Investigating the patterns of homozygosity, linkage disequilibrium, effective population size and inbreeding coefficients in livestock contributes to our understanding of the genetic diversity and evolutionary history. Here we used Illumina PorcineSNP50 Bead Chip to identify the runs of homozygosity (ROH) and estimate the linkage disequilibrium (LD) across the whole genome, and then predict the effective population size. In addition, we calculated the inbreeding coefficients based on ROH in 305 Piétrain pigs and compared its effect with the other two types of inbreeding coefficients obtained by different calculation methods. A total of 23,434 ROHs were detected, and the average length of ROH per individual was about 507.27 Mb. There was no regularity on how those runs of homozygosity distributed in genome. The comparisons of different categories suggested that the formation of long ROH was probably related with recent inbreeding events. Although the density of genes located in ROH core regions is lower than that in the other genomic regions, most of them are related with Piétrain commercial traits like meat qualities. Overall, the results provide insight into the way in which ROH is produced and the identified ROH core regions can be used to map the genes associated with commercial traits in domestic animals. The Piétrain pig originated in Belgium. It was founded by crossing local swine with Berkshire, English Large White and Bayeux pig and then was exported to other countries after 1950s. Piétrain pig has medium size, well-developed muscles and white skin with black spots. It is unavoidable to create close inbreeding during breed formation . CurrentNe) can also reflect the patterns of runs of homozygosity indirectly. As an important parameter of population genetics, it provides an important reference for genome architecture research.Runs of homozygosity (ROH) are consecutive homozygous segments. The autozygous regions exist due to parents descending the same haplotypes to their progeny . In the ROHF) is considered to be the most accurate calculation method for predicting the inbreeding level of each individual [At present, compared with other existing methods, the whole genome inbreeding coefficient based on ROH  GeneChip data. We further investigated the biologic significance of high frequency ROH islands. As expected, we find that ROH islands are associated with some important economic traits, such as meat quality. In addition, the whole genome inbreeding coefficient based on ROH is assessed for prediction ability through comparison with traditional pedigree inbreeding coefficient and other genomic inbreeding coefficients. Overall, the results from this study can provide insights into the pattern of runs of homozygosity and direct its application in animal breeding.Sus scrofa (Pig) 11.1 reference genome version. The next step was to quality-control using PLINK software and R programming. The filtering parameters were as follows: (ⅰ) SNPs with a call rate less than 95% were excluded, (ⅱ) individuals with a call rate less than 95% were discarded, (ⅲ) SNPs with minor allele frequency less than 5% were removed and (ⅳ) the deviated markers (p < 0.1 × 10−3) were excluded based on Hardy–Weinberg test. Finally, we performed a relationship test and removed high-related individuals.376 Piétrain pigs that belonged to the same generation without generation overlapping were used. All research involving animals was conducted under protocols (No. 5 proclaim of the Standing Committee of Hubei People’s Congress) approved by the Standing Committee of Hubei People’s Congress and the ethics committee of Huazhong Agricultural University in China. Pigs were genotyped for 50,264 SNPs spread over all chromosomes using Illumina PorcineSNP50 Bead Chip. Only SNPs on autosomes were considered in the following ROH analyses. All analyses were based on Ne) was defined as the number of individuals that harbor the same distribution of allele frequencies under random genetic drift in an idealized population [Ne can be calculated through the linkage disequilibrium (LD) scores. With the availability of high-density chip in pigs, it was feasible to calculate the LD scores between pairwise markers. In this analysis, the LD scores (2r) between pairwise SNPs was calculated using PLINK v1.07. The effective population sizes (Ne) were calculated according to the formula [c represents the linkage map distance in Morgans [https://www.ncbi.nlm.nih.gov/assembly/GCF_000003025.6) and each chromosome’s genetic length from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/genome/gdv/?org=sus-scrofa). In addition, 2)E reflects the effective population size at t generations ago. A series of distance bin between SNPs were given as follows: 0.1, 0.2, 0.5, 1, 2, 5 cM, allowing 5% floating range of SNPs distance, to compute Ne(t) before 500, 250, 100, 50, 