G C A T T A C G G C A T genes Communication Genomic Scanning of Inbreeding Depression for Litter Size in Two Varieties of Iberian Pigs Carlos Hervás-Rivero 1 , Houssemeddine Srihi 1 , David López-Carbonell 1 , Joaquim Casellas 2 , Noelia Ibáñez-Escriche 3 , Sara Negro 4 and Luis Varona 1, * 1 Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, 50013 Zaragoza, Spain; chervas@unizar.es (C.H.-R.); davidlc@unizar.es (D.L.-C.) 2 Department Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain 3 Instituto Universitario de Ciencia y Tecnología Animal, Universitat Politècnica de València, 46022 Valencia, Spain 4 Programa de Mejora Genética “Castua”, INGA FOOD S. A. (Nutreco), 06200 Almendralejo, Spain * Correspondence: lvarona@unizar.es Abstract: Inbreeding depression is expected to be more pronounced in fitness-related traits, such as pig litter size. Recent studies have suggested that the genetic determinism of inbreeding depression may be heterogeneous across the genome. Therefore, the objective of this study was to conduct a genomic scan of the whole pig autosomal genome to detect the genomic regions that control inbreeding depression for litter size in two varieties of Iberian pigs (Entrepelado and Retinto). The datasets consisted of 2069 (338 sows) and 2028 (327 sows) records of litter size (Total Number Born and Number Born Alive) for the Entrepelado and Retinto varieties. All sows were genotyped using the Geneseek GGP PorcineHD 70 K chip. We employed the Unfavorable Haplotype Finder software to extract runs of homozygosity (ROHs) and conducted a mixed-model analysis to identify highly significant differences between homozygous and heterozygous sows for each specific ROH. A total of eight genomic regions located on SSC2, SSC5, SSC7, SSC8, and SSC13 were significantly associated with inbreeding depression, housing some relevant genes such as FSHR, LHCGR, CORIN, AQP6, Citation: Hervás-Rivero, C.; Srihi, H.; and CEP120. López-Carbonell, D.; Casellas, J.; Ibáñez-Escriche, N.; Negro, S.; Keywords: Iberian pig; litter size; inbreeding depression Varona, L. Genomic Scanning of Inbreeding Depression for Litter Size in Two Varieties of Iberian Pigs. Genes 2023, 14, 1941. https://doi.org/ 10.3390/genes14101941 1. Introduction The most significant consequence of inbreeding in the phenotypic performance of Academic Editors: Linyuan Shen and livestock populations is the occurrence of inbreeding depression [1]. Theoretically, inbreed- Mailin Gan ing depression arises from two genetic mechanisms, the impact from recessive mutations Received: 25 September 2023 and the loss of contributions from of over-dominance genes [2]. This phenomenon is Revised: 9 October 2023 particularly evident in traits related to fitness, such as pig litter size [3,4]. Traditionally, Accepted: 12 October 2023 inbreeding has been quantified using genealogical information [5]. However, the advent of Published: 15 October 2023 high-throughput genotyping technologies has introduced a valuable tool for unraveling the genetic basis of inbreeding depression. Several studies [6–8] have indicated that its genetic determination is distributed unevenly across the genome. One method widely used to detect identical-by-descent (IDB) genomic segments is to Copyright: © 2023 by the authors. conduct runs of homozygosity (ROH) [9]. ROH are completely homozygous segments of Licensee MDPI, Basel, Switzerland. This article is an open access article an individual’s genome. Howard et al. [10] proposed a strategy for identifying genomic distributed under the terms and regions associated with inbreeding depression by contrasting the phenotypic performance conditions of the Creative Commons of individuals carrying specific ROH with those lacking them, employing a mixed-model Attribution (CC BY) license (https:// analysis [11]. creativecommons.org/licenses/by/ In the context of Iberian pig populations, non-uniform