1
|
Bhuiyan MSA, Kim YK, Lee DH, Chung Y, Lee DJ, Kang JM, Lee SH. Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models. Animal 2024; 18:101152. [PMID: 38701710 DOI: 10.1016/j.animal.2024.101152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 05/05/2024] Open
Abstract
The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7-3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0-9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0-3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.
Collapse
Affiliation(s)
- M S A Bhuiyan
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Y K Kim
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea; Quantomic Research & Solution, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - D H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea; Quantomic Research & Solution, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - Y Chung
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - D J Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - J M Kang
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - S H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea.
| |
Collapse
|
2
|
Wu Z, Dou T, Bai L, Han J, Yang F, Wang K, Han X, Qiao R, Li XL, Li XJ. Genomic prediction and genome-wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high-density SNP genotypes in Yunong-black pigs. J Anim Breed Genet 2024; 141:124-137. [PMID: 37822282 DOI: 10.1111/jbg.12830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/13/2023]
Abstract
Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.
Collapse
Affiliation(s)
- Ziyi Wu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Tengfei Dou
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Liyao Bai
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Jinyi Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Feng Yang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Kejun Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xuelei Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Ruimin Qiao
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xiu-Ling Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xin-Jian Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Sanya Institute, Hainan Academy of Agricultural Science, Sanya, Hainan, China
| |
Collapse
|
3
|
Alipanah M, Roudbari Z, Momen M, Esmailizadeh A. Impact of inclusion non-additive effects on genome-wide association and variance's components in Scottish black sheep. Anim Biotechnol 2023; 34:3765-3773. [PMID: 37343283 DOI: 10.1080/10495398.2023.2224845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Abstract
CONTEXT It's well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection. AIM This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep. METHODS This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used. KEY RESULTS The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model. CONCLUSIONS The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16-24 weeks in Scottish Blackface lambs. IMPLICATIONS It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.
Collapse
Affiliation(s)
- Masoud Alipanah
- Department of Plant Production, University of Torbat Heydarieh, Torbat-e Heydarieh, Iran
| | - Zahra Roudbari
- Department of Animal Science, University of Jiroft, Jiroft, Iran
| | - Mehdi Momen
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Ali Esmailizadeh
- Department of Animal Science, Shahid Bahonar University of Kerman, Kerman, Iran
| |
Collapse
|
4
|
Heidaritabar M, Bink MCAM, Dervishi E, Charagu P, Huisman A, Plastow GS. Genome-wide association studies for additive and dominance effects for body composition traits in commercial crossbred Piétrain pigs. J Anim Breed Genet 2023. [PMID: 36883263 DOI: 10.1111/jbg.12768] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/18/2023] [Indexed: 03/09/2023]
Abstract
Fat depth (FD) and muscle depth (MD) are economically important traits and used to estimate carcass lean content (LMP), which is one of the main breeding objectives in pig breeding programmes. We assessed the genetic architectures of body composition traits for additive and dominance effects in commercial crossbred Piétrain pigs using both 50 K array and sequence genotypes. We first performed a genome-wide association study (GWAS) using single-marker association analysis with a false discovery rate of 0.1. Then, we estimated the additive and dominance effects of the most significant variant in the quantitative trait loci (QTL) regions. It was investigated whether the use of whole-genome sequence (WGS) will improve the QTL detection (both additive and dominance) with a higher power compared with lower density SNP arrays. Our results showed that more QTL regions were detected by WGS compared with 50 K array (n = 54 vs. n = 17). Of the novel associated regions associated with FD and LMP and detected by WGS, the most pronounced peak was on SSC13, situated at ~116-118, 121-127 and 129-134 Mbp. Additionally, we found that only additive effects contributed to the genetic architecture of the analysed traits and no significant dominance effects were found for the tested SNPs at QTL regions, regardless of panel density. The associated SNPs are located in or near several relevant candidate genes. Of these genes, GABRR2, GALR1, RNGTT, CDH20 and MC4R have been previously reported as being associated with fat deposition traits. However, the genes on SSC1 (ZNF292, ORC3, CNR1, SRSF12, MDN1, TSHZ1, RELCH and RNF152) and SSC18 (TTC26 and KIAA1549) have not been reported previously to our best knowledge. Our current findings provide insights into the genomic regions influencing composition traits in Piétrain pigs.
