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Postprint (328.4Kb) Document downloaded from: http://hdl.handle.net/10459.1/67440 The final publication is available at: https://doi.org/10.1111/jbg.12451 Copyright (c) Blackwell Verlag GmbH, 2019 1 TITLE 2 A genome-wide association study in divergently selected lines in 3 rabbits reveals novel genomic regions associated with litter size 4 traits 5 6 Author 7 Bolívar Samuel Sosa-Madrid1, María Antonia Santacreu1, Agustín Blasco1, Luca 8 Fontanesi2, Romi Natacha Pena3, and Noelia Ibañez-Escriche1. 9 10 Author details 11 Institutional affiliations 12 1 Institute for Animal Science and Technology, Universitat Politècnica de València, 13 46022 Valencia, Spain. 14 2 Department of Agricultural and Food Sciences, Division of Animal Sciences, 15 University of Bologna, 40127 Bologna, Italy. 16 3 Departament de Ciència Animal, Universitat de Lleida–Agrotecnio Center, E- 17 25198 Lleida, Catalonia, Spain. 18 19 E-mail addresses Bolívar Samuel Sosa-Madrid [email protected] ORCID https://orcid.org/0000-0002-4269-7593 María Antonia Santacreu [email protected] Agustín Blasco [email protected] Luca Fontanesi [email protected] Romi Natacha Pena [email protected] Noelia Ibañez-Escriche [email protected] ORCID https://orcid.org/0000-0002-6221-3576 20 21 Corresponding author: Noelia Ibañez-Escriche ([email protected]) 22 23 24 Summary 25 Uterine capacity (UC), defined as the total number of kits from unilaterally 26 ovariectomized does at birth, has a high genetic correlation with litter size. The aim of 27 our research was to identify genomic regions associated with litter size traits through 28 a genome-wide association study using rabbits from a divergent selection experiment 29 for UC. A high-density SNP array (200K) was used to genotype 181 does from a control 30 population, high and low UC lines. Traits included total number born (TNB), number 31 born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE), and 32 embryo, foetal and prenatal survivals at second parity. We implemented Bayes B 33 method and the associations were tested by Bayes factors and the percentage of the 34 genomic variance (GV) explained by windows. Main genomic regions associated with 35 TNB, NBA, IE, and OR were found. These regions explained 7.36%, 1.27%, 6.94%, 36 and 3.34% of GV, respectively. Two consecutive windows on chromosome 17 were 37 associated with TNB, NBA, and IE. This genomic region accounted for 6.32% of GV of 38 TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 39 candidate genes which presented functional annotations linked to some reproductive 40 processes. Our findings suggest that a genomic region on chromosome 17 has an 41 important effect on litter size traits. However, further analyses are needed to validate 42 this region in other maternal rabbit lines. 43 44 Keyword: divergent selection, GWAS, litter size, QTL, rabbits, uterine capacity. 45 3 46 Introduction 47 Litter size has high economic importance in all polytocous livestock species, including 48 rabbits (Cartuche, Pascual, Gómez, & Blasco, 2014) and swine (Quinton, Wilton, 49 Robinson, & Mathur, 2006). However, the selection response for this complex trait, as 50 well for several other reproduction traits, is small. For example, in rabbit selection 51 experiments for litter size the response can be 0.1 kits per generation (see review Khalil 52 & Al-Saef, 2008). This situation encouraged the application of alternative selection 53 strategies based on litter size components such as uterine capacity (UC) (Argente, 54 Santacreu, Climent, Bolet, & Blasco, 1997), ovulation rate (OR) (Laborda, Mocé, 55 Blasco, & Santacreu, 2012), or selection using independent culling levels for OR and 56 litter size (Badawy, Peiró, Blasco, & Santacreu, 2018; Ziadi, Moce, Laborda, Blasco, 57 & Santacreu, 2013). 58 59 Uterine capacity is the prenatal survival when the ovulation is not a limiting factor of 60 litter size and the uterine horn is crowded with embryos (Argente et al., 1997; Blasco, 61 Argente, Haley, & Santacreu, 1994). This trait can be measured as total number of kits 62 at birth under these conditions (Christenson, Leymaster, & Young, 1987; Mocé, 63 Santacreu, Climent, & Blasco, 2004). From 1991 to 1998, the Animal Science 64 Department of “Universitat Politècnica de València” carried out an experiment of 65 divergent selection for UC. After ten generations of selection, the divergence between 66 the two divergent lines (high and low UC lines) was 1.50 kits for UC (Blasco, Ortega, 67 Climent, & Santacreu, 2005), with a correlated response in litter size of 2.35 kits 68 (Santacreu, Mocé, Climent, & Blasco, 2005). Approximately one-half of the response 69 in UC was obtained in the first two generations suggesting the presence of a major 70 locus with large effect segregating in these populations (Argente, Blasco, Ortega, 4 71 Haley, & Visscher, 2003; Blasco et al., 2005). Thus, a candidate gene strategy was 72 carried out to characterize this locus by comparing polymorphisms and expression 73 levels between the two UC lines of a some promising candidates (Argente et al., 2010; 74 Ballester et al., 2013; Peiró et al., 2008). Some of these genes (progesterone receptor 75 - PGR, hydroxysteroid (17-beta) dehydrogenase 4 - HSD17B4, and Endoplasmic 76 Reticulum Oxidoreductase 1 - ERO1) showed different expression levels in the oviduct 77 of the two UC line, remarkably overexpressed in the low UC line, but these result could 78 not identify any putative causal mutations (Argente et al., 2010; Ballester et al., 2013; 79 Peiró et al., 2008). 