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Genomic Mating As Sustainable Breeding for Chinese Indigenous Ningxiang Pigs

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Genomic Mating As Sustainable Breeding for Chinese Indigenous Ningxiang Pigs PLOS ONE RESEARCH ARTICLE Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs 1 2,3 1,4 2 1 1 Jun HeID , Xiao-Lin Wu *, Qinghua Zeng , Hao Li , Haiming Ma , Juan Jiang , Guilherme J. M. Rosa3, Daniel Gianola3, Richard G. Tait Jr.2, Stewart Bauck2 1 College of Animal Science and Technology, Hunan Agricultural University, Changsha, Hunan, China, 2 Biostatistics and Bioinformatics, Neogen GeneSeek, Lincoln, NE, United States of America, 3 Department of Animal Sciences, University of Wisconsin, Madison, WI, United States of America, 4 Ningxiang Pig Farm of Dalong Livestock Technology Co., Ltd., Ningxiang, Hunan, China a1111111111 * [email protected] a1111111111 a1111111111 a1111111111 a1111111111 Abstract An important economic reason for the loss of local breeds is that they tend to be less produc- tive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and sociologically. In the breeding pro- OPEN ACCESS grams with local breeds, it is crucial to balance the selection for genetic gain and the main- Citation: He J, Wu X-L, Zeng Q, Li H, Ma H, Jiang taining of genetic diversity. These two objectives are often conflicting, and finding the J, et al. (2020) Genomic mating as sustainable optimal point of the trade-off has been a challenge for breeders. Genomic selection (GS) breeding for Chinese indigenous Ningxiang pigs. provides a revolutionary tool for the genetic improvement of farm animals. At the same time, PLoS ONE 15(8): e0236629. https://doi.org/ 10.1371/journal.pone.0236629 it can increase inbreeding and produce a more rapid depletion of genetic variability of the selected traits in future generations. Optimum-contribution selection (OCS) represents an Editor: Xiuchun Tian, University of Connecticut, UNITED STATES approach to maximize genetic gain while constraining inbreeding within a targeted range. In the present study, 515 Ningxiang pigs were genotyped with the Illumina Porcine SNP60 Received: October 28, 2019 array or the GeneSeek Genomic Profiler Porcine 50K array. The Ningxiang pigs were found Accepted: July 10, 2020 to be highly inbred at the genomic level. Average locus-wise inbreeding coefficients were Published: August 14, 2020 0.41 and 0.37 for the two SNP arrays used, whereas genomic inbreeding coefficients based Copyright: © 2020 He et al. This is an open access on runs of homozygosity were 0.24 and 0.25, respectively. Simulated phenotypic data were article distributed under the terms of the Creative used to assess the utility of genomic OCS (GOCS) in comparison with GS without inbreed- Commons Attribution License, which permits ing control. GOCS was conducted under two scenarios, selecting sires only (GOCS_S) or unrestricted use, distribution, and reproduction in any medium, provided the original author and selecting sires and dams (GOCS_SD), while kinships were constrained on selected parents. source are credited. The genetic gain for average daily body weight gain (ADG) per generation was between Data Availability Statement: All the data 18.99 and 20.55 g with GOCS_S, and between 23.20 and 28.92 with GOCS_SD, and it var- (genotype data and simulated phenotypes) used in ied from 25.38 to 48.38 g under GS without controlling inbreeding. While the rate of genetic the present study are available in ArrayExpress, gain per generation obtained using GS was substantially larger than that obtained by the and the accession is E-MTAB-8933. two scenarios of genomic OCS in the beginning generations of selection, the difference in Funding: This work was supported by The Key the genetic gain of ADG between GS and GOCS reduced quickly in latter generations. At Projects of Scientific Research Plan of Hunan generation ten, the difference in the realized rates of genetic gain between GS and Province (2018NK2081) and of Changsha City (kq1801014). JH was financially supported by the GOCS_SD diminished and ended up with even a slightly higher genetic gain with Hundred-Talent Plan of Hunan Province, China GOCS_SD, due to the rapid loss of genetic variance with GS and fixation of causative (XZ2016-08), and the Hunan Co-Innovation Center genes. The rate of inbreeding was mostly maintained below 5% per generation with geno- of Animal Production Safety, China. JJ was mic OCS, whereas it increased to between 10.5% and 15.3% per generation with GS. supported by the Foundation of China Scholarship PLOS ONE | https://doi.org/10.1371/journal.pone.0236629 August 14, 2020 1 / 14 PLOS ONE Genomic mating in Ningxiang pigs Council. XLW, HL, RGTJ, and SB are financially Therefore, genomic OCS appears to be a sustainable strategy for the genetic improvement supported by the Neogen Corporation. These of local breeds such as Ningxiang pigs, but keeping mind that a variety of GOCS methods funders provided support in the form of salaries for the authors [JH, JJ, XLW, HL, RGTJ, and SB], but exist and the optimal forms remain to be exploited further. they did not have any additional