Collapse
Affiliation(s)
- Marzieh Heidaritabar
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Marco C A M Bink
- Hendrix Genetics Research, Technology & Services B.V., Boxmeer, the Netherlands
| | - Elda Dervishi
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Patrick Charagu
- Hendrix Genetics, Swine Business Unit, Regina, Saskatchewan, Canada
| | - Abe Huisman
- Hendrix Genetics Research, Technology & Services B.V., Boxmeer, the Netherlands
| | - Graham S Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| |
Collapse
|
5
|
Schneider H, Heise J, Tetens J, Thaller G, Wellmann R, Bennewitz J. Genomic dominance variance analysis of health and milk production traits in German Holstein cattle. J Anim Breed Genet 2023. [PMID: 36872841 DOI: 10.1111/jbg.12765] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/12/2023] [Indexed: 03/07/2023]
Abstract
Genomic analyses commonly explore the additive genetic variance of traits. The non-additive variance, however, is usually small but often significant in dairy cattle. This study aimed at dissecting the genetic variance of eight health traits that recently entered the total merit index in Germany and the somatic cell score (SCS), as well as four milk production traits by analysing additive and dominance variance components. The heritabilities were low for all health traits (between 0.033 for mastitis and 0.099 for SCS), and moderate for the milk production traits (between 0.261 for milk energy yield and 0.351 for milk yield). For all traits, the contribution of dominance variance to the phenotypic variance was low, varying between 0.018 for ovarian cysts and 0.078 for milk yield. Inbreeding depression, inferred from the SNP-based observed homozygosity, was significant only for the milk production traits. The contribution of dominance variance to the genetic variance was larger for the health traits, ranging from 0.233 for ovarian cysts to 0.551 for mastitis, encouraging further studies that aim at discovering QTLs based on their additive and dominance effects.
Collapse
Affiliation(s)
- Helen Schneider
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Johannes Heise
- Vereinigte Informationssysteme Tierhaltung w.V. (VIT), Verden, Germany
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, Göttingen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, Kiel, Germany
| | - Robin Wellmann
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| |
Collapse
|
6
|
Mohammadpanah M, Ayatollahi Mehrgardi A, Gilbert H, Larzul C, Mercat MJ, Esmailizadeh A, Momen M, Tusell L. Genic and non-genic SNP contributions to additive and dominance genetic effects in purebred and crossbred pig traits. Sci Rep 2022; 12:3795. [PMID: 35264636 PMCID: PMC8907311 DOI: 10.1038/s41598-022-07767-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/19/2022] [Indexed: 11/09/2022] Open
Abstract
The present research has estimated the additive and dominance genetic variances of genic and intergenic segments for average daily gain (ADG), backfat thickness (BFT) and pH of the semimembranosus dorsi muscle (PHS). Further, the predictive performance using additive and additive dominance models in a purebred Piétrain (PB) and a crossbred (Piétrain × Large White, CB) pig population was assessed. All genomic regions contributed equally to the additive and dominance genetic variations and lead to the same predictive ability that did not improve with the inclusion of dominance genetic effect and inbreeding in the models. Using all SNPs available, additive genotypic correlations between PB and CB performances for the three traits were high and positive (> 0.83) and dominance genotypic correlation was very inaccurate. Estimates of dominance genotypic correlations between all pairs of traits in both populations were imprecise but positive for ADG-BFT in CB and BFT-PHS in PB and CB with a high probability (> 0.98). Additive and dominance genotypic correlations between BFT and PHS were of different sign in both populations, which could indicate that genes contributing to the additive genetic progress in both traits would have an antagonistic effect when used for exploiting dominance effects in planned matings.
Collapse
Affiliation(s)
- Mahshid Mohammadpanah
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran
| | - Ahmad Ayatollahi Mehrgardi
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran.