effects of inbreeding depres- 4.0/). sion across the genome were observed in a closed experimental flock of the Guadyerbas Genes 2023, 14, 1941. https://doi.org/10.3390/genes14101941 https://www.mdpi.com/journal/genes Genes 2023, 14, 1941 2 of 11 variety [7]. However, due to the great genetic diversity among the strains of the Iberian pig [12,13], variations in the genetic determinants of inbreeding depression may exist. Hence, the objective of this study is to investigate the genomic architecture of inbreed- ing depression effects in two commercial varieties of Iberian pigs (Entrepelado and Ret- into). We also aim to pinpoint potential candidate genes located within the most relevant genomic regions. 2. Materials and Methods The dataset utilized comprises 2069 records (pertaining to 338 sows) for the Entre- pleado variety and 2028 records (related to 327 sows) for the Retinto for both TNB (Total Number Born) and NBA (Number Born Alive). In conjunction with this, a pedigree that contains genetically interconnected individuals was incorporated, with a total of 581 indi- viduals for Entrepelado and 541 individuals for Retinto. The mean phenotypic performance values for TNB and NBA are summarized in Table 1. Table 1. Mean and standard deviation (between brackets) of TNB (Total Number Born) and NBA (Number Born Alive) in the Entrepelado and Retinto varieties. Population TNB NBA Entrepelado 7.96 (1.93) 7.70 (1.88) Retinto 8.27 (2.18) 7.99 (2.17) Each sow was genotyped using the Geneseek GGP PorcineHD 70 K (Illumina Inc., San Diego, CA, USA) chip. Subsequent to genotyping, the genotypic data underwent filtration using PLINK 1.9 [14]. Filters were applied to ensure individual and SNP call rates exceeding 95%, with inclusion restricted to autosomal chromosomes. This process resulted in a collective sum of 57,450 SNP markers. Instances of missing genotypic data were rectified utilizing the FImpute 3. 0. software [15]. The allocation of SNP markers across the autosomal chromosomes in the Sscrofa 11.1 assembly is detailed in Table 2. Table 2. Chromosome (SSC), number of SNP markers (Nm) and base pairs covered (bp). SSC Nm bp 1 5442 274,315,671 2 3713 151,610,480 3 3308 132,657,669 4 3557 130,773,976 5 2716 104,477,606 6 4368 170,802,600 7 3563 121,758,423 8 3324 138,930,735 9 3513 139,386,589 10 2477 69,319,537 11 2178 79,072,521 12 2295 60,834,034 13 4146 208,240,759 14 3812 141,719,266 15 3281 140,404,164 16 2205 79,282,526 17 1968 63,391,207 18 1607 55,752,892 Firstly, we formulated a mixed linear model to assess the variance components and calculate the inbreeding depression through the gradient of a covariate associated with the percentage of individual heterozygosity, measured as the number of heterozygous SNPs per individual × 100 divided by the total number of SNPs. Genes 2023, 14, 1941 3 of 11 The model we postulated for both varieties is as follows: y = fd + Xb + Th + Zu + Wp + e (1) where y represents the vector comprising phenotypic records (specifically TNB and NBA), f is a vector encompassing individual heterozygosity, and b is the vector of systematic effects, which incorporates order of parity at 5 levels (1st, 2nd, 3rd, 4th and beyond). Additionally, h is a vector of random herd–year–season, with 96 levels for Entrepelado and 113 for Retinto, u denotes the vector of additive genetic random effects, p is the permanent environmental sow effect and e stands for the vector of residuals. Moreover, d serves as a covariate of the relationship between individual heterozygosity and phenotypic performance. The matrices X, T, Z and W are the corresponding incidence matrices. The genomic relationships (G) among the additive genetic effects (u) were calculated using the single-step approach [16,17]. For the estimation of variance components, the average information residual maximum likelihood [18] was adopted, utilizing the blupf90+ software (http://nce.ads.uga.edu/wiki/doku.php?id=application_programs, accessed on 8 October 2023) [19]. Secondly, the Unfavorable Haplotype Finder software (https://github.com/jeremyhoward/ Unfavorable-Haplotype-Finder, accessed on 8 October 2023) [10] was employed with the aim of selecting ROH. In this study, we defined ROH as a continuous sequence of homozygous genotypes spanning over 15 SNP markers. Additionally, we introduced a secondary crite- rion requiring that these ROHs be present in a minimum of 5% and a maximum of 95% of individuals within the population. However, we did not impose any constraints on the phenotypic traits of individuals, whether they carried the ROHs or not, as our primary objective was to identify all ROHs present in the populations. The algorithm’s details are expounded in [10]. Concluding this step, the blupf90+ software [19] was utilized to quantify the pheno- typic impact associated with the presence or absence of each identified ROH. We solved a mixed model for each identified ROH. These models incorporated the previously estimated variance components and included systematic, permanent environmental, and additive genetic effects, along with an additional systematic effect related to the presence or absence of the ROH. The significance for this systematic effect was assessed using a one-sided t-test. 3. Results and Discussion 3.1. Variance Component Estimation The results of the variance component estimation are presented in Table 3. Table 3. Restricted maximum likelihood estimates (and sampling variance) of the additive (σa2 ), permanent environmental (σp2 ), herd–year–season (σh2 ) and residual (σe2 ) variance. Entrepelado Retinto TNB NBA TNB NBA σa2 0.145 (0.085) 0.098 (0.071) 0.165 (0.092) 0.225 (0.110) σp2 0.366 (0.097) 0.341 (0.089) 0.283 (0.11) 0.291 (0.116) σh2 0.170 (0.545) 0.129 (0.071) 0.317 (0.085) 0.172 (0.061) σe2 2.901 (0.101) 2.853 (0.099) 3.908 (0.138) 3.796 (0.134) The estimates of the (co)variance components were similar to the ones provided by Srihi et al. [20], and they imply heritability estimates within the lower range of other estimates for white [21–23] and Iberian [24–26] pigs. It must be noted, however, that their impact on the ROH effect estimates are expected to be very low. Given the estimates of the variance components, the estimates of the covariate with the percentage of heterozygosity were 0.055 ± 0.026 (p = 0.017) and 0.057 ± 0.028 (p = 0.021) for NBA and TNB in the case of Entrepelado and 0.077 ± 0.051 (p = 0.065) and 0.067 ± 0.050 estimates estimatesforforwhite white[21,22,23] [21,22,23]andandIberian Iberian[24,25,26] [24,25,26]pigs. pigs.ItItmust mustbe benoted, noted,however, however,thatthat their impact on the ROH effect estimates are expected to their impact on the ROH effect estimates are expected to be very low. be very low. Given Giventhe theestimates estimatesofofthe thevariance variancecomponents, components,the theestimates estimatesofofthe thecovariate covariatewith with the percentage of heterozygosity were 0.055 ± 0.026 (p = 0.017) and 0.057 ± 0.028 the percentage of heterozygosity were 0.055 ± 0.026 (p = 0.017) and 0.057 ± 0.028 (p = 0.021) (p = 0.021) for Genes 2023, 14,NBA for NBAand 1941 andTNB TNBininthethecase caseofofEntrepelado Entrepeladoand and0.077 0.077±±0.051 0.051(p(p==0.065) 0.065)and and0.067 0.067±±0.050 0.050 4 of 11 (p(p==0.090) 0.090)for forNBA NBAandandTNBTNBininthethecase caseofofRetinto. Retinto.In Inall alltraits traitsand andpopulations, populations,there therewas was anan increase in the litter size as the percentage of heterozygosity increased, leadingtoto increase in the litter size as the percentage of heterozygosity increased, leading significant significantresults (p = resultsfor forthe0.090) the for NBA and Entrepelado Entrepelado TNB in the case of Retinto. In all traits and populations, there population. population. was an increase in the litter size as the percentage of heterozygosity increased, leading to significant results for the Entrepelado population. 