Dataset Information

Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy.

ABSTRACT:

Background

Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels.

Results

We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks.

Conclusions

Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

SUBMITTER: Yang R

PROVIDER: S-EPMC8290195 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Xiang pig (XP) is one of the best-known indigenous pig breeds in China, which is characterized by its small body size, strong disease resistance, high adaptability, favorite meat quality, small litter sizes, and early sexual maturity. However, the genomic evidence that links these unique traits of XP is still poorly understood. To identify the genomic signatures of selection in XP, we performed whole-genome resequencing on 25 unrelated individual XPs. We obtained 876.70 Gb of raw data from the genomic libraries. The LD analysis showed that the lowest level of linkage disequilibrium was observed in Xiang pig. Comparative genomic analysis between XPs and other breeds including Tibetan, Meishan, Duroc and Landrace revealed 3062, 1228, 907 and 1519 selected regions, respectively. The genes identified in selected regions of XPs were associated with growth and development processes (IGF1R, PROP1, TBX19, STAC3, RLF, SELENOM, MSTN), immunity and disease resistance (ZCCHC2, SERPINB2, ADGRE5, CYP7B1, STAT6, IL2, CD80, RHBDD3, PIK3IP1), environmental adaptation (NR2E1, SERPINB8, SERPINB10, SLC26A7, MYO1A, SDR9C7, UVSSA, EXPH5, VEGFC, PDE1A), reproduction (CCNB2, TRPM6, EYA3, CYP7B1, LIMK2, RSPO1, ADAM32, SPAG16), meat quality traits (DECR1, EWSR1), and early sexual maturity (TAC3). Through the absolute allele frequency difference (ΔAF) analysis, we explored two population-specific missense mutations occurred in NR6A1 and LTBP2 genes, which well explained that the vertebrae numbers of Xiang pigs were less than that of the European pig breeds. Our results indicated that Xiang pigs were less affected by artificial selection than the European and Meishan pig breeds. The selected candidate genes were mainly involved in growth and development, disease resistance, reproduction, meat quality, and early sexual maturity. This study provided a list of functional candidate genes, as well as a number of genetic variants, which would provide insight into the molecular basis for the unique traits of Xiang pig.

Project description:Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs.

Project description:Henan Province is located in central China and rich in domestic pig populations; Huainan (HN) pigs are one of three Henan indigenous breeds with great performance, including early maturation, strong disease resistance and high meat quality. Yunan (YN) black pigs are a typical, newly cultivated breed, synthesized between HN pigs and American Duroc, and are subjected to selection for important traits, such as fast growth and excellent meat quality. However, the genomic differences, selection signatures and loci associated with important economic traits in YN black pigs and HN pigs are still not well understood. In this study, based on high-density SNP chip analysis of 159 samples covering commercial DLY (Duroc × Landrace × Large White) pigs, HN pigs and YN black pigs, we performed a comprehensive analysis of phylogenetic relationships and genetic diversity among the three breeds. Furthermore, we used composite likelihood ratio tests (CLR) and F-statistics (Fst) to identify specific signatures of selection associated with important economic traits and potential candidate genes. We found 147 selected regions (top 1%) harboring 90 genes based on genetic differentiation (Fst) in the YN-DLY group. In the HN-DLY group, 169 selected regions harbored 58 genes. In the YN-HN group, 179 selected regions harbored 77 genes. In addition, the QTLs database with the most overlapping regions was associated with triglyceride level, number of mummified pigs, hemoglobin and loin muscle depth for YN black pigs, litter size and intramuscular fat content for HN pigs, and humerus length, linolenic acid content and feed conversion ratio mainly in DLY pigs. Of note, overlapping 14 tissue-specific promoters' annotation with the top Fst 1% selective regions systematically demonstrated the muscle-specific and hypothalamus-specific regulatory elements in YN black pigs. Taken together, these results contribute to an accurate knowledge of crossbreeding, thus benefitting the evaluation of production performance and improving the genome-assisted breeding of other important indigenous pig in the future.

Project description:Patients with inherited retinal dystrophies (IRDs) were recruited from two understudied populations: Mexico and Pakistan as well as a third well-studied population of European Americans to define the genetic architecture of IRD by performing whole-genome sequencing (WGS). Whole-genome analysis was performed on 409 individuals from 108 unrelated pedigrees with IRDs. All patients underwent an ophthalmic evaluation to establish the retinal phenotype. Although the 108 pedigrees in this study had previously been examined for mutations in known IRD genes using a wide range of methodologies including targeted gene(s) or mutation(s) screening, linkage analysis and exome sequencing, the gene mutations responsible for IRD in these 108 pedigrees were not determined. WGS was performed on these pedigrees using Illumina X10 at a minimum of 30X depth. The sequence reads were mapped against hg19 followed by variant calling using GATK. The genome variants were annotated using SnpEff, PolyPhen2, and CADD score; the structural variants (SVs) were called using GenomeSTRiP and LUMPY. We identified potential causative sequence alterations in 61 pedigrees (57%), including 39 novel and 54 reported variants in IRD genes. For 57 of these pedigrees the observed genotype was consistent with the initial clinical diagnosis, the remaining 4 had the clinical diagnosis reclassified based on our findings. In seven pedigrees (12%) we observed atypical causal variants, i.e. unexpected genotype(s), including 4 pedigrees with causal variants in more than one IRD gene within all affected family members, one pedigree with intrafamilial genetic heterogeneity (different affected family members carrying causal variants in different IRD genes), one pedigree carrying a dominant causative variant present in pseudo-recessive form due to consanguinity and one pedigree with a de-novo variant in the affected family member. Combined atypical and large structural variants contributed to about 20% of cases. Among the novel mutations, 75% were detected in Mexican and 50% found in European American pedigrees and have not been reported in any other population while only 20% were detected in Pakistani pedigrees and were not previously reported. The remaining novel IRD causative variants were listed in gnomAD but were found to be very rare and population specific. Mutations in known IRD associated genes contributed to pathology in 63% Mexican, 60% Pakistani and 45% European American pedigrees analyzed. Overall, contribution of known IRD gene variants to disease pathology in these three populations was similar to that observed in other populations worldwide. This study revealed a spectrum of mutations contributing to IRD in three populations, identified a large proportion of novel potentially causative variants that are specific to the corresponding population or not reported in gnomAD and shed light on the genetic architecture of IRD in these diverse global populations.