| | - Hélène Gilbert
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
| | - Catherine Larzul
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
| | | | - Ali Esmailizadeh
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman (SBUK), Kerman, Iran
| | - Mehdi Momen
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Llibertat Tusell
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France.,Animal Breeding and Genetics Program, Institute of Agriculture and Food Research and Technology (IRTA), Torre Marimon s/n, Caldes de Montbui, 08140, Barcelona, Spain
| |
Collapse
|
7
|
Kenny D, Carthy TR, Murphy CP, Sleator RD, Evans RD, Berry DP. The Association Between Genomic Heterozygosity and Carcass Merit in Cattle. Front Genet 2022; 13:789270. [PMID: 35281838 PMCID: PMC8908906 DOI: 10.3389/fgene.2022.789270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/25/2022] [Indexed: 12/16/2022] Open
Abstract
The objective of the present study was to quantify the association between both pedigree and genome-based measures of global heterozygosity and carcass traits, and to identify single nucleotide polymorphisms (SNPs) exhibiting non-additive associations with these traits. The carcass traits of interest were carcass weight (CW), carcass conformation (CC) and carcass fat (CF). To define the genome-based measures of heterozygosity, and to quantify the non-additive associations between SNPs and the carcass traits, imputed, high-density genotype data, comprising of 619,158 SNPs, from 27,213 cattle were used. The correlations between the pedigree-based heterosis coefficient and the three defined genomic measures of heterozygosity ranged from 0.18 to 0.76. The associations between the different measures of heterozygosity and the carcass traits were biologically small, with positive associations for CW and CC, and negative associations for CF. Furthermore, even after accounting for the pedigree-based heterosis coefficient of an animal, part of the remaining variability in some of the carcass traits could be captured by a genomic heterozygosity measure. This signifies that the inclusion of both a heterosis coefficient based on pedigree information and a genome-based measure of heterozygosity could be beneficial to limiting bias in predicting additive genetic merit. Finally, one SNP located on Bos taurus (BTA) chromosome number 5 demonstrated a non-additive association with CW. Furthermore, 182 SNPs (180 SNPs on BTA 2 and two SNPs on BTA 21) demonstrated a non-additive association with CC, while 231 SNPs located on BTA 2, 5, 11, 13, 14, 18, 19 and 21 demonstrated a non-additive association with CF. Results demonstrate that heterozygosity both at a global level and at the level of individual loci contribute little to the variability in carcass merit.
Collapse
Affiliation(s)
- David Kenny
- Animal and Grassland Research and Innovation Centre, Teagasc, Fermoy, Ireland
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Tara R. Carthy
- Animal and Grassland Research and Innovation Centre, Teagasc Grange, Dunsany, Ireland
| | - Craig P. Murphy
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Roy D. Sleator
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | | | - Donagh P. Berry
- Animal and Grassland Research and Innovation Centre, Teagasc, Fermoy, Ireland
- *Correspondence: Donagh P. Berry,
| |
Collapse
|
8
|
Duenk P, Bijma P, Wientjes YCJ, Calus MPL. Review: optimizing genomic selection for crossbred performance by model improvement and data collection. J Anim Sci 2021; 99:skab205. [PMID: 34223907 PMCID: PMC8499581 DOI: 10.1093/jas/skab205] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/02/2021] [Indexed: 11/26/2022] Open
Abstract
Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred (CB) performance for purebred (PB) selection candidates. In this review, we compared genomic prediction strategies that differed in 1) the genomic prediction model used or 2) the data used in the reference population. We found 27 unique studies, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data. Differences in accuracy and response to selection between strategies depended on i) the value of the purebred crossbred genetic correlation (rpc), ii) the genetic distance between the parental lines, iii) the size of PB and CB reference populations, and iv) the relatedness of these reference populations to the selection candidates. In studies where a PB reference population was used, the use of a dominance model yielded accuracies that were equal to or higher than those of additive models. When rpc was lower than ~0.8, and was caused mainly by G × E, it was beneficial to create a reference population of PB animals that are tested in a CB environment. In general, the benefit of collecting CB information increased with decreasing rpc. For a given rpc, the benefit of collecting CB information increased with increasing size of the reference populations. Collecting CB information was not beneficial when rpc was higher than ~0.9, especially when the reference populations were small. Collecting only phenotypes of CB animals may slightly improve accuracy and response to selection, but requires that the pedigree is known. It is, therefore, advisable to genotype these CB animals as well. Finally, considering the breed-origin of alleles allows for modeling breed-specific effects in the CB, but this did not always lead to higher accuracies. Our review shows that the differences in accuracy and response to selection between strategies depend on several factors. One of the most important factors is rpc, and we, therefore, recommend to obtain accurate estimates of rpc of all breeding goal traits. Furthermore, knowledge about the importance of components of rpc (i.e., dominance, epistasis, and G × E) can help breeders to decide which model to use, and whether to collect data on animals in a CB environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.
