We demonstrate its applicability for pig commercial 50 K SNP arrays, by accurately imputing 2.6 billion genotypes at a concordance rate value of 0.971 in 81 Large White pigs (~ 17 × sequencing coverage). We also applied our reference panel to impute the low-density SNP chip into the high-density data for three GWASs and found novel significantly associated SNPs that might be casual variants.Pigs not only function as a major meat source worldwide but also are commonly used as an animal model for studying human complex traits. A large haplotype reference panel has been used to facilitate efficient phasing and imputation of relatively sparse genome-wide microarray chips and low-coverage sequencing data. Using the imputed genotypes in the downstream analysis, such as GWASs, TWASs, eQTL mapping and genomic prediction (GS), is beneficial for obtaining novel findings. However, currently, there is still a lack of publicly available and high-quality pig reference panels with large sample sizes and high diversity, which greatly limits the application of genotype imputation in pigs. In response, we built the pig Haplotype Reference Panel (PHARP) database. PHARP provides a reference panel of 2012 pig haplotypes at 34 million SNPs constructed using whole-genome sequence data from more than 49 studies of 71 pig breeds. It also provides Web-based analytical tools that allow researchers to carry out phasing and imputation consistently and efficiently. PHARP is freely accessible at  To date, considering the high genotyping cost of whole-genome sequencing for thousands of animals or more, the majority of GWASs still use low-density genotyping technologies (at tens of thousands of sites) such as SNP arrays or RRS. The GWASs based on the low-density SNP panels have been successful in terms of finding thousands of loci that have been statistically associated with risks for diseases and traits, and a large number of these loci are well replicated, indicating that they are true associations6. However, because there are often many co-inherited variants in strong linkage disequilibrium (LD) with the most significant trait-associated variant (lead-SNP), the association of a locus with a disease/trait does not specify which variant at that locus is actually causing the association . As a consequence, a higher-resolution view of a genetic region obtained by adding more variants might be needed to determine which of the linked variants are functional. Thus, a high-density (at least one million of sites) genotypes are essential for GWASs, TWASs or eQTL mapping to provide deeper insights into disease/trait biology.Over the last decade, because of the rapid development of high-throughput genotyping technologies, e.g., single nucleotide polymorphism (SNP) arrays7, IMPUTE28, Minimac49 and GLIMPSE10—have been developed to infer unobserved genotypes in one individual from the estimated haplotypes in a reference panel, which comprises a large number of markers. Genotype imputation could be beneficial11 for fine mapping by increasing the chances of identifying a causal variant, meta-analysis by facilitating the combination of results across studies using different genotyping arrays, and increasing the power of association studies by increasing the effective sample size. Therefore, it has been widely used in genetic research, especially in humans6, which usually involves genotyping SNPs in DNA genotyping microarrays (low-density) and then imputing genotypes at tens of millions of additional sites based on the availability of a large cohort of public haplotype reference panels (HRPs)., e.g., the 1000 Genomes Project12 and the Haplotype Reference Consortium (HRC)13.Genotype imputation is a more cost-efficient way to obtain a high-density genotype. Several imputation methods—e.g., BEAGLE14 constructed a reference panel including 403 individuals from 10 populations and applied it to impute 60 K SNPs  into whole-genome sequences of 418 Sutai individuals for a GWAS of lumbar number. They did not detect any significant signals using the original 60 K SNPs but rediscovered the missing QTL for lumbar number in Sutai pigs using imputed-genotypes. Later, Yan et al. also constructed a reference panel including 117 individuals and utilized it to impute 60 K SNPs onto the whole-genome sequences of 1020 individuals for a GWAS of haematological traits and found 87 novel quantitative trait loci (QTLs) for 18 haematological traits at three different physiological stages. Moreover, a previous study also suggested that using imputation-based whole-genome sequencing data can improve the accuracy of genomic prediction for combined populations of pigs15. However, the pig reference panels in previous studies are either publicly unavailable or have a small sample size, which is the major determinant of genotype imputation accuracy17. We only found one publicly available pig reference panel in Animal-ImputeDB18, which includes a very small sample size (n = 233). With the increasing amounts of publicly available pig genomic sequencing data (more than 25 terabytes), especially in the last 5 years, it is urgent to use these resources to construct a reference panel to facilitate a wide application of genotype imputation in pig genetic studies. Therefore, it is essential to develop a convenient database to provide a high-quality reference panel with a large sample size and good evaluation and imputation tools for pig genetic research.As one of the most important livestock and animal models of human diseases, the pig needs to be dissected its genetic mechanisms of traits through fine mapping to locate the causal loci. Recently, pig haplotype reference panels have begun to be used in pig genetic studies. For example, Yan et al.Therefore, the aims of this study are to (i) build the largest (in terms of sample size) reference panel of pigs to date and (ii) provide a user-friendly online tool for efficient phasing and imputation of missing genotypes based on a high-quality reference panel.In this study, we built a pig haplotype reference panel that integrates 1006 individuals’ , which was expected because low genetic similarity between the reference and imputed panels may decrease imputation accuracy.To mimic a typical imputation analysis, we first created three datasets by extracting high-coverage whole-genome sequencing genotypes for 81 Large White pigs at all sites included in the most popular commercial porcine microarray genotyping platform . These were used to impute the remaining genotypes, which were then compared to the held-out genotypes. The imputation accuracy as assessed by the concordance rate (CR) value reached 0.971–0.978  of variants in the reference panel, that can affect imputation accuracy. The r2 = 0.836), whereas Animal-ImputeDB had a CR value of 0.854  and nine  associated genes, respectively. Using the PHARP-imputed genotypes, we found novel genes, such as ANGPTL2, CCL8, TNXB, MC4R, PACSIN1, and MLIP  associated with the backfat thickness phenotype. We found a more significant SNP  with a tight linkage with this SNP (r2 = 0.99), implying a potential causal variant. Similarly, at the GRM4 and PACSIN1 loci, we found one  and two  missense variants, potential casual variants, with high linkages with their most significant SNPs  tested in the datasets of Xu et al. , suggesting high imputation accuracy. Compared to the test dataset 1, the test dataset 3  had a slightly decreased imputation accuracy , possibly caused by a low density of SNPs  and false genotyping of heterozygotes resulting from low coverage. We also investigated the imputation performance for a pig breed (JXH) that is not covered in PHARP and found that, as expected, imputation accuracy decreased  because of low genetic similarity between the reference and imputed panels. To overcome this limitation, we will substantially increase the ancestral diversity of the panel by sequencing more pig breeds in the future. Second, imputation accuracy increases with an increasing SNP density in the imputed panel and grows slowly after the SNP density surpasses 60 kb per SNP . Fourth, PHARP has a better imputation performance than the publicly available pig reference panel in Animal-ImputeDB. The imputation accuracy as assessed by the CR r2 value could be improved from 0.85  to 0.93  , probably because of the large increase in the sample size in PHARP (n = 1006). We are planning to sequence dozens of breeds that are not included in the first release of PHARP and add more pig WGS data that are publicly available worldwide to enlarge the ancestral diversity and sample size. Therefore, we expect to be able to make future gains in imputation performance.PHARP achieves high imputation performance. We systematically estimated the imputation performance of PHARP using multiple test datasets. First, PHARP is able to accurately impute porcine commercial SNP array chips  for the most represented pig breeds worldwide, such as the Large White and Duroc pig breeds. As test dataset 1 includes high-coverage  whole-genome sequencing, implying high-quality genotyping, we do expect the imputation performance estimated from these data to be more reliable and find that the imputation accuracy as assessed by CR value (mimicking a 50 K chip) surpasses 0.97 r > 0.92, SNP Fig. , commercWe have developed centralized imputation server resources to enable pig genetic researchers to easily carry out imputation, which will greatly facilitate the application of reference panels in imputation. Users simply upload phased or unphased genotypes, and imputation is carried out on online servers. The users will be alerted by email once imputation is completed, at which time they can download the imputed datasets.Increasing the power of association studies. We could replicate genes well known to be to highly related to a specific trait. For example, the melanocortin-4 receptor (MC4R) gene is reported to be related to fatness and growth traits in pigs21. However, Zhang et al. failed to detect this signal in D100 and L100 (two phenotypes of fatness and growth traits). Interestingly, we were able to rediscover this gene using their imputed-genotypes for those two phenotypes (Supplementary Table ANGPTL2) gene, with the most significant P value  were compared in several previous studies25, and generally, the sparse chip SNPs were sufficient enough to obtain accurate predictions. The reason might be that the inclusion of many SNPs could also induce more noise, and the sample size did not match the large number of SNPs. However, the prediction accuracies obtained using imputed WGS data will be higher for some traits. We observed this for the traits in the datasets of Xu et al. Some studies also showed that genomic prediction based on LD-pruned WGS-level SNP datasets could result in higher prediction accuracy26, which could be observed for the dataset of Xu et al., where the pruned SNP dataset had the highest