Single-step Genome-wide Association Study Research Articles

Identifying candidate genes associated with sperm morphology abnormalities using weighted single-step GWAS in a Duroc boar population

Artificial insemination (AI) has been used as a routine technology globally in the pig production industry since 1930. One of the preferable advantages of AI technology is that the semen of elite boars can be disseminated to the commercial sow population rapidly. Understanding the genetic background of semen traits may help in developing genetic improvement programs of boars by including these traits into the selection index. In this study, we utilized weighted single-step genome-wide association study (wssGWAS) to identify genetic regions and further candidate genes associated with sperm morphology abnormalities (proximal droplet, distal droplet, bent tail, coiled tail, and distal midpiece reflex) in a Duroc boar population.Several genomic regions explained 2.76%–9.22% of the genetic variances for sperm morphology abnormalities were identified. The first three detected QTL regions together explained about 7.65%–25.10% of the total genetic variances of the studied traits. Several genes were detected and considered as candidate genes for each of the traits under study: coiled tail, HOOK1, ARSA, SYCE3, SOD3, GMNN, RBPJ, STIL, and FGF1; bent tail, FGF1, ADIPOR1, ARPC5, FGFR3, PANX1, IZUMO1R, ANKRD49, and GAL; proximal droplet, NSF, WNT3, WNT9B, LYZL6, FGFR1OP, RNASET2, FYN, LRRC6, EPC1, DICER1, FNDC3A, and PFN1; distal droplet, ARSA, SYCE3, MOV10L1, CBR1, KDM6B, TP53, PTBP2, UBR7, KIF18A, ADAM15, FAAH, TEKT3, and SRD5A1; and distal midpiece reflex, OMA1, PFN1, PELP1, BMP2, GPR18, TM9SF2, and SPIN1. GO and KEGG enrichment analysis revealed the potential function of the identified candidate genes in spermatogenesis, testis functioning, and boar spermatozoa plasma membrane activating and maintenance.In conclusion, we detected candidate genes associated with the coiled tail, bent tail, proximal droplet, distal droplet, and distal midpiece reflex in a Duroc boar population using wssGWAS. Overall, these novel results reflect the polygenic genetic architecture of the studied sperm morphology abnormality traits, which may provide knowledge for conducting genomic selection on these traits. The detected genetic regions can be used in developing trait-specific marker assisted selection models by assigning higher genetic variances to these regions.

Genome-wide association study for milk production traits in a Brazilian Holstein population

Advances in the molecular area of selection have expanded knowledge of the genetic architecture of complex traits through genome-wide association studies (GWAS). Several GWAS have been performed so far, but confirming these results is not always possible due to several factors, including environmental conditions. Thus, our objective was to identify genomic regions associated with traditional milk production traits, including milk yield, somatic cell score, fat, protein and lactose percentages, and fatty acid composition in a Holstein cattle population producing under tropical conditions. For this, 75,228 phenotypic records from 5,981 cows and genotypic data of 56,256 SNP from 1,067 cows were used in a weighted single-step GWAS. A total of 46 windows of 10 SNP explaining more than 1% of the genetic variance across 10 Bos taurus autosomes (BTA) harbored well-known and novel genes. The MGST1 (BTA5), ABCG2 (BTA6), DGAT1 (BTA14), and PAEP (BTA11) genes were confirmed within some of the regions identified in our study. Potential novel genes involved in tissue damage and repair of the mammary gland (COL18A1), immune response (LTTC19), glucose homeostasis (SLC37A1), synthesis of unsaturated fatty acids (LTBP1), and sugar transport (SLC37A1 and MFSD4A) were found for milk yield, somatic cell score, fat percentage, and fatty acid composition. Our findings may assist genomic selection by using these regions to design a customized SNP array to improve milk production traits on farms with similar environmental conditions.

Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. The objectives of this study were to study G × E interactions for female fertility traits in the Danish Holstein dairy cattle population using a reaction norm model (RNM), and to detect the particular genomic regions contributing to the performance of these traits and the G × E interactions. In total 4534 bulls were genotyped by an Illumina BovineSNP50 BeadChip. An RNM with a pedigree-based relationship matrix and a pedigree-genomic combined relationship matrix was used to explore the existence of G × E interactions. In the RNM, the environmental gradient (EG) was defined as herd effect. Further, the genomic regions affecting interval from calving to first insemination (ICF) and interval from first to last insemination (IFL) were detected using single-step genome-wide association study (ssGWAS). The genetic correlations between extreme EGs indicated that G × E interactions were sizable for ICF and IFL. The genomic RNM (pedigree-genomic combined relationship matrix) had higher prediction accuracy than the conventional RNM (pedigree-based relationship matrix). The top genomic regions affecting the slope of the reaction norm included immunity-related genes (IL17, IL17F and LIF), and growth-related genes (MC4R and LEP), while the top regions influencing the intercept of the reaction norm included fertility-related genes such as EREG, AREG and SMAD4. In conclusion, our findings validated the G × E interactions for fertility traits across different herds and were helpful in understanding the genetic background of G × E interactions for these traits.

Use of single-step genome-wide association studies for prospecting genomic regions related to milk production and milk quality of buffalo.

The aim of this research communication was to identify chromosome regions and genes that could be related to milk yield (MY), milk fat (%F) and protein percentage (%P) in Brazilian buffalo cows using information from genotyped and non-genotyped animals. We used the 90 K Axiom® Buffalo Genotyping array. A repeatability model was used. An iterative process was performed to calculate the weights of markers as a function of the squared effects of Single Nucleotide Polymorphism (SNP) and allele frequencies. The 10 SNPs with the largest effects for MY, %F and %P were studied and they explained 7·48, 9·94 and 6·56% of the genetic variance, respectively. These regions harbor genes with biological functions that could be related to the traits analyzed. The identification of such regions and genes will contribute to a better understanding of their influence on milk production and milk quality traits of buffaloes.

Identifying SNPs and candidate genes for three litter traits using single-step GWAS across six parities in Landrace and Large White pigs

Total number born (TNB), number born alive (NBA), and litter weight born alive (LWB) are critically important traits in pig production. The sow's parity is one of the major factors influencing litter traits. Because of monogenic or polygenic contributions and the presence of temporal gene effects in different sows' parities, it is difficult to clarify the biological and genetic background. To systematically explore the genetic mechanism of litter traits, we conducted 18 GWASs using single-step GWAS (ssGWAS) based on two breeds (908 Landrace and 1,130 Large White sow litter records) for each litter trait in different parities. A total of 300 Landrace and 300 Large White sows were genotyped by sequencing (GBS). ssGWAS was performed separately for each breed and each parity due to population stratification and temporal gene effect. In summary, we identified 80 (15 for Landrace and 65 for Large White), 227 (52 for Landrace, 175 for Large White), and 187 (34 for Landrace, 153 for Large White) single nucleotide polymorphisms (SNPs) affecting TNB, NBA, and LWB, respectively. Of them, we suggest that a total of 22 loci (SSC1: 125098202, SSC1: 117560058, SSC14: 147794697, SSC8: 84823302, SSC9: 143554876, and SSC9: 138766097 for Landrace; SSC1: 4023577, SSC1: 3859573, SSC1: 4891063, SSC16: 5197665, SSC10: 32050819, SSC13: 13552924, SSC13: 92819, SSC17: 3579607, SSC13: 196698221, SSC7: 30918403, SSC16: 46221484, SSC16: 46169204, SSC2: 41988642, SSC2: 44475457, SSC2: 42521875, and SSC7: 58411951 for Large White) are shared by TNB, NBA, and LWB. These results indicate the existence of gene temporal effect in each parity. Furthermore, our findings suggest four interesting candidate genes (FBXL7, ALDH1A2, LEPR, and DDX1) associated with litter traits in different parities that have a major effect on embryonic development progression. In conclusion, 22 crucial SNPs and four interesting candidate genes were identified for three litter traits across six parities. These findings advance our understanding of the genetic architecture of litter traits and confirm the presence of temporal gene effects in different parities. Importantly, functional validation studies for findings of particular interest are recommended in litter traits.

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs

BackgroundIn recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines.ResultsWe identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway.ConclusionsWe identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.

The identification of novel regions for reproduction trait in Landrace and Large White pigs using a single step genome-wide association study.

ObjectiveThe purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs.MethodsThe traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records.ResultsThe outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL.ConclusionThe genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations.