Collapse
Affiliation(s)
- Pascal Duenk
- Animal Breeding and Genomics, Wageningen University and
Research, P.O. Box 338, 6700 AH Wageningen,
The Netherlands
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University and
Research, P.O. Box 338, 6700 AH Wageningen,
The Netherlands
| | - Yvonne C J Wientjes
- Animal Breeding and Genomics, Wageningen University and
Research, P.O. Box 338, 6700 AH Wageningen,
The Netherlands
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University and
Research, P.O. Box 338, 6700 AH Wageningen,
The Netherlands
| |
Collapse
|
9
|
Yadav S, Wei X, Joyce P, Atkin F, Deomano E, Sun Y, Nguyen LT, Ross EM, Cavallaro T, Aitken KS, Hayes BJ, Voss-Fels KP. Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2235-2252. [PMID: 33903985 PMCID: PMC8263546 DOI: 10.1007/s00122-021-03822-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/21/2021] [Indexed: 05/29/2023]
Abstract
Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.
Collapse
Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, 4741, Australia
| | - Priya Joyce
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa, Gordonvale, QLD, 4865, Australia
| | - Emily Deomano
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Yue Sun
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Loan T Nguyen
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Tony Cavallaro
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Karen S Aitken
- Agriculture and Food, CSIRO, QBP, St. Lucia, QLD, 4067, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia.
| |
Collapse
|
10
|
Zhu S, Zhao H, Han M, Yuan C, Guo T, Liu J, Yue Y, Qiao G, Wang T, Li F, Gun S, Yang B. Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep. Front Vet Sci 2020; 7:573692. [PMID: 33263012 PMCID: PMC7686030 DOI: 10.3389/fvets.2020.573692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 09/16/2020] [Indexed: 11/17/2022] Open
Abstract
Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.
Collapse
Affiliation(s)
- Shaohua Zhu
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Hongchang Zhao
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Mei Han
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Chao Yuan
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tingting Guo
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jianbin Liu
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yaojing Yue
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Guoyan Qiao
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tianxiang Wang
- Gansu Provincial Sheep Breeding Technology Extension Station, Sunan, China
| | - Fanwen Li
- Gansu Provincial Sheep Breeding Technology Extension Station, Sunan, China
| | - Shuangbao Gun
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Bohui Yang
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
| |
Collapse
|
11
|
Amorim ST, Yu H, Momen M, de Albuquerque LG, Cravo Pereira AS, Baldi F, Morota G. An assessment of genomic connectedness measures in Nellore cattle. J Anim Sci 2020; 98:skaa289. [PMID: 32877515 PMCID: PMC7792904 DOI: 10.1093/jas/skaa289] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 08/27/2020] [Indexed: 12/17/2022] Open
Abstract
An important criterion to consider in genetic evaluations is the extent of genetic connectedness across management units (MU), especially if they differ in their genetic mean. Reliable comparisons of genetic values across MU depend on the degree of connectedness: the higher the connectedness, the more reliable the comparison. Traditionally, genetic connectedness was calculated through pedigree-based methods; however, in the era of genomic selection, this can be better estimated utilizing new approaches based on genomics. Most procedures consider only additive genetic effects, which may not accurately reflect the underlying gene action of the evaluated trait, and little is known about the impact of non-additive gene action on connectedness measures. The objective of this study was to investigate the extent of genomic connectedness measures, for the first time, in Brazilian field data by applying additive and non-additive relationship matrices using a fatty acid profile data set from seven farms located in the three regions of Brazil, which are part of the three breeding programs. Myristic acid (C14:0) was used due to its importance for human health and reported presence of non-additive gene action. The pedigree included 427,740 animals and 925 of them were genotyped using the Bovine high-density genotyping chip. Six relationship matrices were constructed, parametrically and non-parametrically capturing additive and non-additive genetic effects from both pedigree and genomic data. We assessed genome-based connectedness across MU using the prediction error variance of difference (PEVD) and the coefficient of determination (CD). PEVD values ranged from 0.540 to 1.707, and CD from 0.146 to 0.456. Genomic information consistently enhanced the measures of connectedness compared to the numerator relationship matrix by at least 63%. Combining additive and non-additive genomic kernel relationship matrices or a non-parametric relationship matrix increased the capture of connectedness. Overall, the Gaussian kernel yielded the largest measure of connectedness. Our findings showed that connectedness metrics can be extended to incorporate genomic information and non-additive genetic variation using field data. We propose that different genomic relationship matrices can be designed to capture additive and non-additive genetic effects, increase the measures of connectedness, and to more accurately estimate the true state of connectedness in herds.