prediction accuracies for most traits , resulting in a small proportion of shared variants. For example, less than 10% of the SNPs included on the 50 K SNP array are included in GGRS. Genome-wide eQTL mapping. Gene expression levels measured by high-throughput technologies, such as RNA-Seq, are treated as quantitative traits. Genotypes are also called using the RNA-Seq data from the same set of individuals and can be imputed to the WGS level, and statistical analyses are performed to detect associations between imputed markers and expression traits. Genetic resource identification. PHARP includes an enriched ancestral diversity that can be used as a control for clustering analysis. PHARP data can be applied to easily pinpoint an unknown individual/population as a novel genetic resource or identify which pig breed it is closely related/similar by doing cluster analysis such as the NJ-trees construction.There is an additional potential utilization of PHARP. In summary, we generated a large-scale reference panel for pigs, which will be a highly valuable resource for resolving the deficiency of large-sample-size pig genomic data. We believe that our efforts will markedly contribute to improving the genotype imputation accuracy in pigs, and ultimately facilitate genomic research of variants and their roles in pig complex traits.In the future, we envisage the reference panel increasing in size and consisting of samples from a more diverse set of breeds. On the one hand, our group will genotype thousands of individuals from more than 100 pig breeds using WGS in the upcoming one/two year and is cooperating with other pig genome research groups to include their WGS data. On the other hand, more pig WGS data are becoming publicly available. Thus, we expect to greatly enlarge the sample size of PHARP, which should lead to further gains in imputation performance, especially for rare variants. Moreover, we will add small indels into the reference panels and continue to update PHARP. We also considerate to add functions for visualizing pig genomic features, such as LD, selection signatures and MAFs, derived from this panel data.WGS for PHARP building. From 52 pig whole genome sequencing projects collected in SRA (as of April 2020), we collected 1097 individuals’ WGS data, including more than two thousands of experimental datasets (Supplementary Table Test data for missing genotype imputation performance based on PHARP. The test data included (i) WGS data  from PRJEB39374 and PRJEB38156; (ii) WGS data ; (iii) WGS data  and (iv) individuals genotyped by both the 50 K Chip and ELC  from our previous study31  and phenotypes of 13 traits related to growth and fatness and reproduction in two pig breeds (Duroc and Sujiang) were collected from three previously reported GWASs34 (see details in Supplementary Table Data processing and variant discovery. We used SRA Toolkit (https://github.com/ncbi/sra-tools) to download (prefetch) WGS data and convert (fasterq-dump) them from SRA to FASTQ format; performed quality control, read filtering and base correction for the raw FASTQ data by using fastp35 with default parameters; mapped the high-quality reads to the latest version of the pig reference genome (Sscrofa11.1) using BWA v0.7.1736 with the MEM function and the parameters for paired-end data; converted SAM files to BAM files and merged library data from individual and multiple experiments into one dataset using samtools v1.1037; removed duplicated reads with sambamba v0.7.138; individually calculated coverage and depth with Mosdepth v0.2.939; and finally applied GATK v4.1.640 HaplotypeCaller to each sample to generate an intermediate GVCF, which was then used in GenotypeGVCFs for joint genotyping across all samples.Sample filtering. We first removed samples that could not be successfully converted from SRA to FASTQ format  and that had a depth less than 4× (n = 166), as suggested by Jiang et al.41 to reduce false-positive variant detection in pigs. To detect possible duplicates, we counted the number of genotypes (measured by Euclidean distance using the dist function in R) that differed between each sample pair using the original genotypes of SNPs after pruning by PLINk v1.9 (--indep-pairwise 50 5 0.2)43. We identified four sample pairs with Euclidean distance outlier  as duplicates and removed one of the samples in each pair as described in Supplementary Fig. Site filtering. After sample filtering, we filtered SNPs with the following criteria: (1) “QD < 2.0, FS > 60.0, MQ < 40.0, MQRankSum < − 12.5, ReadPosRankSum < − 8.0, SOR > 3.0”; (2) a minor allele frequency (MAF) < 0.01; and (3) a call rate < 0.9 using GATK VariantFiltration. After applying these filters, in total 34,135,654 SNPs of autosomes were retained in the final site list.Pre-phasing. We applied the SHAPEIT v220 method to individually pre-phase the called genotypes for each autosome, as it was reported that the pre-phasing approach substantially improved imputation accuracy when using the haplotypes16.31. Finally, a total of approximate 32.5, 20.2 and 7.8 million of SNPs were kept for test dataset 1, 2, and 3 and 4, which were majorly used for imputation accuracy performance estimation.For each genotype imputation performance test dataset 1 and 3 from WGS, we used the same procedure and filtering steps as described above in “9, in which parameters were set to default values. The reference/imputed panel was pre-phased by SHAPEIT v220. We used two measures to evaluate the imputation accuracy: (i) the concordance rate (CR), which is calculated as the percentage of genotypes imputed correctly among the total imputed genotypes, and (ii) r2, a correlation-based measure, which is the squared correlation between the true and imputed doses of an allele across all imputed samples. Five scenarios of genotype imputation performance estimations are given below.We used the four experiments data sets (related to the three pig breeds) mentioned above to assess the imputation accuracy performance of our reference panel. Imputation was carried out using Minmac4To mimic a typical imputation analysis, we created three imputed panels on the basis of three chip lists: the Illumina PorcineSNP50 Genotyping BeadChip (50 K), the Illumina PorcineSNP60 v2 Genotyping BeadChip (60 K), and the GeneSeek GGP Porcine 80 K SNP chip (80 K). These pseudo-chip genotypes  were pre-phased by SHAPEIT v2 before being used to impute the remaining genotypes, which were then compared to the held-out genotypes.To estimate the general imputation accuracy affected by genotype array density, we also created imputed panels  with a gradient density of sites as follows: we divided autosomes into bins according to the physical location at a specific length , and randomly selected one site from each bin. We used the selected sites to compose new pseudo-chip genotypes, which were then used to impute the genotypes of unselected sites. We then compared the unselected site genotypes between imputed and observed  data and repeated the above pseudo-chip genotypes imputation at each specific density five times.Generally, increasing the size of the reference panel will increase the imputation accuracy, as a larger panel provides a larger set of template haplotypes to match against. We randomly selected 10, 30, 50, 100, 200, 400, 600, 800, and 900 samples from PHARP to construct a subset reference panel (repeated 5 times). Test dataset 4 and test dataset 1 mimicking a 50 K chip were used to estimate the imputation accuracy.We built two reference panels, i.e., a subset of the Large White (n = 115) and Duroc (n = 85) pig breeds with the largest sample sizes from PHARP, to compare the imputation accuracies of different reference panels. The imputation accuracy was estimated the same way as mentioned in the ‘Size of the reference panel’ section using the same test data sets.18, containing 233 samples. To keep the assembly of our test dataset 4 consistent with Animal-ImputeDB (based on Sscrofa10.2), we used liftOver (the chain file was downloaded from http://hgdownload.soe.ucsc.edu/goldenPath/susScr11/liftOver/) to convert the genome coordinates of test dataset from Sscrofa11.1 to Sscrofa10.2. We conformed their chromosome strand and allele order matched those of the Animal-ImputeDB reference panel using conform-gt.jar. We only used sites consistent between the reference panels to enable direct comparison. These 50 K genotypes of 20 samples were used to impute the remaining genotypes, which were then compared to the ELC genotypes of the same samples.To date, we have only found one publicly available pig reference panel from Animal-ImputeDBhttp://www.mysql.com) and runs on a Linux-based Apache Web server. PHP 7.0.33 (http://www.php.net/) is used for server-side scripting. We designed and built the interactive interface using layui (https://www.layui.com/) with the HTML, CSS and JS frameworks on the Web. We recommend using a modern Web browser such as Google Chrome (preferred), Firefox, or Safari to achieve the best display effect.The current version of PHARP was developed using MySQL 5.7.27 , and subsequently applied it to imputation data sets with an imputation quality threshold of R2 (Minimac4) > 0.4 before association testing.To illustrate the benefits of using the PHARP resource, we applied it to three previously reported GWASs as mentioned in the ‘Data sets and their sources’ section. For each study, we first pre-phased individuals by SHAPEIT v234. Association analysis using a single marker regression model was implemented in GEMMA v.0.98.1 software44. The association plots were produced using the R ggplot2 package.To make the association results comparable before and after imputation, we adopted the same association model and significance threshold as described in each original paper32 was also used to carry out genomic prediction based on different SNP datasets. The fivefold cross-validation scheme was adopted to evaluate prediction accuracy. Briefly, the individuals were randomly separated into five groups with the same sample size, and each time, the genomic estimated breeding values (GEBVs) of one tested group were predicted using the phenotypic information of the other four groups and genomic information of all individuals with the following genomic best linear unbiased prediction (GBLUP) model:45 , including (i) chip SNPs from the original studies (GBLUP_CHIP); (ii) imputed SNPs (GBLUP_IMP); and (iii) LD-pruned (r2 < 0.5) imputed SNPs (GBLUP_PRUNE) using PLINK (–indep-pairwise 50 5 0.2). 46, and the prediction accuracy was calculated as the Pearson’s correlation coefficient between the GEBVs and true breeding values (TBVs), where TBVs were estimated based on the full dataset using the GBLUP model with the GRM built from the chip SNP dataset as used in Xu et al.32. The fivefold cross validation was repeated ten times.Datasets included in the aforementioned GWASSupplementary Information.