3.2. 3.2.Runs RunsofofHomozygosity Homozygosity(ROH) (ROH)Identification Identification We Weidentified identified43,188 3.2. Runsand 43,188 35,175 35,175runs of Homozygosity and runs ofofhomozygosity (ROH) Identification homozygosity (ROHs) (ROHs)consisting consistingofofmore more than 15 SNPs within theWeEntrepelado identified and 43,188 andthe Retinto 35,175 runs varieties, of homozygosity than 15 SNPs within the Entrepelado and the Retinto varieties, respectively. respectively. (ROHs) The TheROH consisting of more than ROH 15 length SNPs within with with the the minimum minimum hadthe length had Entrepelado 145,783 145,783 baseand base the Retinto pairs, pairs, and varieties, and the the larger respectively. larger ROH The ROH with the ROH comprised comprised 15,162,018 minimum length had 145,783 base pairs, and the larger ROH comprised 15,162,018 base 15,162,018 base base pairs. pairs. Figure Figure 11 andand Figure Figure 22 illustrate illustrate thethe distribution distribution ofof ROH ROH sizes sizes pairs. Figures 1 and 2 illustrate the distribution of ROH sizes according to the SNP number according accordingtotothe theSNP SNPnumber numberand andbase basepairs, pairs,respectively. respectively. and base pairs, respectively. Figure Figure1.1.Distribution ofofthe Figure Distribution 1. ROH the ROHsizes sizes(according Distribution of the ROH to (according toSNP sizes number) (according SNP ininthe to SNP number) Entrepelado number) the (EE) (EE)and in the Entrepelado Entrepelado and(EE) and Retinto (RR) populations. Retinto Retinto (RR) populations. (RR) populations. Figure Figure2.2.Distribution ofofthe DistributionFigure ROH the2. sizes sizes(according Distribution ROH of the ROHtoto (according base sizes pairs) pairs)inin (according base tothe Entrepelado base the Entrepelado (EE) (EE)and andRetinto pairs) in the Entrepelado (EE) and Retinto Retinto (RR) populations. (RR) populations. (RR)populations. These distributions highlight the prevalence of short ROHs in the Entrepelado pop- These These distributions distributions highlight ulation, highlight the the in contrast to prevalence theprevalence of short short ROHs right-skewedofdistribution ROHs inin the observed the Entrepelado Entrepelado in the Retinto population. population, population, inin contrast contrast toto the right-skewed the may This observation right-skewed suggest moredistribution distribution observed observed recent inbreeding ininRetinto within the the the Retinto Retinto population. The population. This observation may suggest more recent inbreeding within the Retinto population. The average size of the ROHs in each population was 25.79 SNPs (±18.00)5 offor Genes 2023, 14, 1941 11 Entrepelado and 35.96 SNPs (±24.30) for Retinto. Furthermore, the average percentage of an individual’s genome covered by ROHs, considering overlapping regions between ROHs, was 26.87% (±3.78%) average size offor theEntrepelado and 40.74% ROHs in each population was (±3.20%) 25.79 SNPs for Retinto. (±18.00) for Entrepelado and 35.96 SNPs (±24.30) for Retinto. Furthermore, the average percentage of an individual’s 3.3. ROH Segmentsgenome covered by ROHs, considering overlapping regions between ROHs, was 26.87% and Inbreeding Depression (±3.78%) for Entrepelado and 40.74% (±3.20%) for Retinto. Among all the detected ROHs, we were able to identify 20,143 (Entrepelado) and 3.3. ROH Segments and Inbreeding Depression 26,771 (Retinto) ROHs shared by at least 5% and at most 95% of individuals, composed of Among all the detected ROHs, we were able to identify 20,143 (Entrepelado) and more than 15 SNPs, in which we expected to find that most of the variance was due to the 26,771 (Retinto) ROHs shared by at least 5% and at most 95% of individuals, composed of ROH