Collapse
Affiliation(s)
- Sabrina T Amorim
- Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias,
Departamento de Zootecnia, Via de acesso Prof. Paulo Donato Castellane, CEP
Jaboticabal, SP, Brazil
| | - Haipeng Yu
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and
State University, Blacksburg, VA
| | - Mehdi Momen
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and
State University, Blacksburg, VA
| | - Lúcia Galvão de Albuquerque
- Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias,
Departamento de Zootecnia, Via de acesso Prof. Paulo Donato Castellane, CEP
Jaboticabal, SP, Brazil
| | - Angélica S Cravo Pereira
- Universidade de São Paulo, Faculdade de Zootecnia e Engenharia de Alimentos,
Núcleo de Apoio à Pesquisa em Melhoramento Animal, Biotecnologia e
Transgenia, Rua Duque de Caxias Norte, CEP Pirassununga, SP, Brazil
| | - Fernando Baldi
- Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias,
Departamento de Zootecnia, Via de acesso Prof. Paulo Donato Castellane, CEP
Jaboticabal, SP, Brazil
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and
State University, Blacksburg, VA
| |
Collapse
|
12
|
Pégard M, Segura V, Muñoz F, Bastien C, Jorge V, Sanchez L. Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar. FRONTIERS IN PLANT SCIENCE 2020; 11:581954. [PMID: 33193528 PMCID: PMC7655903 DOI: 10.3389/fpls.2020.581954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.
Collapse
Affiliation(s)
| | - Vincent Segura
- BioForA, INRA, ONF, Orléans, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | | | | | | | | |
Collapse
|
13
|
The Impact of Non-additive Effects on the Genetic Correlation Between Populations. G3-GENES GENOMES GENETICS 2020; 10:783-795. [PMID: 31857332 PMCID: PMC7003072 DOI: 10.1534/g3.119.400663] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.
Collapse
|
14
|
Garcia-Baccino CA, Lourenco DAL, Miller S, Cantet RJC, Vitezica ZG. Estimating dominance genetic variances for growth traits in American Angus males using genomic models. J Anim Sci 2020; 98:skz384. [PMID: 31867623 PMCID: PMC6978891 DOI: 10.1093/jas/skz384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/19/2019] [Indexed: 12/11/2022] Open
Abstract
Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.
Collapse
Affiliation(s)
- Carolina A Garcia-Baccino
- Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | | | - Rodolfo J C Cantet
- Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina
- INPA, UBA-CONICET, Buenos Aires, Argentina
| | | |
Collapse
|
15
|
Tusell L, Gilbert H, Vitezica ZG, Mercat MJ, Legarra A, Larzul C. Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits. Animal 2019; 13:2429-2439. [PMID: 31120005 DOI: 10.1017/s1751731119001046] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The partition of the total genetic variance into its additive and non-additive components can differ from trait to trait, and between purebred and crossbred populations. A quantification of these genetic variance components will determine the extent to which it would be of interest to account for dominance in genomic evaluations or to establish mate allocation strategies along different populations and traits. This study aims at assessing the contribution of the additive and dominance genomic variances to the phenotype expression of several purebred Piétrain and crossbred (Piétrain × Large White) pig performances. A total of 636 purebred and 720 crossbred male piglets were phenotyped for 22 traits that can be classified into six groups of traits: growth rate and feed efficiency, carcass composition, meat quality, behaviour, boar taint and puberty. Additive and dominance variances estimated in univariate genotypic models, including additive and dominance genotypic effects, and a genomic inbreeding covariate allowed to retrieve the additive and dominance single nucleotide polymorphism variances for purebred and crossbred performances. These estimated variances were used, together with the allelic frequencies of the parental populations, to obtain additive and dominance variances in terms of genetic breeding values and dominance deviations. Estimates of the Piétrain and Large White allelic contributions to the crossbred variance were of about the same magnitude in all the traits. Estimates of additive genetic variances were similar regardless of the inclusion of dominance. Some traits showed relevant amount of dominance genetic variance with respect to phenotypic variance in both populations (i.e. growth rate 8%, feed conversion ratio 9% to 12%, backfat thickness 14% to 12%, purebreds-crossbreds). Other traits showed higher amount in crossbreds (i.e. ham cut 8% to 13%, loin 7% to 16%, pH semimembranosus 13% to 18%, pH longissimus dorsi 9% to 14%, androstenone 5% to 13% and estradiol 6% to 11%, purebreds-crossbreds). It was not encountered a clear common pattern of dominance expression between groups of analysed traits and between populations. These estimates give initial hints regarding which traits could benefit from accounting for dominance for example to improve genomic estimated breeding value accuracy in genetic evaluations or to boost the total genetic value of progeny by means of assortative mating.