CA12, CHRNE, and CASS4. Notably, the APOE ε4 variant reduces APOE expression across all regions, even in AD-unaffected controls. Our results reveal region-dependent and exon-specific effects of multiple aseQTLs, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. In an attempt to pinpoint the cell types responsible for the observed tissue-level aseQTLs using the snRNA-seq data, we detected many aseQTLs in microglia or monocytes associated with immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.Elucidating regulatory effects of Alzheimer’s disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We first develop a flexible hierarchical Poisson mixed model (HPMM) and demonstrate its superior statistical power to a beta-binomial model achieved by unifying samples in both allelic and genotype-level expression data. Using the HPMM, we identified 24 (~73%) aseQTLs in at least one brain region, including three new eQTLs associated with  APOE and TREM2, most of these identified signals are in non-coding regions and thus present difficulties in elucidating their causal pathways in neuropathology and pinpointing the genes, cell types, and brain regions mediating their associations with LOAD.Late-onset sporadic Alzheimer’s disease (AD) is the most prevalent progressive neurodegenerative disorder among all dementia cases affecting a large proportion of the elderly population . Late-oncis-acting expression quantitative trait loci (eQTLs). The cis-eQTLs have been extensively investigated in multiple tissues and regions across the brain [A potential biological consequence of a non-coding genetic variant is local regulation of gene expression. These regulatory variants are called he brain –13, subshe brain . More imhe brain , 14, of cis-regulatory effects, in this work, we focused on interrogating single nucleotide polymorphisms (SNPs) identified in GWAS of LOAD and conducting an allele-specific eQTL (aseQTL) analysis in four regions in the cerebrum using 3000+ tissue and cell-sorting bulk RNA-seq samples and >0.25 million neural cells from single-nucleus RNA-seq (snRNA-seq) data. We define aseQTLs as genetic variants associated with allelic expression of a local gene. The allelic expression can be measured at a single exonic SNP or a haplotype, and we focused on the SNP-level allelic expression  in this study. Compared with genotype-based eQTL analysis, aseQTL analysis considerably boosts the statistical power. Allele-specific analysis exploits the imbalance between allelic counts within each of the heterozygous subjects. Because allelic imbalance is insusceptible to inter-subject variation, such as environmental exposures, RNA degradation, and technical noise introduced in experiments, this strategy markedly improves the statistical power and the accuracy of the estimated eQTL effects.To discover novel eQTLs across more brain regions and further explore their cell-type-specific A handful of statistical models have been proposed to leverage allelic imbalance or to detect aseQTLs in bulk RNA-seq data –21. We cHere, we propose a flexible and statistically powerful method based on a hierarchical Poisson mixed model (HPMM) for detecting aseQTLs. The HPMM incorporates both allelic and genotype-level expression and prioritizes the heterozygous samples to boost the statistical power. Our simulation study demonstrates that combining both homozygous and heterozygous samples using the HPMM improves the statistical power compared to a beta-binomial model. Furthermore, it is straightforward to implement using existing statistical tools like the lme4 R package. This is achieved by introducing hierarchical random effects and a coding strategy for genotypes so that allelic counts of a heterozygous sample are remodeled as paired samples. In the snRNA-seq data, unique molecular identifiers (UMIs) are often adopted to mitigate a source of bias introduced in the library amplification step during the sample preparation . Such siTREM2 and CD33), we further evaluated the regulatory effects in these two cell types using both cell-sorting bulk RNA-seq and snRNA-seq data. Our results reveal that many AD-associated SNPs are tissue-level aseQTLs in the brain and show exon-specific regulatory effects. Some of these effects are mediated by specific neural or immune cells.By leveraging the HPMM, we performed a comprehensive aseQTL analysis to investigate the regulatory effects of 33 AD-associated SNPs on the expression of local genes. We examined and compared the aseQTL effects across the prefrontal cortex (PFC), the temporal cortex (TC), the posterior cingulate cortex (PCC), and the head of caudate nucleus (HCN) by using bulk RNA-seq data. The cross-region comparison is of interest because the early stage of LOAD involves the hippocampus and entorhinal cortex, and the degeneration spreads to other brain regions in a later stage, suggesting that the heterogeneity across brain regions may partly explain the pathogenesis of LOAD. To explore the cell-type-specific regulatory mechanisms, we conducted eQTL and aseQTL analyses using snRNA-seq data comprising 240,000+ cells from the PFC. We investigated major neural cell types, including excitatory neurons, inhibitory neurons, astrocytes, oligodendrocytes, and oligodendrocyte progenitor cells (OPCs). To our knowledge, this is the first analysis of aseQTLs for AD at the single-cell level. Because many AD-associated SNPs are located in or near genes specifically expressed in microglia or monocytes  AD-associated common SNPs  > 5%) in the European population reported or replicated in large-scale GWAS and meta-analyses [APOE region, 23 SNPs were selected from the lead SNPs reported in [We specifically focused on 33 independent (except for the orted in  to removcis-eQTLs in the brain [p < 0.05) associations  associated with the ASE of CLU only in the TC, but not even nominally significant in the other regions , and the effects observed in the other exons were not significant  in the second exon of nt Table . Becauseal cells , 28, thiicroglia .APOE, CLU, and PILRB. Allelic associations in these genes exhibit opposite effects at SNPs located in different exons , the leading genetic risk factor, was associated with elevated expression of exon 2 measured at rs440446  of APOE . We found that the significant associations were preserved in the control subjects, suggesting that the regulatory effects of the APOE ε4 allele were not mediated through the diagnosis of AD.Additionally, we observed exon-dependent associations in, e.g., ons Fig. . Furtherons Fig. . For exa446 Fig. , but witPOE Fig. . These ellele rs49358-C, tPOE Fig. . The expllele rs49358-C, tPOE Fig. , and ENSAPOE, the identified aseQTLs can be mediated by their high LD with the exonic SNPs, which might be the driving variant per se. Therefore, to further explore the biological interpretation, we divided these significant associations into three categories based on the LD between the SNPs and whether the exonic SNP per se was associated with the ASE  with rs2093760 and very strongly (p = 8.95E-16) associated with the ASE   Fig. , Middle.10 Table . These AWe classify the remaining significant aseQTLs as the third category, mainly including the situation in which the disease-associated SNP was not in strong LD with the exonic SNP, and the association between the ASE and the exonic SNP was much more significant than that of the AD-associated SNP Fig. , Bottom.NDUFS2, ADAM10, CR1, APH1B, POLR2E, ZNF232, PILRA, STAG3, and PRSS36 are reported in at least one of these studies. The association between rs2093760 and CR1 was identified only in the TC in [CLU, BIN1, CD33, PTK2B, B4GALT3, CASTOR3, and ADAMTS4 in the brain are not reported in these gene-level results, probably because exonic SNPs disrupting the exon-level or transcript-level expression might not be detected from the analysis of the gene-level expression. Therefore, we then compared the summary statistics of splicing QTLs (sQTLs), transcript-level and exon-level eQTL analyses in GTEx [CLU, PTK2B, BIN1, and CASTOR3 are in high LD with the sQTLs in the PFC reported in [CLU, our aseQTL analysis captured two sQTLs, rs9331888, reported in [CA12 in the HCN, rs113260531 and CHRNE in the PFC and HCN, rs6014724 and CASS4 in the HCN, are novel.To further assess these significant associations, we compared the identified aseQTLs with gene-level eQTLs reported from a previous meta-analysis using the same cohorts in the PFC and TC  and prevhe TC in  and in t in GTEx , CommonM in GTEx . Indeed,orted in . Notablyorted in , 35 and p < 0.05)  Table . In addiGiven the evidence that many AD GWAS SNPs were aseQTLs in the cortex, we next attempted to pinpoint the neural cell types that mediate such associations. We collected snRNA-seq data in the frontal cortex comprising >240,000 cells from 71 subjects from two cohorts in ROSMAP , 38 Fig. Using tp < 0.05) between two AD-associated SNPs and the ASE of three genes, including PILRB, TRIM4, and MTCH2  in PILRB . Additionally, both exonic SNPs were much more strongly associated with the allelic imbalance of PILRB , among which 327 tests (65.5%) were in the two types of neurons Fig. . Only foH2 Table . All theH2 Table . SpecifiRB Table . Therefons Table , Fig. S3ns Table , suggestcis-eQTL analysis for local genes within a ± 500k bp window in each of the six neural cell types. We identified three significant associations (FDR p < 0.05)  significant in inhibitory neurons and oligodendrocytes.A drawback of the allelic analysis using the snRNA-seq data is the low expression called at exonic variants that are not close to 3’-end, which potentially misses many signals and leads to a small number of identified associations. To compare the results more thoroughly between the allele-specific and the genotype-level eQTL analyses, we also performed a genotype-level 5) Table , includites Fig. . CR1 waslia Fig. . BecauseCR1 Fig.  is likelely Fig. f, g. Thep < 0.05) . The associations with BIN1, FCER1G, and ABCA7 were also observed in our region-specific aseQTL analysis  were not detected in the region-specific analysis, probably due to their much lower expression at the tissue level. Most of the significant associations in microglia were primarily driven by strong allelic imbalance observed in the double-heterozygous samples  Table  involvinsis Fig. , suggestnds Fig. . The othles Fig. . Becausecis-eQTL of HLA-DQA2 in microglia with a more significant p value after including the microglia-specific samples  and between rs867611 and PICALM (p = 9E-4)  Table . In contis Table , suggest+CD16- classical monocytes in a cohort [p < 0.05) involving six AD-associated SNPs and nine genes  with the exonic variants.Multiple putative AD-related genes are also abundantly expressed in monocytes, implying that monocytes might also be involved in AD by, for example, infiltration through the blood-brain barrier –43 or ita cohort  comprisies Table . Most ofCD33 measured at chr19:51728641 (rs2455069) and chr19:51728477 (rs12459419) in monocytes (p = 4.52E-12 and 2.05E-6)  Fig. , confirm-6) Fig.  that oveHLA-DQB1 in monocytes  with an exonic variant rs11421, located in the 5’ UTR of FCER1G, which was itself a significant aseQTL of FCER1G , and the direction of effect sizes was consistent . In comparison, a previous eQTL analysis in classical monocytes using the DICE (~100 samples) [HLA-DQB1 was also observed in this analysis. This study did not investigate the other associations because these genes were not within our defined window for a cis-gene.The evidence from allelic imbalance is in line with genotype-level differential expression in most of these associations Fig. , Fig. S1samples)  identifiIn this study, we investigated the regulatory effects of AD GWAS SNPs using the allelic expression in four brain regions, six major