effect. Therefore, more than we solved 15 SNPs, 20,143 in which and to26,771 we expected find thatmixed-model equations most of the variance was due to for the Entrepelado and Retinto, respectively. ROH effect. The objective Therefore, we solved was to 20,143 and 26,771 obtain estimates mixed-model equations of for the effects Entrepelado and Retinto, respectively. The objective was to obtain estimates of the effects related to related to the presence or absence of each specific ROH. The distributions of these effect the presence or absence of each specific ROH. The distributions of these effect estimates, estimates, pertaining to TNB pertaining andand to TNB NBA, NBA,are are illustrated illustrated in in Figure Figure 3 for 3both forpopulations. both populations. Figure Figure 3. Distribution 3. Distribution of the estimatesofoftheeffects estimates of effects associated associated with thewith the presence presence or absence or absence ofofROH ROHfor for TNB (Total Number Born) and NBA (Number Born Alive) in Entrepelado TNB (Total Number Born) and NBA (Number Born Alive) in Entrepelado and Retinto. Red lines and Retinto. Red lines represent the average effect of the ROH on TNB and NBA. represent the average effect of the ROH on TNB and NBA. The average estimate of the effects was consistently close to zero across all scenarios, The average estimate of the indicating that effects most of the was ROHsconsistently close with were not associated to zero acrossdepression. inbreeding all scenarios, The genomic regions associated with inbreeding depression (p < 0.05) indicating that most of the ROHs were not associated with inbreeding depression. The encompassed 1123 and 1533 runs of homozygosity (ROH) for NBA in Entrepelado and Retinto, respectively, while genomic regions associated for TNB, theywith inbreeding numbered 1197 and depression 1453 regions.(pThese < 0.05) encompassed findings 1123 and represent a proportion 1533 runs of homozygosity (ROH) of significant ROHs thatfor NBA ranged in5.4% from Entrepelado (for RR and and TNB) Retinto, respectively, to 5.9% (for EE and TNB), slightlynumbered while for TNB, they higher than what 1197would andbe1453 expected at random. regions. These findings represent a These significant ROHs exhibit a heterogeneous distribution across all chromosomes proportion of significant ROHs that ranged from 5.4% (for RR and TNB) to 5.9% (for EE for both populations, as depicted in Figures 4 and 5. Among these ROHs, 14 and 3 ROHs and TNB), slightlyboast higher than what a particularly would striking be expected significance, at random. with p-values below 0.001. The ROHs associated These significant ROHs exhibit with p-values lower thana heterogeneous 0.001 are presenteddistribution across in Tables 4 and 5 for all the chromosomes Entrepelado and Retinto populations, respectively. for both populations, as depicted in Figure 4 and Figure 5. Among these ROHs, 14 and 3 ROHs boast a particularly striking significance, with p-values below 0.001. The ROHs associated with p-values lower than 0.001 are presented in Table 4 and Table 5 for the Entrepelado and Retinto populations, respectively. Genes Genes2023, 2023,14, 14,x1941 FOR PEER REVIEW 6 of 6 of1111 Genes 2023, 14, x FOR PEER REVIEW 6 of 11 Figure Figure4. Distribution Distributionof ROHs ROHswith withp-values p-valueslower lowerthan than0.05, 0.05,0.01 0.01and and0.001 0.001in Entrepelado Entrepelado(EE) Figure 4.4. Distribution ofofROHs with p-values lower than 0.05, 0.01 and 0.001 ininEntrepelado (EE) (EE) population for (A) Total Number Born (TNB) and (B) Number Born Alive (NBA). population for (A) Total Number Born (TNB) and (B) Number Born Alive population for (A) Total Number Born (TNB) and (B) Number Born Alive (NBA). (NBA). Figure 5. Distribution of ROHs with p-values lower than 0.05, 0.01 and 0.001 in Retinto (RR) Figure Figure 5. Distribution of 5. Distribution