., Li S. J. (2013). Effects in cattle of genetic variation within the IGF1R gene on the superovulation performance and pregnancy rates after embryo transfer. Anim. Reprod. Sci. 143 24–29. 10.1016/j.anireprosci.2013.10.008 [DOI] [PubMed] [Google Scholar]
  143. Yutaka M., Toshimi B., Mitsuyoshi S. (2015). Genetic analysis of twinning rate and milk yield using a threshold-linear model in Japanese Holsteins. Anim. Sci. J. 86 31–36. 10.1111/asj.12236 [DOI] [PubMed] [Google Scholar]
  144. Zhang Y., Johnston D., Bolormaa S., Hawken R., Tier B. (2014). Genomic selection for female reproduction in Australian tropically adapted beef cattle. Anim. Prod. Sci. 54 16–24. 10.1071/an13016 [DOI] [Google Scholar]
  145. Zhang Z., Kargo M., Liu A., Thomasen J. R., Pan Y., Su G. (2019). Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model. Heredity 123 202–214. 10.1038/s41437-019-0192-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (594.8KB, docx)

Articles from Frontiers in Genetics are provided here courtesy of Frontiers Media SA

RESOURCES