neural cell types, and monocytes. Our results replicate not only previously reported signals and, most importantly, also discover novel aseQTLs. It is shown in  that theCD33 detected significant associations between rs3865444 and allelic imbalance at exonic SNPs exclusively in exon 2. These associations were observed in monocytes and multiple brain regions, including the TC, PCC, and HCN, supporting previous findings showing that the AD GWAS locus rs3865444 is associated with the splicing of exon 2 of CD33 [CLU, of which our aseQTL analysis captured two sQTLs in the promoter and exon 5. A recent study reveals that rs7982 is associated with intron retention in three regions in the temporal lobe, including TC, superior temporal gyrus, and parahippocampal gyrus [We have demonstrated that the SNP-level aseQTL analysis not only identifies eQTLs but can also uncover exon-specific regulation. As an example, our aseQTL analysis of  of CD33 , 34, 46.al gyrus . Our resAPOE ε4 variant is associated with attenuated expression of the major transcript of APOE based on the evidence from both allelic imbalance and gene-level expression. These results corroborate previous findings of lower mRNA and protein levels of APOE in ε4 carriers [APOE ε3/ε4 carriers at the tissue level. Because APOE expresses most abundantly in astrocytes among these neural cell types, astrocytes might be the primary cell type contributing to this effect. Due to this eQTL effect, the higher proportion of APOE ε4 carriers among the patients with AD can lead to a decreased expression observed in the patients, explaining the reported association between the reduced APOE expression in astrocytes and the diagnosis of AD [APOE expression in microglia is upregulated in patients with AD [APOE in microglia might be due to either a different regulatory mechanism or a disease-associated subpopulation specifically in microglia, although recent studies have not detected an AD-specific subpopulation in microglia in the cortex of patients with AD [APOE ε4 variant at the cell level, including microglia.Interestingly, the carriers –51. Our is of AD . Compare with AD . This ov with AD , 52. MorCR1 observed in the TC was seen in oligodendrocytes but not in microglia. CR1 is one of the key genes involved in the complement system and shows the highest expression level in oligodendrocytes followed by microglia among the neural cells in our data. Genes in the complement system are abundantly expressed in microglia and are co-expressed with APOE [CR1 in almost complete LD with rs2093760. More research is needed to elucidate which functional consequence of this haplotype is the primary factor contributing to the risk of LOAD. Compared to a previous study detecting few cis-eQTLs in the primary microglia [BIN1, FCER1G, ABCA7, and HLA-DQB1, which were also detected in the region-specific analysis. These findings suggest that microglia might mediate their effects observed at the tissue level.Using the single-cell data, we have attempted to pinpoint the cell types that mediate genetic regulatory effects detected at the region-specific analysis. For example, the association between the haplotype containing the GWAS SNP rs2093760 and the ASE of ith APOE . Our finicroglia , we idenWe have demonstrated using both a simulation study and the real data analysis that the proposed HPMM increases the statistical power by prioritizing the evidence from the allelic imbalance and combining the evidence from both double-heterozygous and homozygous subjects. This method is fast, easy to implement, flexible for adjusting additional covariates and investigating interactions. Our real data analysis also demonstrated that the additional information from the genotype-level expression is important, especially when the evidence from the ASE is limited due to a small sample size. Future work can be focused on further extending the HPMM to accommodate haplotype-based ASE.It should be noted that the LD between the GWAS SNP and the exonic SNP among the double-heterozygous subjects complicates the interpretation of the findings from the SNP-level aseQTL analysis. It is easier to draw a conclusion when the exonic SNP that is used as the proxy for measuring the ASE is not associated with the ASE intrinsically. Otherwise, we show both theoretically and practically that a false interpretation of the regulatory function can result from an inflated conditional correlation between the exonic proxy SNP and the GWAS SNP among double-heterozygous subjects. We implement a straightforward classification strategy to tackle this issue to gain better biological insights. This problem might not be serious in a haplotype-level allele-specific analysis because the ASE was quantified by aggregating multiple exonic SNPs of the gene.One of the limitations of the current cell-type allele-specific analysis using the 3’-sequencing snRNA-seq data is that the reads are distributed non-uniformly, and its concentration is biased towards 3’ UTR or 5’ UTR depending on the experimental protocol. Consequently, many exonic SNPs within the gene body are not captured by most reads or covered by a quite low number of reads. This technical limitation leads to low statistical power to detect associations in these regions. Further research using long-read sequencing technologies might offer much better coverage for the aseQTL analysis at the cell level. Another limitation of this study is the small sample size in the snRNA-seq data, which might at least partly justify the lack of significant findings in the cell-type-specific analysis. For loci with low counts or a small sample size, more sophisticated methods based on shrinkage might also be utilized .In conclusion, we detected many AD GWAS loci showing strong associations with the allelic expression of local genes or exons, which can be tissue- or cell-type-dependent. Our results pinpointed the underlying brain regions and cell types that mediate the associations for multiple AD GWAS loci, which provide valuable insights into the cellular regulatory mechanisms in the genetic architecture underlying the pathogenesis of LOAD.https://www.synapse.org/#!Synapse:syn22333035). The library was generated using either a poly-A selection or ribosomal depletion method. The reads were trimmed before being aligned to the reference genome (hg19) using Bowtie. These RNA-seq samples were collected from 925 subjects with an age of death ranging from 67.4 to 108.3, among whom 602 are female, and 571 are patients diagnosed with AD by the pathology of the post-mortem brain. We further removed from the downstream analysis five subjects whose genotypes called from the RNA-seq samples were inconsistent with their whole-genome sequencing (WGS) data. The bam files of the 317 bulk RNA-seq samples in the TC from the MayoRNAseq project [https://www.synapse.org/#!Synapse:syn20818651). This data set included 82 patients with AD, 84 patients with progressive supranuclear palsy (PSP), 71 subjects with pathologic aging, and 78 healthy controls. The reads were aligned to the GRCh38 reference genome using the SNAPR software [We obtained 747, 572, and 882 bam files of the tissue-level bulk RNA-seq samples in the HCN, PCC, and PFC, respectively, in ROSMAP from Synapse ( project  were dowsoftware .https://www.synapse.org/#!Synapse:syn11468526 and https://www.synapse.org/#!Synapse:syn22024496). The monocytes were extracted based on the surface marker CD14+CD16- from peripheral blood mononuclear cells (PBMCs) of 615 subjects. We included 336 samples that had both the RNA-seq and genotype data, including 238 females and 139 patients with AD. RNA molecules were extracted using the ribosomal depletion method, and the library was prepared using a SMART-seq2 or SMART-seq2-like protocol. The microglia were isolated from the dorsolateral PFC of 10 subjects using magnetic anti CD11b beads and then followed by fluorescence-activated cell sorting based on the CD11bhighCD45int7AAD- staining profile. RNA molecules with a poly-A tail were extracted, and the library was constructed using the SmartSeq-2 protocol [We obtained the fastq files of the cell-sorting bulk RNA-seq samples in monocytes and microglia in ROSMAP from Synapse . The two snRNA-seq data sets in the PFC included ~80,000 cells in Brodmann area (BA) 10 from 48 subjects and >160,000 cells from 24 subjects. More details about the generation of these two data sets are described in [We obtained the fastq files of the three snRNA-seq data sets, two in the PFC and one in microglia, in ROMSAP from Synapse . The trimmed fastq files were then aligned to the hg19 human genome assembly using the STAR aligner [https://bioconductor.org/packages/release/data/annotation/html/SNPlocs.Hsapiens.dbSNP144.GRCh37.html) and the Haplotype Reference Consortium (HRC) SNP reference panel, and intersect at least one exon defined in the GTF build GRCh37.87. Using the WASP-filtered bam files, the ASE at the exonic SNPs were quantified using the pileup function in the samtools [To process the two bulk RNA-seq data sets in microglia and monocytes that started with fastq files, we first trimmed the paired-end reads using Trim Galore! , including a WGS data set of 1196 subjects in the VCF format and two SNP array data sets of 382 (Illumina HumanOmniExpress) and 1709 (Affymetrix GeneChip 6.0) subjects in the plink format. If a subject had both the WGS and SNP array data, we always used the WGS data for better accuracy. Before the imputation, we checked strands, alleles, and positions and removed ambiguous SNPs using the tool HRC-1000G-check-bim.pl with default settings and the reference panel of the EUR population in the 1000 Genomes project (https://www.well.ox.ac.uk/~wrayner/tools/). We phased and imputed the WGS data set using the Michigan Imputation Server [https://www.synapse.org/#!Synapse:syn10901601). We used the same procedure as in the preparation of the ROSMAP genotype data for the imputation. We imputed the genotype data using the TOPMed imputation reference panel and mapped them back to hg19 using liftover. In the allele-specific analysis, we used only high-quality imputed exonic SNPs that had an imputation quality score>0.96 to control phasing errors.The three genotype data sets in ROSMAP were downloaded from Synapse  with the parameters “-l A --validateMappings --rangeFactorizationBins 4”. The downstream analysis followed the same procedure as described above.In the analysis of transcript-level g salmon  with a pTesting allelic imbalance is much more powerful than testing genotype-level expression data because the allelic information is generally not subject to technical noises or confounders, which is a severe problem in gene expression analysis. We propose an HPMM that enjoys the power gain from the allelic imbalance and borrows evidence from the expression of homozygous samples to further improve the statistical power. This HPMM is also straightforward to accommodate covariates and interaction terms. The key idea in the HPMM is to introduce two random-effects terms. One accounts for the allele-level overdispersion in the count data, and the other treats the two allelic counts of each subject as paired samples so that the allelic imbalance within the heterozygous samples has higher influence on the inference than the homozygous individual-level expression.A and a, and only one count is observed for each homozygous subject (AA or aa). Denote by yij the number of RNA-seq reads overlapping the exonic SNP of subject i, j represents one of the allele patterns, Xij the coding of the allele patterns defined byWe first describe the HPMM for analyzing the exonic SNPs. The model can be applied with a minor modification for the aseQTL analysis, which will be discussed later. Assume that each heterozygous subject has two allelic