role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. Competing interests: QHZ is employed by the Ningxiang Pig Farm of Dalong Livestock Technology Co., Ltd. (China). XLW, HL, RGTJ, and Introduction SB are employed by the Neogen Corporation. The The past century has witnessed the development of a few intensively selected commercial employments do not alter our adherence to PLOS breeds (and lines) in farm animals and the challenge that many more local breeds had been ONE policies on sharing data and materialsº. lost or are endangered [1,2]. The latter situation imposes a high risk to the world's animal pro- duction if these commercial breeds run into genetic problems due to inbreeding or correlated selection responses, or if changes in the production section require traits that are poorly devel- oped in the commercial breeds because there will be no alternatives [1]. Economics is a signifi- cant driver for the loss of local breeds, as they tend to be less productive, and hence having less market value than commercial breeds [2]. Nevertheless, local breeds are irreplaceable, both genetically and sociologically. For example, local breeds often are better adapted to the local environment, and therefore they tend to be more resistant to high temperatures or diseases than commercial breeds. Maximizing genetic gain and maintaining genetic diversity are con- flict objectives in breeding programs [3], and finding the optimal level of trade-off has been a challenge for breeders. This is especially so with a local breed of small population size, which has a higher probability for loss of genetic variability and appearance of inbreeding depression than commercial breeds of large populations. In reality, crossing breeding has been used as an alternative strategy for improving the productivity of local breeds, but it, at the same time, accelerates the risk of losing or endangering local breeds [3,4]. For a sustainable selection that aims at achieving long-term genetic gain, it is crucial to have balanced goals that can control inbreeding by increasing heterozygosity and meanwhile keep genetic diversity of a population. Genomic selection (GS) has been a revolutionary method for the genetic improvement of farm animals [5,6]. GS can accelerate breeding by shortening generation intervals, and to higher selection accuracy, especially for traits of low heritability or difficult to observe. How- ever, GS can elevate the inbreeding rate (ΔF) rapidly, and high-production animals tend to have high kinship coefficients between them. The loss of diversity limits long-term gain for the trait under selection, and it also jeopardizes future breeding for other traits. In contrast, opti- mum-contribution selection (OCS) provides a strategy that balances genetic gain and diversity. OCS allows obtaining desirable genetic gain while constraining the rate of inbreeding in the progeny by restricting relationships between selected parents. Thus, it can maintain sustain- able, long-term genetic gains [7±9]. OCS operates on the theory of genetic contributions [10], which states that the maximum rate of genetic gain, given a constraint on parental relation- ships, is realized when an exact threshold-linear relationship exists between the Mendelian- sampling of ancestral animals and their genetic contributions to the descendants of a popula- tion. Initially developed with a pedigree-based relationship matrix, OCS can align ancestors to the threshold-linear relationship only approximately [9,11]. This is because the true Mende- lian-sampling terms of the ancestors are unknown, and they are estimated from pedigree information based on expectations but not realizations [9,12,13]. For example, the expected relationship between two full-sibs is 0.5, but the realized relationship can deviate considerably from 0.5, depending on the segregation of the parental chromosomes. With the availability of informative SNPs covering the whole genome, one can trace the inheritance of chromosomal segments and estimate relationships between selection candidates more accurately. Therefore, PLOS ONE | https://doi.org/10.1371/journal.pone.0236629 August 14, 2020 2 / 14 PLOS ONE Genomic mating in Ningxiang pigs genomic OCS (GOCS, or OCS with genomic information) can increase the accuracy of assess- ment of Mendelian-sampling terms by aligning ancestors closer to the exact threshold±linear relationships than OCS based on pedigree information only [14]. Optimum contribution selection consists of a variety of algorithms and methods. Generally speaking, OCS maximizes genetic gain under a predefined genomic inbreeding rate by calcu- lating the optimal contribution of all selected candidates to the next generation through a con- strained optimization. Like pedigree OCS, genomic OCS has been refined considerably to accommodate operational breeding constraints, such as restricting the number of individuals contributing to the next generation and imposing upper or lower limits on how much an indi- vidual contributes. Some other forms of genomic mating and relevant computation strategies are also worth mentioning hereafter.
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