25, 10 generations. The whole process was performed for SNPs on autosomes [The effective population size , ROH-based inbreeding coefficients (ROHF), and inbreeding coefficients based on the excess of homozygosity (HOMF).To assess the accuracy of prediction inbreeding effects using ROH, three types of inbreeding coefficients were calculated in this study, including Pedigree-based inbreeding coefficients (PEDF) were calculated for Piétrain pigs, which were born between 2014 and 2017. The pedigree data were recorded by KFNetsKing software. The total record contains 3834 individuals from 126 litters, including 305 experimental samples. They were descended from 34 parent stock (PS) sires and 126 PS sows. Furthermore, there were a total of 29 grandparent (GP) sires, 93 GP sows, 22 great-grandparent (GGP) sires, 59 GGP sows, 10 great-great-grandparent (GGGP) sires and 23 GGGP sows in record. Among 126 litters records, 118 litters have half sibling litter records in this population (PEDF with pedigree depth ranging from 3 to 4 generations. Pedigree-based inbreeding coefficients were estimated by the pedigreemm package in R (https://cran.r-project.org/web/packages/pedigreemm/index.html). Genomic inbreeding coefficients (HOMF) were calculated for 305 Piétrain pigs with genotype information using the following formula:Pedigree-based inbreeding coefficients .Genomic inbreeding coefficient based on ROHs was provided by McQuillan et al. (2008) , definedSpearman’s correlation coefficients were calculated between all above inbreeding coefficients through pairwise comparison using SAS.http://ensemble.org). The core regions of ROH were also matched with previously reported pig Quantitative Trait Locus (QTL) obtained from pig Quantitative Trait Locus Database (Pig QTLdb) . In addition, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotations were performed using the list of genes detected in core regions of ROH in The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 tool.The core regions of ROH were annotated to identify the known genes in those regions using the genomic database in Ensembl (PI_HAT ≥ 0.5). Finally, a total of 305 individuals with 35,549 SNPs were available for further analysis.To investigate the pattern of homozygosity, 376 Piétrain pigs were used in chip analysis. After quality control, 4 samples were removed for low genotyping, 1788 SNPs were filtered in missingness test, 2179 markers were excluded based on HWE test and 11,616 SNPs failed in the minor allele frequency test. In addition, we calculated relationship coefficient using identity-by-descent implemented in PLINK v1.07 and 71 samples were excluded  were estimated using 921 individuals with pedigree depth ranging from 3 to 4 generations. In addition, the genomic inbreeding coefficients (HOMF and ROHF) were calculated using 305 individuals with genotype data. The mean value of ROHF based on the total length of ROH was 0.205. More specifically, the mean of inbreeding coefficient based on short ROH (1 to <5 MB) was 0.049, the mean based on the intermediate ROH (5 to <10 MB) was 0.058 and long ROH (≥10 MB) 0.147.Pedigree-based inbreeding coefficients  as shown in HOMF nor ROHF was highly correlated with PEDF: r = 0.161  between PEDF and HOMF, r = 0.197  between PEDF and ROHF, respectively . By contrast, neither short ROH nor intermediate ROH has high correlation with HOMF , as shown in To further investigate the inbreeding coefficients obtained by different calculation methods, we performed pairwise comparisons between ectively . These rSLC35F1, B3GAT2, DCBLD1, LMBRD1, OMA1, MRAP2 and CLGN  estimated in Piétrain pigs [2r, the mean LD extent can be extended to a distance of 5 Mb, suggesting that LD can help form patterns of the short ROH. As for intermediate and long ROHs that exceed the range of LD extent, they should were broken in the process of recombination, but did not, suggesting that they are supposed to be shaped by other factors [Runs of homozygosity are contiguous homozygous genotype fragments in the genome that are present in an individual since the identical gametes are inherited from the parents. In previous studies, Bosse et al. studied genetic patterns of ROH from several breeds (n = 52) through 60K SNPchip and the smallest population (Hampshire) only contained 2 individuals . Purfielain pigs . In addit al. 201 estimateNe under the assumption that 1 cM approximate to 1 Mb in physical distance following the previous study [Ne and affect the estimation accuracy, because different chromosomes have diverse linkage conditions due to their distinct physical and genetic distance. Corbin et al. proposed that the assumption that 1 cM approximate to 1 Mb in physical distance would result in lack of consistency for Ne estimated [Ne would be very sensitive and the result would be unreliable when generations <7. Hence, our estimation of Ne process