Single step genome-wide association studies based on genotyping by sequence data reveals novel loci for the litter traits of domestic pigs

In this study, data genotyping by sequence (GBS) was used to perform single step GWAS (ssGWAS) to identify SNPs associated with the litter traits in domestic pigs and search for candidate genes in the region of significant SNPs. After quality control, 167,355 high-quality SNPs from 532 pigs were obtained. Phenotypic traits on 2112 gilt litters from 532 pigs were recorded including total number born (TNB), number born alive (NBA), and litter weight born alive (LWB). A single-step genomic BLUP approach (ssGBLUP) was used to implement the genome-wide association analysis at a 5% genome-wide significance level. A total of 8, 23 and 20 significant SNPs were associated with TNB, NBA, and LWB, respectively, and these significant SNPs accounted for 62.78%, 79.75%, and 58.79% of genetic variance. Furthermore, 1 (SSC14: 16314857), 4 (SSC1: 81986236, SSC1: 66599775, SSC1: 161999013, and SSC1: 267883107), and 5 (SSC9: 29030061, SSC2: 32368561, SSC5: 110375350, SSC13: 45619882 and SSC13: 45647829) significant SNPs for TNB, NBA, and LWB were inferred to be novel loci. At SSC1, the AIM1 and FOXO3 genes were found to be associated with NBA; these genes increase ovarian reproductive capacity and follicle number and decrease gonadotropin levels. The genes SLC36A4 and INTU are involved in cell growth, cytogenesis and development were found to be associated with LWB. These significant SNPs can be used as an indication for regions in the Sus scrofa genome for variability in litter traits, but further studies are expected to confirm causative mutations.

Identification of genomic regions related to tenderness in Nellore beef cattle

The aim of this study was to identify genomic regions that associated with beef tenderness in Nellore cattle. Phenotypes were obtained according to the standard USDA Quality Grade (1999). Data from 909 genotyped Nellore bulls were used in the Genome-Wide Association Study (GWAS) undertaken using a single-step approach including also a pedigree file composed of 6276 animals. The analyses were performed using the Blupf90 software, estimating the effect of genomic windows of 10 consecutive markers. The GWAS results identified 18 genomic regions located on 14 different chromosomes (1, 4, 6, 7, 8, 10, 18, 19, 20, 21, 22, 25, 26 and 29), which explained more than 1% of the total additive genetic variance; several candidate genes were located in these regions including SLC2A9, FRAS1, ANXA3, FAM219A, DNAI, AVEN, SHISA7, UBE2S, CDC42EP5, CNTN3, C16orf96, UBALD1, MGRN1 and SNORA1 With the single-step GWAS, it was possible to identify regions and genes related to meat tenderness in Nellore beef cattle.

Abstract 3636: Systems biology approach links genetic, epigenetic, and transcriptomic features to tamoxifen sensitivity.

Abstract Utilizing existing genome-wide genotype, transcriptome and microRNA (miRNA) datasets, we developed an integrative approach to evaluate the role of genetic-epigenetic regulatory networks in human complex traits. In this study, we focused on cellular sensitivity to tamoxifen, a commonly prescribed breast cancer medication as a phenotype of interest. A sequential multi-dimensional model was constructed to first identify genes/miRNAs whose expression levels are correlated with tamoxifen sensitivity. This in turn facilitated the identification of SNPs associated with potential functionally relevant genomic features. Finally, associations between these selected SNPs and cellular sensitivity to tamoxifen were evaluated. A permutation-based false discovery rate (FDR) procedure showed that association findings from our approach have much lower FDRs when compared to those obtained through a traditional single-step genome-wide association study (GWAS) between SNP and drug sensitivity. Our model identified 50 unique SNPs associated with 30 miRNAs and 34 gene expression levels, all of which associated with cellular sensitivity to tamoxifen phenotypes in HapMap YRI samples. Among them, several miRNAs and genes have been previously implicated to play a role in tamoxifen sensitivity; most are novel. Functional studies on select miRNAs/genes are currently underway in both the HapMap samples and breast cancer cell lines. Citation Format: Eric R. Gamazon, Liming Weng, Hae Kyung Im, Xiaotong Zhang, Dana M. Ziliak, R. Stephanie Huang. Systems biology approach links genetic, epigenetic, and transcriptomic features to tamoxifen sensitivity. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3636. doi:10.1158/1538-7445.AM2013-3636