ofROHs ROHswith with p-values lower than 0.05,0.05, 0.01 0.001 and 0.001 in Retinto (RR) population for (A) Total Number Bornp-values lower (TNB) and (B)than Number 0.01 Bornand in Retinto Alive (NBA). (RR) popula- population for (A) Total Number Born (TNB) and (B) Number Born Alive (NBA). tion for (A) Total Number Born (TNB) and (B) Number Born Alive (NBA). Table 4. Chromosome (SSC), base pair position (bp), and effect on number of piglets for Number Table 4. Chromosome (SSC), base pair position (bp), and effect on number of piglets for Number Born TableAlive (piglet NBA),(SSC), 4. Chromosome effect on number of piglets (bp),for Total Number Born of (piglet TNB), p-value Born Alive (piglet NBA), effectbase pair position on number of piglets and for effect Total on number Number piglets Born (piglet for Number TNB), p-value for Number Born Born Alive (pigletAlive (p-value NBA), NBA), effect NBA), p-value on number for of for Total piglets Number Born (p-value TNB) and candidate p-value for Number Born Alive (p-value p-value Totalfor Total Number Number Born (piglet Born (p-value TNB) andTNB), candidate genes within the genomic regions for Entrepelado population. for Number Born Alive (p-value NBA), p-value for Total genes within the genomic regions for Entrepelado population. Number Born (p-value TNB) and candidate SSC bp(c) genes within Pigletsthe genomic Piglets (NBA) regions for Entrepelado (TNB) p-Valuepopulation. (NBA) p-Value (TNB) Genes SSC bp(c) Piglets (NBA) Piglets (TNB) p-Value (NBA) p-Value (TNB) Genes 126,506,354–126,841,331 −0.5428 −0.6264 2.23 × 10−3 8.83 × 10−4 SSC 126,506,354–126,841,331 bp(c) −0.5428 Piglets (NBA) −0.6264 Piglets (TNB) 2.23 (NBA) p-Value × 10−3 −3 8.83(TNB) p-Value × 10−4 Genes 126,516,022–126,857,203 −0.5358 −0.6239 1.28 × 10 −3 4.08 × 10−4 126,516,022–126,857,203 126,506,354–126,841,331 −0.5358 −0.5428 −0.6239 −0.6264 1.28 2.23 × 10 × ×10 −3 4.08 × 10××−10 8.839.82 10 4 −4 126,562,142–126,971,814 −0.4838 −0.5840 3.46 10 − 3 −3 − −4 4 −4 126,562,142–126,971,814 126,516,022–126,857,203 −0.4838 −0.5358 −0.5840 −0.6239 3.46 1.28 × 10−3 × 10 −3 4.089.82 × 10× 10 SSC2 126,700,457–127,000,238 −0.4935 −0.5904 2.69 × 10−3−3 7.87 ×−10 −4 4 −4 CEP120 126,562,142–126,971,814 SSC2 126,700,457–127,000,238 −0.4838 −0.4935 −0.5840 −0.5904 3.46 × 10 2.69 × 10 × 10× 10 9.827.87 CEP120 SSC2 126,777,258–127,080,032 −0.4838 −0.5840 3.46 × ×1010 7.879.82 × 10××−10 −3 −3 4 −4 126,700,457–127,000,238 126,777,258–127,080,032 −0.4935 −0.4838 −0.5904 −0.5840 2.69 3.46 × 10 −3 9.82 10 −4 CEP120 127,096,117–127,764,704 126,777,258–127,080,032 −0.6088 −0.4838 −0.7179 −0.5840 2.75 3.46 −3−3 × ×1010 × 10×−10 9.829.18 4 −4 127,096,117–127,764,704 −0.6088 −0.7179 2.75 × 10 −3 −3−3 9.18 ×−10 −4 4 −4 127,096,117–127,764,704 127,492,532–127,890,446 −0.6088 −0.6305 −0.7179 −0.7393 2.75 × ×1010 1.70 × 10× 10 9.185.36 127,492,532–127,890,446 127,492,532–127,890,446 −0.6305 −0.6305 −0.7393 −0.7393 1.70 1.70 × 10 × ×10 −3−3 5.36 × 10××−10 5.369.12 10 4 −4 15,871,592–16,9148,74 −0.6141 −0.7758 5.00 10−3 −4 ATF1 15,871,592–16,9148,74 −0.6141 −0.7758 5.00 × 10 −3 9.12 × 10−4 ATF1 SSC5 AQP5 SSC5 16,134,195–16,852,941 −0.5377 −0.7575 1.10 × 10−2 9.73 × 10−4 AQP5 16,134,195–16,852,941 −0.5377 −0.7575 1.10 × 10−2 9.73 × 10−4 AQP6 AQP6 Genes 2023, 14, 1941 7 of 11 Table 4. Cont. SSC bp(c) Piglets (NBA) Piglets (TNB) p-Value (NBA) p-Value (TNB) Genes ATF1 15,871,592–16,914,874 −0.6141 −0.7758 5.00 × 10−3 9.12 × 10−4 SSC5 AQP5 16,134,195–16,852,941 −0.5377 −0.7575 1.10 × 10−2 9.73 × 10−4 AQP6 RACGAP1 20,074,761–20,586,603 −0.5995 −0.5869 7.23 × 10−5 2.03 × 10−4 GMNN 28,252,780–28,721,664 −0.6691 −0.6795 3.54 × 10−5 6.34 × 10−5 SSC7 BAG2 35,594,623–36,062,243 −0.6175 −0.6308 6.97 × 10−4 9.86 × 10−4 RAB23 20,074,761–20,586,603 −0.5995 −0.5869 7.23 × 10−5 2.03 × 10−4 80,594,858–81,214,645 −0.4839 −0.5679 2.28 × 10−3 7.63 × 10−4 SSC13 CLSTN2 80,682,896–81,329,857 −0.4929 −0.5693 1.72 × 10−3 6.53 × 10−4 Table 5. Chromosome (SSC), base pair position (bp), effect on number of piglets for Number Born Alive (piglet NBA), effect on number of piglets for Total Number Born (piglet TNB), p-value and FDR for Number Born Alive (p-value/FDR NBA), p-value and FDR for Total Number Born (p-value/FDR TNB), and candidate genes within the genomic regions for Retinto population. SSC bp(c) Piglets (NBA) Piglets (TNB) p-Value (NBA) p-Value (TNB) Genes 632,758–1,869,413 −0.9609 −0.7750 1.35 × 10−4 1.41 × 10−3 734,657–1,869,413 −0.9609 −0.6383 1.35 × 10−4 4.82 × 10−3 SSC1 989,159–1,358,337 −0.9609 −0.7750 1.35 × 10−4 1.41 × 10−3 1,189,180–1,471,069 −0.8987 −0.7750 2.31 × 10−4 1.41 × 10−3 1,189,180–2,160,998 −0.8174 −0.7244 5.59 × 10−4 2.06 × 10−3 88,968,396–91,465,748 −0.5929 −0.5525 7.53 × 10−4 1.26 × 10−3 90,580,151–91,903,672 −0.6761 −0.6322 1.69 × 10−4 3.08 × 10−4 91,082,368–92,692,939 −0.6931 −0.6421 1.60 × 10−4 3.29 × 10−4 FSHR 91,240,698–91,903,672 −0.6388 −0.6003 2.88 × 10−4 4.70 × 10−4 SSC3 LHCGR 91,265,546–92,395,629 −0.6761 −0.6322 1.69 × 10−4 3.08 × 10−4 GTF2A1L 91,381,281–92,692,939 −0.6608 −0.6010 2.64 × 10−4 6.37 × 10−4 91,381,281–92,437,314 −0.6458 −0.5931 2.75 × 10−4 5.88 × 10−4 91,381,281–91,966,239 −0.6102 −0.5632 4.53 × 10−4 8.66 × 10−4 37,024,885–37,966,306 −1.0159 −0.8876 6.77 × 10−5 3.58 × 10−4 GABRB1 SSC8 37,513,284–38,036,453 −0.8992 −0.7349 2.59 × 10−4 2.00 × 10−3 CORIN 196,187,718−196,460,966 −0.6757 −0.6677 9.06 × 10−4 8.51 × 10−4 USP16 SSC13 196,216,549–196,471,450 −0.7089 −0.6826 3.89 × 10−4 4.98 × 10−4 CFAP298 The regions identified in the NBA are also observed in the TNB results and are lo- cated on SSC7, having the lowest p-values. These regions span between 20,074,761 and 20,586,603 base pairs (bp), wherein proximity to QTLs associated with pig litter size [27,28] is noted. Within this region lies the GMNN (Geminin DNA Replication Inhibitor) gene, whose encoded protein plays an essential role in embryo development and implantation [29]. Additionally, in the genomic region spanning from 28,252,780 to 28,721,664 bp, we find the genes BAG2 (BAG Cochaperone 2) and RAB23 (RAB23, Member RAS Oncogene Family). The former is potentially linked to infertility, as the mediated inhibition of CHIP expression contributes to endometriosis [30], and the latter has been associated with litter size, as evi- denced by GWAS in Bama Xiang pigs [31], and with failure during reproduction in puberty in a F2 population crossbreed of Duroc and Erhualian pigs [32]. Lastly, no associations with litter size were detected in the genomic region spanning from 80,682,896 to 81,329,857 bp. The remaining regions with p-values lower than 0.001 in the TNB are distributed across SSC2 (7 ROHs), SSC5 (2 ROHs), and SSC13 (2 ROHs) in a contiguous manner. Within SSC2, spanning from 126,506,354 to 127,890,446 bp, we identified the CEP120 (Centrosomal Protein Genes 2023, 14, 1941 8 of 11 120) gene, which has been associated with maternally derived aneuploidy [33]. In the SSC5 region (15,871,592–16,914,874 bp), we find the ATF1 (Activating Transcription Factor 1) gene, known to be involved in the estrogenic signaling pathway [34]. This region also includes AQP5 and AQP6 (Aquaporin 5 and Aquaporin 6), which have been suggested as markers for male infertility in livestock [35]. AQP5 is overexpressed in granulosa cells and flattened follicle cells of the primordial follicles in the ovary and in the oviduct [36], while it is downregulated in pigs infected with Porcine Reproductive and Respiratory Syndrome [37]. We also identified RACGAP1 (Rac GTPase-Activating Protein 1), the inhibition of which is required in vitro for human embryonic trophoblast invasion into endometrial stromal cells [38]. Lastly, in the SSC13 region spanning from 80,594,858 to 81,329,857 bp, the only related gene found was CLSTN2 (Calsyntenin 2), which has been