terminated at 10 generations. We observed that Ne decreased gradually over generations, implying the decline of genetic variations in the long-term evolutionary process. Combining with the prevalence of ROH and numerous short ROH in this analysis, it suggested that the decrease of effective population size, as a signal of decline in genetic diversity, can also reflect high levels of homozygosity indirectly.In population genetics, the decline rate of linkage disequilibrium and the linkage map distance can be used to infer population history and recent effective population size . First, us study , and thestimated . TherefoF, was estimated through pedigree record, following a principle that an individual transmits an allele to an offspring with 50% probability all the time. However, in the transport process, meiosis was full of randomness with a large stochastic variance. Furthermore, in realistic breeding, it’s common that pedigree record was incomplete or missing. Therefore, it was difficult to trace back the relationship between ancestors and distant parents, which has also become a vital factor affecting the accuracy of inbreeding estimate. This inaccuracy led to the weak correlation between pedigree-based inbreeding coefficients and genomic inbreeding coefficients as we observed.It is universally considered that inbreeding was an important factor affecting the pattern of homozygosity. In conventional breeding without available marker data, the inbreeding coefficient, ROHF, another genomic inbreeding estimator HOMF takes all of the homozygote into account, including separate single locus and homozygous regions. In that case, it will confound all of the history events in experimental population. At the same time, it will increase errors and biases caused by homozygotes generated by random recombination. On the contrary, ROHF could exclude accidental errors though removing single homozygous and quite short segments. In addition, it has been used in animal genetic analysis [ROHF to improve mating systems in Italian sheep breeds [In recent years, the inbreeding coefficient based on ROH has received more attention, and it was considered to be an accurate estimator to control the probability of inbreeding ,42. Companalysis . For exaanalysis . Bosse eanalysis . Mastranp breeds . Furtherp breeds ,43,44. HQTL:22,293 harbored in one core region of ROH was related with average backfat thickness. In addition, a great part of them was relevant to production traits, like QTL:27977, a location associated with feed conversion ratio. In addition, there were several QTLs associated with reproduction traits, such as QTL:18325, a location expression in sperm motility. In addition, some of the core ROH regions also harbored QTLs related to exterior, like QTL:32743, associated with breed’s coat color. Among them, we noticed several functional related genes in basic biologic processes, such as SLC35F1, a gene played a key role in controlling heat rate; B3GAT2, was involved in cellular migration and adhesion in the nervous system; DCBLD1, LMBRD1 and OMA1, all participated in compound integral component of membrane. In addition, we also noticed some genes which are related with Piétrain economical traits: MRAP2 can regulate the energy homeostasis machinery; CLGN, an important modulator in spermatogenesis and infertility. As terminal sires in pig industry, Piétrain pig was subjected to high-intensity artificial selection for a long time. Moreover, these observations are consistent with the expectation that homozygosity is associated with some biologic function and important economic traits affected by positive selection. After long-term positive selection, especially more recent artificial selection, regulatory regions that control the important economic traits would be highly homogenous because of directional selection. In other words, those specific regions are easier to be exposed in ROH core regions. Those observed phenomena indicate that positive selection is a significant force shaping patterns of homozygosity in the process of evolution, and the high-frequency ROH can contribute to exploit potential target genes of human-imposed selection.In this study, we identified 16 ROH core regions where homozygous rate are pretty high for a specific population, many previous studies revealed that those homozygous segments had a major impact on gene mapping and disease ,46,47. THOMF) based on homozygous genotypes and the inbreeding coefficients (ROHF) based on ROH exposes that the proportion of ROH has an instruction sense in assessing the population’s level of inbreeding. Thus, using genomic information to detect ROH is a powerful method for estimating population inbreeding, especially in assessing the level of inbreeding where pedigrees records were incomplete or missing. Furthermore, the categories of ROHs may be related with the