80 81 The recent availability of an updated rabbit reference genome (Carneiro et al., 2014) 82 and a high-density single nucleotide polymorphisms (SNP) array (Blasco & Pena, 83 2018) has opened new possibilities for more comprehensive genomic analyses in this 84 specie, similar to what is possible in all other major livestock species. Together with 85 these tools, several methods for genome-wide association analyses have been also 86 already developed and applied in many different species (Fan, Du, Gorbach, & 87 Rothschild, 2010). Among them, genome-wide association studies (GWAS) using 88 multi-marker regression approaches can attain better power detection to identify 89 genomic regions associated with a trait than the classical approach of single maker 90 simple regression (López de Maturana et al., 2014; Toosi, Fernando, & Dekkers, 91 2018). 92 93 In this study, we designed a GWAS in rabbit based on the described extreme and 94 divergent lines for UC and applied a Bayesian multi-marker regression approach to 95 identify quantitative trait loci (QTL) affecting litter size traits in this species. 5 96 97 Material and Methods 98 Ethical statement 99 Animal manipulations and the experimental procedures were approved by the Ethical 100 Committee of the Polytechnic University of Valencia, according to Council Directives 101 98/58/EC (European Economic Community, 1998). 102 103 Animals and phenotypes 104 Animals came from an experiment of divergent selection for uterine capacity and a 105 cryopreserved control population (Blasco et al., 2005; Santacreu et al., 2005). After 106 ten generations of selection for uterine capacity, the selection was relaxed. For the 107 current study, we collected blood samples from non-ULO female rabbits. The study 108 involves 90 does of the high UC line, 69 does of the low UC line and 30 does of the 109 control population. All samples of high and low UC lines came from the 11th and 12th 110 generations (Mocé, Santacreu, Climent, & Blasco, 2005; Santacreu et al., 2005). The 111 base population of divergent lines for UC came from the 12th generation of a line 112 selected for number of kits at weaning (named V line). The control population was 113 derived from cryopreserved embryos from the 13th and 15th generations of V line. The 114 embryos were transferred to receptor does to produce a control population which was 115 contemporary to UC females from 11th generation.(Santacreu et al., 2005). 116 117 The traits were recorded at the second parity: NBA, as the number of alive kits at parity; 118 NBD, as the number of dead kits; TNB, as the sum of NBA and NBD; OR, calculated 119 as the number of corpora lutea; IE, calculated as the number of implantation sites by 6 120 laparoscopy at day 12 of the gestation; ES, computed as a ratio IE/OR; FS, as a ratio 121 TNB/IE; and PS, as a ratio TNB/OR (Mocé et al., 2005; Santacreu et al., 2005). 122 123 Genotypes and quality control 124 Genomic DNA was isolated from blood using Favorgen Kit (FABGK 001-2; Favorgen 125 Biotech Corp., Taiwan). We collected 189 samples with a minimum concentration of 126 20 ηg/µl and minimum volume of 45 µl. The concentrations were estimated with 127 Nanodrop ND-1000 (NanoDrop Technologies, Wilmington, DE, USA) and borne out 128 with PicoGreen (Invitrogen Corp. Carlsbad, C.A.). The threshold values for the integrity 129 of DNA were 1.8 OD260 /OD280 and 1.5 OD260 /OD320. The genotyping was performed 130 in The National Genotyping Centre of “Universidad de Santiago de Compostela”. 131 132 Does were genotyped using the Affymetrix Axiom OrcunSNP Array (Affymetrix, Inc. 133 Santa Clara, CA, USA) (Blasco & Pena, 2018). The SNP array contains 199,692 134 molecular markers. Quality control (QC) and genotype calling from raw data in the 135 form of CEL files were implemented with Axiom Analysis Suite v. 4.0 and reanalysed 136 by ZANARDI (Marras et al., 2015). The SNP quality control was performed using the 137 following criteria: call rate ≥ 0.95, P-value > 1.0E-7 for the χ2 test for Hardy Weinberg 138 equilibrium, MAF ≥ 0.03 and only SNPs with known chromosome position. Animal 139 samples were excluded from the dataset for values of dish quality control (DQC) < 140 0.89, missing genotype frequency > 0.03, Plate QC ≤ 0.96 or for failing a Mendelian 141 segregation test.
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