Collapse
Affiliation(s)
- L Tusell
- GenPhySE, Université de Toulouse, Institut National de la Recherche Agronomique, Institut National Polytechnique de Toulouse, Institut National Polytechnique - École Nationale Vétérinaire de Toulouse, 31320, Castanet-Tolosan, France
| | - H Gilbert
- GenPhySE, Université de Toulouse, Institut National de la Recherche Agronomique, Institut National Polytechnique de Toulouse, Institut National Polytechnique - École Nationale Vétérinaire de Toulouse, 31320, Castanet-Tolosan, France
| | - Z G Vitezica
- GenPhySE, Université de Toulouse, Institut National de la Recherche Agronomique, Institut National Polytechnique de Toulouse, Institut National Polytechnique - École Nationale Vétérinaire de Toulouse, 31320, Castanet-Tolosan, France
| | - M J Mercat
- IFIP Institut du Porc/ALLIANCE R&S, La Motte au Vicomte, 35651 Le Rheu, France
| | - A Legarra
- GenPhySE, Université de Toulouse, Institut National de la Recherche Agronomique, Institut National Polytechnique de Toulouse, Institut National Polytechnique - École Nationale Vétérinaire de Toulouse, 31320, Castanet-Tolosan, France
| | - C Larzul
- GenPhySE, Université de Toulouse, Institut National de la Recherche Agronomique, Institut National Polytechnique de Toulouse, Institut National Polytechnique - École Nationale Vétérinaire de Toulouse, 31320, Castanet-Tolosan, France
| |
Collapse
|
16
|
González-Diéguez D, Tusell L, Carillier-Jacquin C, Bouquet A, Vitezica ZG. SNP-based mate allocation strategies to maximize total genetic value in pigs. Genet Sel Evol 2019; 51:55. [PMID: 31558151 PMCID: PMC6764135 DOI: 10.1186/s12711-019-0498-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 09/20/2019] [Indexed: 11/10/2022] Open
Abstract
Background Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored. Results Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. Conclusions Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain.
Collapse
Affiliation(s)
| | - Llibertat Tusell
- GenPhySE, INRA, Université de Toulouse, 31326, Castanet-Tolosan, France
| | | | - Alban Bouquet
- IFIP Institut du Porc, BP35104, 35651, Le Rheu, France.,France Génétique Porc, BP35104, 35651, Le Rheu, France
| | - Zulma G Vitezica
- GenPhySE, INRA, Université de Toulouse, 31326, Castanet-Tolosan, France
| |
Collapse
|
17
|
Herzig AF, Nutile T, Ruggiero D, Ciullo M, Perdry H, Leutenegger AL. Detecting the dominance component of heritability in isolated and outbred human populations. Sci Rep 2018; 8:18048. [PMID: 30575761 PMCID: PMC6303332 DOI: 10.1038/s41598-018-36050-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 11/10/2018] [Indexed: 11/21/2022] Open
Abstract
Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets, characteristic of genetic isolates and of unrelated individuals, before analysing the isolate of Cilento for various commonly studied traits. We show the strengths of using genetic relationship matrices for variance decomposition over identity-by-descent based methods in a population isolate and that heritability estimates in isolates will avoid the downward biases that may occur in studies of samples of unrelated individuals; irrespective of the simulated distribution of causal variants. Yet, we also show that precise estimates of dominance in isolates are demonstrably problematic in the presence of shared environmental effects and such effects should be accounted for. Nevertheless, we demonstrate how studying isolates can help determine the existence or non-existence of dominance for complex traits, and we find strong indications of non-zero dominance for low-density lipoprotein level in Cilento. Finally, we recommend future study designs to analyse trait variance decomposition from ensemble data across multiple population isolates.