generations on the time frame, short ROH more reflects ancient inbreeding, while long ROH involves in recent inbreeding. Overall, the investigation of ROH patterns provides a helpful reference for selection and assortative mating in modern inbreeding and points an original direction for research of population genetic structure.In summary, this study investigated the patterns of runs of homozygosity, LD, effective population size and inbreeding coefficients, which provided a clear understanding of how diversity evolved in Piétrain pigs. The decrease of effective population size can mirror artificial selection in breeding process and the recession of genetic diversity. This phenomenon also proves the formation of ROH. Moreover, several genes associated with production traits were found overlapping with ROH regions. The high relationship between inbreeding coefficients (


We conclude that BP1–BP2 deletion moderately increases the risk of the generally milder, but commoner, CVM phenotypes seen in this unselected population, in addition to its previously demonstrated association in case/control studies ascertained for CVM.Deletion of a non-imprinted 500kb genomic region at chromosome 15q11.2, between breakpoints 1 and 2 of the Prader–Willi/Angelman locus (BP1–BP2 deletion), has been associated in previous studies with phenotypes including congenital cardiovascular malformations (CVM). Previous studies investigating association between BP1–BP2 deletion and CVM have tended to recruit cases with rarer and more severe CVM phenotypes; the impact of CVM on relatively unselected population cohorts, anticipated to contain chiefly less severe but commoner CHD phenotypes, is relatively unexplored. More precisely defining the impact of BP1–BP2 deletion on CVM risk could be useful to guide genetic counselling, since the deletion is frequently identified in the neurodevelopmental clinic. Using the UK Biobank (UKB) cohort of ~500,000 individuals, we identified individuals with CVM and investigated the association with deletions at the BP1–BP2 locus. In addition, we assessed the association of BP1–BP2 deletions with neuropsychiatric diagnoses, cognitive function and academic achievement. Cases of CVM had an increased prevalence of the deletion compared with controls (0.64%; OR = 1.73 [95% CI 1.08–2.75];  NIPA1, NIPA2, CYFIP1 and TUBGCP5) within a 500-kb region  on the long arm of chromosome 15 and encompass 4 genes (p = 0.02) [Genomic deletions are known to be a significant cause of congenital malformations, among which congenital cardiovascular malformations (CVM) have the highest population prevalence of around 9 per 1000 live births –24. We p = 0.02) . Other s = 0.02) , 27 and  = 0.02) , 29. HowThe UK Biobank (UKB), comprising ~500,000 individuals with genotype data, hospital admission records, baseline cognitive function testing, and a variety of other collected information, provides a large-scale resource for investigating complex genotype–phenotype associations. Here, we determine the prevalence of BP1–BP2 deletions within the UKB cohort and explore its association with CVM; and with neurodevelopmental diagnoses and cognitive function.n = 488,366), were used to generate CNV calls using the PennCNV software [n = 301). Samples containing an excess of CNVs (>40) (n = 1401) were also removed at this stage. This threshold intentionally retained samples with a relatively high number of CNVs as global CNV burden has previously been found to contribute to the risk of sporadic CVM [B-allele frequency and log2 ratio transformed intensity value files, generated and made available by UKB using the Applied Biosystems UK BiLEVE Axiom and UKB Axiom arrays  between the outermost array probes covering this region .15q11.2 deletions were identified where a heterozygous deletion spanned at least 95% of the four genes involved  data available in the UKB contain information on hospital admissions for the cohort. It includes primary and secondary diagnoses in the form of ICD9 and ICD10 codes, as well as details of operations and procedures as recorded through OPCS-4 codes. Using the classification schema shown in Fig. In addition to codes covering ‘congenital malformation of the circulatory system’, we used additional codes to identify, amongst others, certain defects of congenital origin presenting in later life /Advanced Subsidiary levels (AS levels) or equivalent’ and each of these categories compared with the baseline group by fitting a multinomial log-linear model, accounting for age and sex. Fecundity was assessed in males and females separately using Poisson regression fitted to the number of children fathered and number of live births per person, respectively. Outliers were removed if greater than five times the standard deviation from the mean. This excluded samples with >8 children fathered in males and >7 live births in females. An