counts corresponding to the two alleles nda Fig. . Denote μ is the intercept, β is the effect of the exonic SNP, γ are the design matrix and effects of covariates, bi is the individual-level random effects, εij is the allele-level random effects, both of which follow a zero-mean normal distribution, and li is the library size or a normalizing factor. That is, i is heterozygous. The rationale behind this model is to treat the two observations from a heterozygous subject as paired samples, which is achieved by halving its library size and introducing the random effects bi to capture the individual-level noises. The HPMM can be easily fitted using, e.g., the glmer function in the lme4 R package [εij can be omitted to improve the power. To test whether the allelic imbalance is different between two groups , we add an interaction term to the modelDi ∈ {0, 1} is the diagnosis of the disease. Thus, the differential eQTL effects between case-control groups can be detected by testing δ = 0 in model (2).We use the following HPMM to estimate the exonic aseQTL effect package . An examεij, as shown in [betabin function in the aod R package (https://cran.r-project.org/web/packages/aod/index.html). We simulated allelic expression of 500 subjects based on model (1) with β ranging from 0 to 0.3, If there is no homozygous subject, model (1) is equivalent to a binomial model with an overdispersion modeled by shown in . We thusshown in  implemenA/a and an exonic SNP with two alleles B/b, where a and b are the minor alleles. Denote by i carrying the allele pattern j of GWAS SNP, i who is double-heterozygous, we have Xij the variable coded for the GWAS SNP defined byThe aseQTL analysis of AD GWAS SNPs adopted a similar HPMM framework. The difference is that the allelic counts are not directly observed for the GWAS SNPs but are obtained through phased haplotypes as they are not located in an exonic region. Consider haplotypes containing a pair of SNPs, a GWAS SNP with two alleles a}) Fig.  because Xij, and the estimated β was the aseQTL effect of the AD GWAS SNP (See Supplementary Text for an example script).In this study, we fitted model (1) using this definition of β. Therefore, the correlation between alleles in the double-heterozygous subjects is more influential. It should be noted that this allelic correlation between the GWAS and exonic SNPs in double-heterozygous subjects can be very large even if the two SNPs are not in LD at the population level, particularly when the sample size is small or the MAF is low , then Pab is nearly zero. Thus, under a small sample size, we may not even observe subjects with the haplotype ab in double-heterozygous subjects. The lack of this haplotype means that the only possible combination in the double-heterozygous subjects is The interpretation of the allelic analysis of a GWAS SNP is more complicated than that of an exonic aseQTL because the allelic reads are not directly measured for the GWAS SNP. Like GWAS, the LD between the two SNPs complicates the interpretation. Because the double-heterozygous subjects are treated like paired samples in model (1), they have a large impact on the estimate of low Fig. . For exaR2 > 0.96.We performed stringent quality control for sequencing error and mapping bias, which might significantly affect the allele-specific analysis as mentioned in . As deschttp://eqtl.brainseq.org/phase1/eqtl/. The summary statistics of gene-level and transcript-level eQTLs in CommonMind [https://www.ebi.ac.uk/eqtl/). The functional annotation of the exonic variants in the aseQTL analysis was performed using the VariantAnnotation R package [TxDb.Hsapiens.UCSC.hg38.knownGene, the amino acid coding change , and prediction of the impact of non-synonymous variants using PolyPhen (Polymorphism Phenotyping).To compare the eQTL results in CD14+CD16- classical monocytes, we obtained the VCF files containing the summary statistics of significant eQTLs in classical monocytes identified in the DICE. To compare the eQTLs in the brain regions, we obtained the summary statistics of significant eQTLs from GTEx (v8) in three brain tissues, including the PFC, ACC, and hippocampus , and frommonMind  were dow package , in whicSupplementary legendsTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9Figure S1Figure S2Figure S3Figure S4Figure S5Figure S6Figure S7Supplementary Text


This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes . In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed. Bos indicus) an important genetic resource for Brazilian pasture-based beef production systems. Nonetheless, B. indicus breeds generally present lower reproductive efficiency compared to taurine cattle  is one of the most common selection criteria for fertility in beef cattle breeding programs, among other reasons, because it can be easily measured and contributes to improving heifers’ sexual precocity. Identifying genes associated with the maintenance of reproductive functions is therefore of paramount importance for enhancing the understanding of the AFC genetic basis, which may have practical implications in designing more efficient breeding strategies to improve fertility rates in Nellore cattle populations. Technological advances and cost reduction of high-throughput genotyping technologies have popularized genome-wide association studies (GWAS), which have contributed to revealing several candidate genes for fertility-related traits in beef cattle over recent years .loci effects assuming either infinitesimal contribution or locus-specific variance assigned by different a priori distributions  to identify potential causal variants using genome-wide data, especially for human diseases . ML requThe phenotypic and pedigree data were obtained from the Alliance Nellore database, which integrates information from Nellore cattle raised in different commercial herds, located in the Southeast, Midwest, and Northeast regions of Brazil. Animals included in the database were born between 1984 and 2015. The reproductive management adopted in those herds involves an in-advance breeding season occurring between February and April, with approximately 60 days in length, in which heifers between 14 and 18 months of age are exposed to reproduction for identifying sexually precocious animals. Heifers that did not conceive in the anticipated breeding season participate along with the other dams in the regular breeding season occurring between November and January.y is a vector of observed phenotypes; a is the vector of random additive genetic effects; X and Z are incidence matrices connecting a to the observed values, and e is the vector of random residuals. It is assumed that a ∼ N  using the BLUPF90 family programs (2) for the studied trait was computed as In this study, the Age at First Calving (AFC) was adopted as a fertility-related trait, obtained as the difference in days between the date of first calving and the dam birth date. The contemporary groups (CG) comprised animals born in the same herd, year, and season, and which were raised in the same management group at weaning and yearling. A data editing step was performed, in which animals with records deviating ±3.5 standard deviations from the CG mean were excluded from the dataset. Further, CG with less than five observations were not considered. A mixed model approach was used to compute the estimated breeding values (EBV) for AFC, considering the following model:programs . The narp-value lower than 10−5 for the Hardy-Weinberg equilibrium test. Only samples with a call rate higher than 0.90 were maintained in the genotypic data. The genotypes file filtering was performed using the R software , which were initially genotyped with either the Illumina BovineHD panel  or with the GeneSeek Genomic Profiler Indicus HD , with approximately 777,000 and 75,000 SNPs, respectively, distributed throughout the genome. The lower density panel (GCP75Ki) genotypes were imputed to HD using the FImpute v2.2 software , considesoftware . After Qn representing the number of available samples and p the number of SNPs, coded as 0, 1 and 2 for genotypes AA, AB, and BB, respectively. Initially, a bootstrap sample is drawn from this data set and used for training an individual classification or regression tree. At each node of this given tree, a subset of p SNPs and evaluated using a recursive binary splitting rule, for which the best predictor variable The random forest (RF) is a machine learning method that aggregates complementary information from an ensemble of classification or regression trees trained on different bootstrap samples  drawn with replacement from the original data set . Brieflyesponses . The preith animal computed from trees in which this animal was OOB, and A particularity of the RF is the out-of-bag data (OOB), which corresponds to the animals not included (roughly 1/3) in the bootstrap sampling for building a specific tree. Since the bootstrap sampling is performed with replacement, the trees are built using random samples of the same size as the original training data. Notice that some observations may appear more than once in the bootstrap sample, whereas others will not be sampled at all (composing the OBB sample for that specific tree). The OOB can be used as an internal validation set for each tree, which allows the computation of the generalization error term . In the jth SNP as the average difference between the Another appealing attribute of this internal validation process is that it can provide variable importance measures (VIM) for each predictor variable composing the regression trees. The most frequently used measure is the permutation-based VIM, which can be internally computed for the on error . For an randomForest package  was set to default (nodesize = 5) in all analyses. The parametrization that produced the lowest final The GWAS was performed using the  package  availabl package . Becausece score :fSNPj =r2 metric using the Gaston R package  throughout the manuscript for conceptually differentiating associations between unlinked loci.To improve the stability of the GWAS results, the importance scores of each SNP were averaged over the five independent RF analyses to compute the final importance scores. A common practice in genome-wide association studies performed with RF is to set the absolute value of the most negative importance score as the threshold for identifying a subset of relevant SNPs , this wo package . Notice https://www.ncbi.nlm.nih.gov/genome/gdv/?org=bos-taurus) from the National Center for Biotechnology Information (NCBI), considering the ARS-UCD1.2 (https://www.ncbi.nlm.nih.gov/assembly/GCA_002263795.2) as the reference map. For gene annotation, it was considered a 500 Kb window (SNP location ±250 Kb) harboring each SNP with The identification of candidate genes flagged by SNPs previously selected in the RF analysis was performed using the genome data viewer  are vectors of pre-selected SNPs in the RF analysis ith SNP, i and j, The generated hierarchical tree-based structure in the RF algorithm is informative for capturing both additive and non-additive effects, especially SNP-SNP interactions. In this regard, a marker can receive a high importance score if it presents a strong marginal additive effect or if it interacts with other markers to create important predictive patterns. However, the RF importance scores per se do not provide any information on the nature of the genetic effects captured by SNPs selected in the genome-wide scan. To investigate the gene action of markers previously identified with the RF algorithm, the following linear mixed models were fitted:p matrix  and σu2p-values were corrected for multiple testing using false-discovery rate  and each pairwise combination between two lead SNPs (M2). The variance components were