Collapse
Affiliation(s)
- Anthony F Herzig
- Inserm, U946, Genetic variation and Human diseases, Paris, France. .,Université Paris-Diderot, Sorbonne Paris Cité, U946, Paris, France.
| | - Teresa Nutile
- Institute of Genetics and Biophysics A. Buzzati-Traverso - CNR, Naples, Italy
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics A. Buzzati-Traverso - CNR, Naples, Italy.,IRCCS Neuromed, Pozzilli, Isernia, Italy
| | - Marina Ciullo
- Institute of Genetics and Biophysics A. Buzzati-Traverso - CNR, Naples, Italy. .,IRCCS Neuromed, Pozzilli, Isernia, Italy.
| | - Hervé Perdry
- Université Paris-Saclay, University. Paris-Sud, Inserm, CESP, Villejuif, France
| | - Anne-Louise Leutenegger
- Inserm, U946, Genetic variation and Human diseases, Paris, France.,Université Paris-Diderot, Sorbonne Paris Cité, U946, Paris, France
| |
Collapse
|
18
|
Raidan FSS, Porto-Neto LR, Li Y, Lehnert SA, Vitezica ZG, Reverter A. Evaluation of nonadditive effects in yearling weight of tropical beef cattle. J Anim Sci 2018; 96:4028-4034. [PMID: 30032181 DOI: 10.1093/jas/sky275] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 07/08/2018] [Indexed: 01/08/2023] Open
Abstract
Nonadditive effects may contribute to genetic variation of complex traits. Their inclusion in genetic evaluation models may therefore improve breeding value estimates and lead to more accurate selection decisions. In this study, we evaluated a systematic series of models accounting for additive, dominance and first-order epistatic interaction (additive by additive, GxG; additive by dominance, GxD; and dominance by dominance, DxD) on body yearling weight (YWT) of 2,550 Tropical Composite (TC) and 2,111 Brahman (BB) cattle in Australia. For both breeds, similar estimates of additive and phenotypic variances and narrow and broad-sense heritability values were obtained across the evaluated models except when GxG effect was considered. In this case, additive variance was slightly lower than that obtained in the models which do not consider this effect. The estimated dominance and epistatic variances from additive and dominance effects (AD) and additive, dominance and epistatic effects models (ADE) were greater than that ADH and ADEH models (as described above plus heterozygosity as a covariate). However, all genetic parameter estimates were associated with a large standard deviation. Averaged across ADH and ADHE models, the magnitude of dominance variance compared to the phenotypic variance of YWT was 14% (BB) and 10% (TC). The magnitude of epistasis compared to the phenotypic variance for BB and TC was 17% and 29%, respectively for GxG; 0.7% and 0% for GxD; and 0% and 0% for DxD. The inclusion of nonadditive effects slightly improves the predictive accuracy of breeding values from 0.28 for A to 0.33 for ADHEGxG and from 0.18 for A to 0.23 ADEGxD in BB and TC cattle. Models that included heterozygosity (ADH and ADHE) must be used to estimate nonadditive genetic variance components. A 1 Mb sliding window analysis identified a region on BTA 14 explaining 10.08% and 1.21% of total genetic variance (additive + dominance) of YWT in BB and TC, respectively. Our results suggest that dominance, epistasis, and heterozygosity should be included in models for genetic parameters estimation. To identify the animals with the highest additive genetic value in selection decisions, only the additive effect should be used in evaluation models.
Collapse
Affiliation(s)
- Fernanda S S Raidan
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | - Laercio R Porto-Neto
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | - Yutao Li
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | - Sigrid A Lehnert
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | | | - Antonio Reverter
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| |
Collapse
|
19
|
Varona L, Legarra A, Toro MA, Vitezica ZG. Non-additive Effects in Genomic Selection. Front Genet 2018; 9:78. [PMID: 29559995 PMCID: PMC5845743 DOI: 10.3389/fgene.2018.00078] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 02/19/2018] [Indexed: 12/02/2022] Open
Abstract
In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.
Collapse
Affiliation(s)
- Luis Varona
- Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Zaragoza, Spain.,Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain
| | - Andres Legarra
- Génétique Physiologie et Systèmes d'Elevage (GenPhySE), Institut National de la Recherche Agronomique de Toulouse, Castanet-Tolosan, France
| | - Miguel A Toro
- Departamento Producción Agraria, ETS Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | - Zulma G Vitezica
- Génétique Physiologie et Systèmes d'Elevage (GenPhySE), Université de Toulouse, Castanet-Tolosan, France
| |
Collapse
|