proposed as a potential candidate gene in Erhualian pigs [28,39] and in sheep after conducting a GWAS [40]. In the case of the RR population, we identified 17 ROHs with p-values lower than 0.001 in the NBA, 10 of which were shared with the TNB, as detailed in Table 4. The region with the lowest p-value is situated in SSC8, spanning from 37,024,885 to 37,966,306 base pairs (bp), with p-values of 6.77 × 10−5 in the case of NBA and 3.58 × 10−4 in the case of TNB. Additionally, within SSC8, there is another ROH ranging from 37,513,284 to 38,036,453 bp with a low p-value exclusive to NBA. Within this SSC8 region, several noteworthy genes are located, including GABRB1 (γ-Aminobutyric Acid Type A Receptor Subunit Beta1), which plays a role in inhibiting GnRH neurons. This inhibition is essential for the production of the GnRH hormone, which, in turn, is crucial for the synthesis of LH (luteinizing hormone) and FSH (follicle-stimulating hormone), both of which are crucial for reproduction [41–43]. CORIN (Corin, Serine Peptidase) is up-regulated in the decidua of the pregnant uterus, which suggests a potential role during pregnancy [44], and it has been proposed as a candidate gene for calving easiness in dairy and beef cattle [45]. This SSC8 region is also associated with QTLs linked to reproduction traits, such as litter size in the Chinese Erhualian pig breed [28] and the number of stillborn piglets in Shaziling pigs [46]. Furthermore, regions on SSC3 and SSC13 were shared by the NBA and TNB and contained genes like FSHR (follicle-stimulating hormone receptor) and LHCGR (luteinizing hormone receptor), both critical in regulating female reproductive processes. Additionally, GTF2A1L (General Transcription Factor IIA Subunit 1 Like) may play an important role in testis biology and male infertility [47]. On SSC13, in positions 196, 187, 718–196, 471 and 450, near a QTL for litter size [28], lies the USP16 (Ubiquitin-Specific Peptidase 16) gene, responsible for regulating embryonic stem cell gene expression [48]. In this region, CFAP298 (Cilia- and Flagella-Associated Protein 298) has been described, with a mutation known to cause infertility in human patients [49]. Lastly, there is a region with p-values lower than 0.001 in the NBA at SSC1 spanning from 632,758 to 2,160,998 bp, although no specific relationships with reproductive traits were identified. 4. Conclusions The results of this study indicate the presence of inbreeding depression in the litter size traits of two strains of Iberian pigs. Furthermore, the distribution of the inbreeding depression effects is heterogeneous along the genome, and the architecture of inbreeding depression differs between populations. Additionally, we were able to identify eight genomic regions significantly associated with inbreeding depression that contain several relevant genes, such as FSHR, LHCGR, CORIN, AQP6 and CEP120. Author Contributions: Conceptualization, L.V.; methodology, C.H.-R., H.S., D.L.-C. and L.V.; soft- ware, C.H.-R. and L.V.; validation, C.H.-R., H.S. and D.L.-C.; formal analysis, C.H.-R., H.S. and D.L.-C.; investigation, C.H.-R., H.S. and D.L.-C.; resources, J.C. and N.I.-E.; data curation, S.N. and N.I.-E.; writing—original draft preparation, C.H.-R. and L.V.; writing—review and editing, N.I.-E. and J.C.; visualization, C.H.-R., H.S. and D.L.-C.; supervision, L.V.; project administration, J.C. and N.I.-E.; funding acquisition, J.C. and N.I.-E. All authors have read and agreed to the published version of the manuscript. Genes 2023, 14, 1941 9 of 11 Funding: This research was partially funded by grants CGL2016-80155-R, PID2020-114705RB-I00 (Ministerio de Ciencia e Innovación), and IDI-20170304 (CDTI). Srihi received funding from the European Union’s H2020 research and innovation program under a Marie Sklodowska–Curie grant agreement, No. 801586. 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