interaction test was performed to confirm the differences between male and female subgroups.The frequency of BP1–BP2 deletion in cases of CVM, cases of neuropsychiatric disorder, and controls was compared using chi-squared tests. Associations between the deletion and quantitative measures of cognitive function were assessed using different statistical models dependent upon the distribution of the data, and adjusting for age and sex. Linear regression was used for reaction time . Poisson regression was fitted for fluid intelligence score. For the pairs matching test where values are heavily skewed towards zero, values were converted to a binary variable (0(0), 1(>0)) and logistic regression fitted on the binary outcome. Assessment of the numeric memory test was again assessed with logistic regression by converting the values into a binary variable (0(≤6), 1(>6)). The association between the deletion and the highest qualification achieved was assessed by combining ‘O levels/General Certificate of Secondary Education or equivalent’, ‘Certificate of Secondary Education or equivalent’, ‘National Vocational Qualification or Higher National Diploma or Higher National Certificate or equivalent’, ‘Other professional qualifications’, ‘None of the above’ and ‘Prefer not to answer’ into a single category and treating this as a baseline group. Further details on UK specific qualifications are available at In total, 2792 participants with genetic data available were identified as having non-syndromic CVM and 472,378 were classified as controls (see “Methods”). Table p = 0.03). In keeping with previous observations, no participant was homozygous for the BP1–BP2 deletion.In total, 1832 BP1–BP2 deletions were identified in UKB participants with genotyping data available. The CVM and control groups accounted for a total of 1787 of the samples with BP1–BP2 deletion, with 45 deletions present in participants who were excluded according to the criteria outlined in “Methods”. Of the 2792 non-syndromic CVM samples, 18 carried the BP1–BP2 deletion resulting in an estimated prevalence of 0.64% Table . The speTwo of the eighteen CVM case samples carrying the BP1–BP2 deletion also had neuropsychiatric diagnoses. Those individuals had cardiovascular diagnoses of ASD and aortic valve disease/replacement; and neuropsychiatric diagnoses of bipolar disorder and scholastic developmental disorder, respectively.p = 0.004)  Table . The speAfter removing participants with relevant neuropsychiatric disorders, 483,330 samples remained. Of these, 1808 carried the BP1–BP2 deletion. Figure p = 5.60E−22). Similarly, a lower proportion of participants with BP1–BP2 deletion had attained A/AS levels as their highest educational qualification than participants without the deletion .Carriers of the BP1–BP2 deletion were compared with non-carriers in terms of the highest academic qualifications that they had achieved. Table p = 0.00175). This difference was not evident in females . A test for interaction confirmed the differences between the male and female subgroups (p-interaction = 5.46E−04).Fecundity was assessed in those individuals with and without the BP1–BP2 deletion. Self-reported number of children fathered  and number of live births  in each cohort are shown in Fig. p = 0.03) and, in broad agreement with recent findings from others [p = 0.0043), measures of cognitive function, academic achievement and fecundity.The 15q11.2 (BP1–BP2) deletion (sometimes referred to as the Burnside-Butler syndrome susceptibility locus) has previously been associated with phenotypes including developmental delay, autism, schizophrenia and CVM; the great majority of the evidence regarding the deletion thus far originates from cohorts specifically selected for one or other of those phenotypes. We estimated the prevalence of the deletion and its associations with CVM and other diagnoses in the UKB cohort. We confirmed the association of the deletion with CVM   Fig. , supportTUBGCP5, CYFIP1, NIPA1 and NIPA2, are located in the deleted region. The effects of hemizygosity at each individual gene in the region have not been systematically described in mouse models, nor has any mouse model of the entire deletion been reported as yet. Therefore, the mechanisms whereby the deletion causes its associated phenotypes, and the interaction between hemizygosity for the region and other genetic and environmental factors to result in the wide variety of phenotypes described, remain to be elucidated.The clinical and genetic aspects of BP1–BP2 deletion have been recently reviewed [Regarding the classification of the deletion for the purposes of clinical genetics and genetic counselling, we tend to agree with Jønch et al. that the deletion should be classified as “pathogenic, of mild effect size” . The effSupplementary Table LegendsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5