estimated iteratively using the EMMREML R package . The sigate FDR;  thresholate FDR; ; the PPIIn the present study, the estimated breeding values (EBVs) for age at first calving (AFC) of 868 sires and 2,306 dams with available genotypes were used as response variables in the RF-based genome-wide association study for AFC. The estimated heritability for AFC was low (0.08 ± 0.005), indicating that this trait is highly influenced by environmental factors and other effects not accounted for in the mixed model analysis. Because of the low heritability value found, a 0.3 cut-off value was imposed for the EBV accuracy to reduce the noise inclusion in the RF analyses. The EBV for both sires and dams showed an approximately normal distribution, lying in similar intervals and with average values of −0.37 ± 19.5, and −4.4 ± 15.4 days, respectively . On the The influence of RF parameters on the model predictive performance is presented in on error . This paon error . Since tBos taurus autosomes (BTA) were obtained in five independent RF-based analyses, initialized with different seeds  were in exon regions, 33.33% (39) were in intronic regions, and 59.82% (70) were located downstream or upstream of candidate genes. Considering the 250 Kb size (downstream-upstream) window, the selected SNPs with the highest r2 in those blocks were 0.71 (0.45–0.99), 0.92 (0.84–0.99), and 0.96 (0.84–0.99), respectively.Most of the selected variants in the same BTA are in LD blocks with other relevant neighboring markers , providiNLRP5, NLRP8, NLRP13 (candidate genes), and NLRP9 (training list), FUT8 (candidate gene) and FUT1 (training list), and SEMA4C (candidate gene) and SEMA4A (training list).The full list of candidate genes located within the 250 Kb downstream-upstream interval flagged by the lead SNPs is shown in a priori evidence reported in the literature, the most promising candidate genes found for AFC are SP3 (BTA 2), TBX19, CD247, CREG1, DCAF6, ADCY10, MPZL1, MPC2, POU2F1, GPR161 (BTA 3), ATP2B1, DYRK2, APOBEC1, USP15, DPPA3, NANOG (BTA 5), FUT8, LMAN2L (BTA 10), AFF3, ATP6V1B1, SEMA4C, VAX2, TEX261, ZAP70 (BTA 11), MYO18B (BTA17), CNOT3, NLRP5, NLRP8, NLRP13, LOC107131469, LOC107131476, LOC107131477, LOC107131465, PRPF31, RPS9, TFPT (BTA18), MKRN3, NDN, MAGEL2, SNRPN, SNURF, GABRG3, UBE3A (BTA21), RAB40C, STUB1, and AXIN1 (BTA 25).According to the evidence found in the reported literature and the functional analysis results, almost all genomic regions highlighted in this study encompass candidate genes with key roles in male or female fertility, or with growth-associated functions. Considering the prioritization analysis (Toppgene), the functional enrichment (Cytoscape), and the NANOG is associated with embryo development and cellular response to growth factor stimulus, and the GPR161 participates in three different functional groups: “embryo development,” “bounding membrane of organelle,” and “nucleobase-containing small molecule metabolic process”  obtained from the single-marker linear regression (M1) was 0.61, indicating only partial agreement between the two approaches. The M1 analyses revealed that several SNPs identified with the RF algorithm do not present strong marginal linear effects, especially those located in BTAs 3, 5, and 11. However, the M2 analyses indicated that several of these markers with weak marginal effects are potentially involved in hotspots of local or inter-chromosomal additive-additive epistatic interactions , located approximately 109 Kb upstream of an uncharacterized gene (gene ID: 112446651), presented suggestive interaction  with markers located in intronic or exonic regions of at least 4 different genes of the BTA 21 .The most significant interaction effect in the M2 analysis involved a marker in the BTA 11  within an intronic region of the zed gene . Interesp-value < 1 × 10−6), indicating that the resultant gene transcripts are at least partially biologically connected and interact more than expected for a random set of proteins of the same size. Although the statistical interactions found in this study were not directly confirmed by the PPI network, many genes flagged by SNPs with significant inter-chromosomal epistasis appeared in the network with connections involving the same set of autosomes. For instance, the CD247 (BTA3) presented the strongest evidence for a functional link with the ZAP70 (AFF3 (BTA11). Another noticeable example is the TBX19, that formed edges with two other genes located in the BTA11 (ANKRD39 and ANKRD23). In addition, there is strong evidence that the edge SNRPN—PRPF31 connects functionally two clusters of genes located in the BTA21 and BTA18  was significantly associated (FDR = 1.07 × 10−6) with the Prader-Willi and Angelman syndromes pathway in the PPI analysis.The PPI analysis revealed 128 edges for the 69 genes entered, with roughly 54% of the connections representing moderate to strong evidence according to the database mining . The PPIhe ZAP70 , which ind BTA18 . Noticeand BTA18 . MoreoveAge at first calving is a complex trait that reflects the heifer’s reproductive performance in at least three different stages, the time to puberty onset, the interval between puberty onset and the first conception, and gestation length. It is a sex-limited trait that presents low to moderated heritability estimates and has polygenic nature, which imposes several limitations on gene mapping . In thisSP3, DPPA3, NANOG, GPR161, SEMA4C, VAX2, MYO18B, CNOT3, NLRP5, ATP6V1B1, and AXIN1) that coordinate biological functions indispensable for embryonic development  has been found to present high expression in the oocyte of human primordial follicles  have a co-expression role in the bovine embryo pre-implantation process . The CCR4-NOT transcription complex subunit 3 (CNOT3) is a transcription activity regulator, this gene was flagged by SNPs validated for fertility (pregnancy within the first 42 days of mating) in two distinct dairy breeds  and the CCR4-NOT deadenylation complex (including CNOT3) play an essential role in male germ cell development in mice , integrates the subcortical maternal complex, an essential multiprotein complex for embryonic development and uniquely expressed in mammalian oocytes and early embryos  in this same chromosome may have complementary functions in embryo development regulation through hemoglobin complex-related pathways (. Hemoglobin (Hb) is mainly found in erythrocyte cells; however, there is recent evidence of ovarian regulation of Hb synthesis through the ovulatory signal cascade, with high expression of Hb subunits in human granulosa and cumulus cell samples, suggesting a potential role of the hemoglobin complex in the early embryo development  was associated with pheromone receptor activity  has a critical role in sperm maturation in the epididymis, this gene is located in the BTA 3 and downstream the marker BovineHD0300000287. It was noticed that splicing errors in the ADCY10 were responsible for bovine spermatozoa subfertility (ubiquitin Specific Peptidase 15 (USP15) in mice is expressed in the developing acrosomal cap of spermatids in the testes , significantly associated with sire conception rate (NOB1 and NFTA5 (located in the BTA 18), found in whole-exome sequencing of bulls divergent for fertility , which has a causal variant affecting cryptorchidism susceptibility in rats  found in the BTA 5 , progesterone (PGR), and androgen (KLK3) receptor responsive genes  approach, the same set of genes found in the BTA21 was reported for early pregnancy in Nellore cattle, in a study that used partially the same dataset as in the present work . These avariance . Hence, ATPase Ca++ transporting plasma membrane 1 (ATP2B1) ends approximately 215 Kb downstream of the marker BovineHD0500005765 (19.82 Mb). This same gene was located in the vicinities of single nucleotide polymorphisms significantly associated with calving interval in Italian Holstein Cattle , the RPS9 may also have an important role in the regulation of AFC. This gene is located at an intronic region of a putative QTL for calving traits  segregating in Holstein cattle at approximately 57 Mb (RPS9 had the strongest associations with fertility traits (p < 1 × 10−10) in dairy cattle and, at the same time, exhibited lesser expression in the corpus luteum of low fertility cows  in standard GWAS and the remaining markers  is independent of the interacting loci allele frequencies and its knowledge may present importance to dissecting the genetic architecture of complex traits and understanding the biological function of candidate genes  used to investigate the genetic effects of markers identified in the RF genome-wide scan, the random polygenic component accounted for approximately 54% of the total observed variance in the breeding values. This result matches the pedigree-based EBV expected accuracy for the reference population so that the main and interaction effects were captured as extra hidden variation in the residual component. This is important to avoid potential confounding with the additive covariance structure present in the data.loci (Nonetheless, caution is required to infer the significant interactions observed in this study as causal gene-gene epistatic effects, since imperfect LD between a marker pair and the causal QTL can create the illusion of the presence of non-linearity in purely additive systems, the so-called phantom epistasis . Howeverloci  whereas One possible explanation for why the detected interacting marker pairs were mostly located in different autosomes lies in how the RF algorithm operates and its limitations toward the presence of highly correlated variables in the dataset. The RF importance scores are computed by measuring the prediction error increase when a particular variable is randomly permuted in the OBB data. During the tree-building process, a highly correlated marker is very unlikely to be the best variable to split on the child nodes whereas epistatic SNP pairs appear more frequently as a parent-child node within the same tree , this rer2 metric (loci if selection favors certain allelic combinations (It is noteworthy that interactions between unlinked genes are biologically supported if the involved loci encode components of a metabolic pathway or network, signaling pathway, or transcription factor network . Moreove metric . It is kinations . These ainations .AFF3 gene is a transcription factor that interacts with different zinc finger proteins for the epigenetic regulation of imprinted genes by binding to both differentially DNA-methylated and enhancer regions of mouse embryonic stem cells in an allelic-specific manner (DCAF6, one of the candidate epistatic pairs for AFF3 is also a cofactor that enhances the transcriptional activity of androgen receptors (TBX19 and AFF3 are related to adrenocortical-related dysfunction in humans (Still, many genes flagged by interacting markers are biologically plausible to be involved in epistatic hotspots. For instance, the c manner . The DCAeceptors  while bon humans .The RF approach has been successfully applied for genome-wide scanning in livestock data. For instance, To the best of our knowledge, this was the first attempt of applying a non-parametric approach for scanning potential loci affecting reproductive traits in Nellore cattle using high-density genomic data. The RF-based genome-wide scan and functional analysis highlighted genomic regions spanning candidate genes with key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cells maturation, and pheromone recognition. Complementary analyses revealed that many top-ranked markers in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes. The reported results are expected to enhance the understanding of genetic mechanisms involved in the regulation of AFC in this breed.


ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30–60) and body length (BL), respectively. Furthermore, 13 candidate genes ( These findings have considerably improved our knowledge of the genetic architecture of pig growth traits. However, poor resolution in QTL mapping experiments and the complicated genetic architecture of many QTLs result in an unavoidable challenge for identifying causative mutations [Pork is a popular form of animal protein and is now one of the main sources of human dietary protein. Growth and body size traits, including live backfat thickness (LBT), average daily gain (ADG), body length (BL), body height (BH), back height (BAH), chest circumference (CC), chest depth (CD), and rump circumference (RC), are vital economic traits in the swine industry and have a crucial effect on pig production ,2,3. Amoutations .CACNA1E and ACBD6) associated with BFT and ADG, and Cai et al. [MRAP2, LEPROT, PMAIP1, BMP2, ELFN1, etc.) significantly related to ADG for three Danbred pig breeds, Duroc, Landrace, and Yorkshire. Liu et al. [MAPK4, HMGA1, etc.) associated with body size traits in pigs using GWAS based on Illumina Porcine 80K SNP chips. However, GWAS based on the porcine SNP array could only detect a small number of known SNPs, and the detected markers were not evenly distributed throughout the genome. Furthermore, GWAS based on whole-genome sequencing (WGS) is another genotyping method that has been used in recent years. Nevertheless, GWAS for Sus scrofa (Sscrofa) with large populations based on WGS is still prohibitively expensive at present. Therefore, specific-locus amplified fragment sequencing (SLAF-seq), a reduced representation sequencing technology was developed, which could create large-scale SNP data quickly, reliably, efficiently, and affordably [Genome-wide association study (GWAS) has been increasingly used to identify variants and functional genes associated with traits of interest with high resolution with the development of high-density single nucleotide polymorphism (SNP) arrays and the reduction of high-density SNP analysis costs. GWAS based on the porcine SNP array for growth-related traits of pigs has identified many QTLs and candidate genes in recent years ,19,20,21i et al.  detectedu et al.  found 11fordably . SLAF-sefordably ,29,30,31fordably ,33. Furtfordably .To generate more genetic variation to study the complex genetic structure of important economic traits, including growth traits, a four-way crossbred pig population was established in the current study, in which Landrace, Yorkshire, and Duroc were used as hybrid males and Saba pigs as hybrid females. Western commercial pigs are significantly different from Chinese native pigs in terms of growth performance and physical characteristics. Large White, Landrace, and Duroc with fast growth and a high lean percentage are typical Western commercial breeds, which are widely dispersed throughout the world. However, the Saba pig is a fat-type pig breed, which is an invaluable Chinese genetic resource and is distributed in Yunnan Province, China . Due to In this study, ten growth traits, including live backfat thickness (LBT), average daily gain from 30 to 60 kg (ADG30–60), average daily gain from 60 to 100 kg (ADG60–100), average daily gain from 30 to 100 kg (ADG30–100), body length (BL), body height (BH), back height (BAH), chest circumference (CC), chest depth (CD), and rump circumference (RC), were examined on 223 four-way crossbred pigs raised in the same environmental conditions. GWAS was then conducted based on SLAF-seq technology to identify significant SNPs associated with these traits. To our knowledge, there is the first report about genome-wide studies for growth-related traits in pigs using the SLAF-seq technology. The outcomes offer a foundation for pig breeding and the improvement of growth traits using molecular markers.All of the animals employed in this research were treated and used following the standards established by the Ministry of Agriculture and Rural Affairs of China for the care and use of experimental animals. The ethics committee of Yunnan Agricultural University  approved the entire research.A four-way crossbred pig population was established as described previously . In shorTen growth traits, including LBT, ADG30–60, ADG60–100, ADG30–100, BL, BH, BAH, CC, CD, and RC, were measured by following standard procedures. The growth performance measurement of the test pigs started at about 30 kg and ended at about 100 kg. LBT, ADG30–60, ADG60–100, and ADG30–100 were measured according to the method of “Pig Production Performance Determination Regulations” (NY/T822). Collecting three ADG traits phenotypic data was a very laborious and complicated process. Thus, only 198 out of 223 individuals were phenotyped.All test pigs were measured for body size traits, including BL (from the midpoint of the ears to the base of the tail), BH (from the shoulders to the ground), BAH (from the lowest point of the back to the ground), CC (the circumference around the posterior edge of the scapula), CD , and RC  on the flat ground when the weight of the test pigs reached 105.25 ± 15.75 kg. BL, CC and RC were measured with a tape measure, while BH, BAH and CD were measured using a measuring stick.Proc MEANS in SAS 9.4  was employed to produce descriptive statistics, including the number, minimum, maximum, mean, standard deviation, and coefficient of variation, of ten growth traits for pig accessions. Using the R package “ggpubr”, the sample distribution was represented as a frequency distribution histogram. The R function “PerformanceAnalytics” was used for the phenotypic correlation analysis. The genetic correlations for three ADG traits were estimated using GCTA software .ftp://ftp.ensembl.org/pub/release-102/ (accessed on 21 November 2021)) as the reference genome was used for an electronic digestion prediction experiment. According to the selection principle of the enzyme digestion scheme [Oryza sativa spp. japonica; 374.30 Mb; http://rapdb.DNA.affrc.go.jp/ (accessed on 21 November 2021)) was used to verify the reliability of the experimental process. Ultimately, SLAF-seq for each individual was conducted on an Illumina HiSeq 2500 platform  at Beijing Biomarker Technologies Corporation in Beijing, China.Molecular markers throughout the entire genome were generated as described previously . In shorn scheme , the appThe raw SLAF-seq data were further analyzed using Dual-Index software  to obtaip-value < 1 × 10−5  were regarded as significant associations with the trait of interest. Based on the number of filtered SNPs , the threshold p-value 4.39 × 10−7  and 4.39 × 10−8  were genome-wide 10% and 1% significance levels, respectively. Finally, the manhattan and Quantile-quantile (Q-Q) plots of GWAS were drawn using the R package “qqman”.A total of 227,921 filtered SNPs detected from 223 accessions were used for GWAS. The mixed linear model (MLM) of GEMMA software  was emplsoftware , and Z wsoftware . α and βsoftware  was partwww.ensembl.org (accessed on 15 December 2021)). Afterward, GO annotation of candidate genes was conducted using Gene Ontology Consortium (http://geneontology.org (accessed on 15 December 2021)).Based on the previous references ,45,46, tProc GLM in SAS 9.4  was used to estimate the associations between SNP marker genotypes and growth traits. Additive genetic effects were calculated by comparing the two homozygous genotypes in pairs, and the dominance effects were computed as the deviation of the heterozygote effect from the mean of the two homozygous genotypes.r = 0.70, p < 0.001). The genetic correlations for three ADG traits are shown in r = −0.27).The statistical information on the ten growth traits is shown in RsaI and HaeIII, were chosen as enzyme combinations for the development of SLAF tags in accordance with the selection principle of the enzyme digestion scheme, and the sequence with the length of 314–344 bp was defined as SLAF tags. In total, 1109.92 M paired-end reads were obtained from 223 four-way crossbred pigs. The average value of Q30  and Guanine-cytosine (GC) content were 90.74% (83.17–94.72%) and 44.83% (41.89–49.18%), respectively, demonstrating that our sequencing results for 223 accessions were reliable (Oryza sativa ssp. Japonica) was employed as a control. The results demonstrated that the construction of SLAF libraries was normal because the enzyme digestion efficiency and paired-end comparison efficiency of control data reached 90.77% and 95.4%, respectively.A total of 223 individuals were genotyped and descriptive statistics of the sequence data were presented in our previous study . In shorreliable . Furtherreliable . During In total, 16,997 polymorphic SLAF tags and 10,784,484 SNPs were identified across 233 accessions after genomic mapping and SNP calling. The average value of the number, integrity and heterozygosity ratio of SNPs were 2,216,210 , 20.55% (8.05–42.8%) and 7.51% (6.66–14.39%), respectively . Further10p values of the MLM were fairly close to the expected −log10p values. The results showed that the MLM well controlled the research’s false positives. Q-Q plot of each growth trait was shown following the manhattan plot of the corresponding traits (p < 1.0 × 10−5) for the traits investigated using MLM . The significant SNP on SSC10 was located 29.6 kb upstream of ATP synthase subunit O .In total, five SNPs distributed on SSC1, SSC4, SSC10, and SSC18 were significantly associated with LBT. On SSC4, two adjacent SNPs (rs322460444 and rs332806988) identified were not located in any genes. SNP rs322460444 exceeded the 10% genome-wide significance level (GHRHR) gene. On SSC4, three adjacent SNPs  were associated with ADG30–60. The nearest genes of these three SNPs were corticotropin releasing hormone (CRH) and tripartite motif containing 55 (TRIM55).For ADG30–60, two adjacent SNPs (rs334892514 and rs690227348) on SSC18 were located 1.4 and 1.5 kb, respectively, upstream of the growth hormone-releasing hormone receptor (p = 2.20 × 10−7 and p = 6.29 × 10−8). Among them, the rs331585700 was located 18 kb upstream of eukaryotic translation initiation factor 2 α kinase 1 (EIF2AK1). Additionally, the significant SNP on SSC14 related to ADG60–100 was located within pleckstrin homology domain-containing family A member 1 (PLEKHA1).For ADG60–100, two SNPs (rs331585700 and SSC13:94905576) exceeded the 10% genome-wide significance level (BRAP).For ADG30–100, one significant SNP (rs1111308563) on SSC14 was located within BRCA1 associated protein . Another significant SNP (rs341689410) was located 27.6 kb and 27.8 kb downstream of nudix hydrolase 3 (NUDT3) and high mobility group AT-hook 1 (HMGA1), respectively.For BL, all significant SNPs detected were located on SSC7. Among them, the peak SNP (rs335597506) was located within collagen type XI α 2 chain  gene. Another significant SNP (rs318981132) was located 4.4 kb downstream of tripartite motif containing 39 (TRIM39). Two adjacent SNPs on SSC14 were located with the small G protein signaling modulator 1 (SGSM1) gene.For BAH, two neighboring SNPs (rs711388225 and rs336803962) on SSC7 were located 6.2 kb downstream of the NHL repeat containing the E3 ubiquitin protein ligase 1 .Only one SNP was detected to be significantly associated with CC. The SNP was located 20.7 kb upstream of nuclear factor of activated T-cells 2 (p = 3.09 × 10−7 and p = 3.81 × 10−7). The most significant SNP (rs334022393) on SSC3 was located 26.5 kb downstream of NCK adaptor protein 2 (NCK2), while the significant SNP (rs705385434) on SSC2 was located 2.8 kb upstream of mastermind like transcriptional coactivator 1 (MAML1).For CD, two SNPs (rs334022393 and rs705385434) exceeded the 10% genome-wide significance level (PSD3).For RC, two adjacent SNPs (rs699438879 and rs712077976) on SSC17 were located with the pleckstrin and Sec7 domain containing 3  was searched based on SNP and QTL locations to see if QTLs linked to growth traits in this study repeat any previously known QTLs. A total of 53 SNPs significantly associated with growth traits in a four-way crossbred pig population were identified, of which 18 SNPs were located in previously reported QTL regions in pigs. The remaining 35 SNPs had not been included in any previously reported QTLs associated with pig growth traits. Interestingly, two novel QTLs on SSC4 (68.43–70.14 Mb) and SSC7 (25.22–32.32 Mb) were found to be associated with ADG30–60 and BL, respectively. The results of QTLs comparison are shown in The Pig Quantitative Trait Locus (QTL) Database  with corresponding traits. Genotypes (C–T and A–T) of two SNPs (SSC14:131969638 and SSC14:42805901) presented significant associated (p < 0.05) with corresponding traits. The G, T, and G alleles were favorable for bigger ADG30–60 on rs325760894, rs81382100, and rs320502793, respectively. The T allele was favorable for bigger ADG60–100 on SNP SSC14:131969638. The C and A alleles were favorable for higher BAH on SSC14:42805887 and SSC14:42805901, respectively. The T allele was favorable for bigger CD on rs705385434. The effects of the genotypes and additive and dominance effects are shown in The association between candidate SNPs genotypes and growth traits was estimated. The result revealed that genotypes  of five SNPs, including rs325760894, rs81382100, rs320502793, SSC14:42805887, and rs705385434, presented extremely significant associated , and between ADG60–100 and CC   on SSC7 significantly associated with BL . In seve< 0.001) , respectATP5O. The results of GO annotation revealed that ATP5O participates in ATP biosynthetic process. A study found that ATP5O played a role in the regulation of glucose metabolism in vivo [The study showed that the significant SNP on SSC10 related to LBT was located 29.6 kb upstream of  in vivo .GHRHR gene. GO annotation results indicated that GHRHR is mainly involved in positive regulation of multicellular organism growth and positive regulation of growth hormone secretion. The biological process is that growth hormone-releasing hormone (GHRH) stimulates pituitary growth hormone (GH) synthesis and secretion as well as somatotroph proliferation in mammals, which is mediated by GHRHR [GHRH and GHRHR in other mammals also influenced growth and development [GHRHR regulates the growth traits of Texel lambs [GHRHR regulated skeletal muscle growth and development by mediating synthesis and secretion of GH and should be considered a strong candidate gene for porcine ADG30–60.Two adjacent SNPs on SSC18 associated with ADG30–60 were located 1.4 and 1.5 kb, respectively, upstream of the by GHRHR ,50,51,52by GHRHR  and stimby GHRHR . Previouby GHRHR ,56,57. Selopment . Armstroel lambs . Thus, Gp < 0.01) with ADG30–60. The ADG30–60 of GG, TT and GG genotypes were 52~80 g more than GT, CT and AG genotypes, and 139~152 g more than TT, CC and AA genotypes . This unique subclass of RING Finger-B box-coiled-coiled (RBCC)/tripartite motif (TRIM) proteins contain two coiled-coil dimerization motif boxes, a zinc-binding B-box motif, and a highly conserved N-terminal RING domain [TRIM55 resulted in delayed myoblast fusion and myofibrillogenesis and affected contractile activity. Zhang et al. [TRIM55 is a promising candidate gene for traits related to skeletal muscle development since it is developmentally regulated and likely has a significant function during the embryonic and early postnatal skeletal development phases. Given that TRIM55 is functionally related to skeletal muscle development, it might be a leading candidate gene for the locus.Furthermore, the GWAS result showed that three significant SNPs on SSC4 were associated with ADG30–60. Genotypes  of these three SNPs presented extremely significant association  [PLEKHA1. Genotypes (C–T) of the SNP presented significant association (p < 0.05) with ADG60–100. The ADG60–100 of TT Genotype was 226 g and 134 g more than of CC and CT, respectively  was located 18 kb upstream of the feration . Gong etex (BMI) . In addiectively . GO annomulation . Kim et ure pigs . Additiooporosis ,70. By c0 was loctabolism . CollectBRAP gene. GO annotation results showed that BRAP participates in MAPK cascade and ras protein signal transduction  was located with the sduction . It is wCOL11A2. Zhou et al. [COL11A2 was significantly associated with body shape of Large Yellow Croaker. Some research showed that the COL11A2 gene was related to the skeletal system morphogenesis and growth of cartilage [COL11A2 gene responsible for skeletal system morphogenesis and body growth was differentially expressed in adipose tissue between Jeju native pigs and Berkshire [COL11A2 mutant mice is 25% lower than that of wild-type counterparts [COL11A2 was related to the body height of the Chongming white goat. Therefore, the COL11A2 gene should be regarded as a strong candidate gene for BL. Furthermore, another significant SNP (rs341689410) on SSC7 for BL was located 27.6 kb and 27.8 kb downstream of NUDT3 and HMGA1, respectively. The NUDT3 gene participates in the catabolism of diadenosine polyphosphate, and variations in this gene have been linked to human BMI [HMGA1 gene is one member of the high mobility group A family. The fact that HMGA1/HMGA2 double knock-out mice are smaller than HMGA1 knock-out mice suggests that HMGA1 can influence the body size of animals [HMGA1 could operate as a glucose disposal mediator by controlling the activity of insulin-like growth factor 1 [HMGA1 and pig body size traits [HMGA1 was a candidate gene for several body size traits of Yorkshire pigs [HMGA1 was expressed in pig limb cells and had an impact on chondrocyte proliferation and differentiation, so HMGA1 was considered a candidate gene for limb bone length in a Large White × Minzhu intercross population. Given the functional significance of HMGA1 and the numerous studies that have demonstrated how strongly it was correlated with body size features, HMGA1 should be regarded as a strong candidate gene for pig body size traits, including BL.For BL, the peak SNP rs335597506) on SSC7 was located within u et al.  found thartilage ,79. Anoterkshire . Moreoveterparts . Gao et terparts  found thuman BMI  and humauman BMI . The HMG animals . A studyfactor 1 . It was factor 1 . Furthere traits . Through35597506  SNP rs3489410 on ire pigs . Accordiire pigs , HMGA1 wNHLRC1 gene. This gene encodes the malin protein containing a zinc-binding RING finger motif with E3-ubiquitin ligase activity [NHLRC1 gene was related to the body height of Sahiwal cattle [TRIM39, which is also a member of the RBCC/TRIM subfamily of zinc finger proteins that participated in a variety of biological processes, including cell differentiation [TRIM39 gene was significantly associated with body weight at 28 days of age in broilers [p < 0.01) and significant association (p < 0.05) with BAH, respectively. Among them, the BAH of CC genotype of SSC14:42805887 was 2.21 cm and 5.75 cm higher than that of CT and TT, respectively, and the BAH of AA genotype of SSC14:42805901 was 1.99 cm and 6.73 cm higher than that of AT and TT, respectively. The two adjacent SNPs were located within the SGSM1 gene, which participates in regulating cell circle, proliferation and differentiation [SGSM1 gene was significantly related to the chest circumference of Chinese Sushan pigs [For BAH, two neighboring SNPs on SSC7 were located 6.2 kb downstream of the activity ,90, whicactivity . Two stul cattle  and Chinl cattle . Anotherntiation . One stubroilers . In addintiation . Accordihan pigs .NFATC2. It was discovered that the NFATC2 gene contributed to the growth development of skeletal muscle via the PGF2 receptor [NFATC2 regulated myoblast fusion, which was extremely important for further cell growth [NFATC2 was significantly associated with birth weight and body weight at the 8th week of age (weaning weight) of crossbred pigs [NFATC2 gene could be deemed as a potential candidate gene for CC.Only one SNP was significantly associated with CC. The location of the SNP was 20.7 kb upstream of receptor . Myoblasl growth . A studyred pigs . Thus, tNCK2 gene. The previous work identified NCK2 as a new regulator of adiposity and suggested that NCK2 was crucial for preventing white adipose tissue expansion and dysfunction in mice and humans [NCK2 gene has been located in four QTLs for fat thickness, marbling score, yearling and mature body weight [p < 0.01) with CD. The CD of GG genotype was 2.23 cm and 6.94 cm less than that of GT and TT, respectively. The SNP was located 2.8 kb upstream of MAML1. GO annotation results showed that MAML1 participates in positive regulation of myotube differentiation and myoblast differentiation  were measured. Genetic correlations between ADG30–60 and ADG30–100, and between ADG60–100 and ADG30–100 were more than 0.87, while the genetic correlation between ADG30–60 and ADG60–100 was −0.27 , which iGHRHR and TRIM55 detected were candidate genes for ADG30–60, which were closely related to the growth and development of skeletal muscle. However, EIF2AK1 and PLEKHA1 detected were candidate genes for ADG60–100, which were associated with body mass index and post-embryonic development and skeletal system morphogenesis, respectively. Furthermore, GWAS results showed that no identical significant SNP was identified for the two traits. Ten SNPs distributed in SSC4, SSC8, and SSC18 were significantly associated with ADG30–60, while seven SNPs distributed in SSC1, SSC2, SSC3, SSC13, SSC14, and SSC17 were significantly related to ADG30–60. Collectively, the growth of pigs in the two periods might be regulated by different genomic regions and different functional genes.In the present study, the average of ADG30–60 (448 g) was 108 g less than ADG60–100 (556 g), indicating that the average daily gain of pigs was more in the 60–100 kg stage. Thus, the rapid fat deposition of pigs in the 60–100 kg stage might lead to bigger daily gain. Through gene mapping and functional annotation, ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were identified as being associated with growth traits of pigs. The growth of pigs in the 30–60 kg and 60–100 kg stages might be regulated by different genomic regions and different functional genes. Overall, our study provided new evidence that multiple genes were involved in regulating growth traits in pigs. These SNPs and corresponding candidate genes served as a biological foundation for improving growth and production performance in swine breeding.In conclusion, we conducted a GWAS based on SLAF-seq for ten growth traits in 223 four-way crossbred pigs using MLM. A total of 53 significant SNPs, two novel QTLs on SSC4 and SSC7, and 13 candidate genes (