The related SNPs and genes to body size using GWAS- latent variable modeling in dromedaries
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina HiSeq 2000
ORGANISM(S): Camelus Dromedarius
SUBMITTER: EBI
PROVIDER: PRJEB82242 | EVA | 2024-11-09
REPOSITORIES: EVA
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Project description:Body size is an important indicator of growth and health in sheep. In the present study, we performed Genome-Wide Association Studies (GWAS) to detect significant single-nucleotide polymorphisms (SNPs) associated with Hu sheep's body size. After genotyping parental (G1) and offspring (G2) generation of the nucleus herd for meat production of Hu sheep and conducting GWAS on the body height, chest circumference, body length, tail length, and tail width of the two groups, 5 SNPs associated with body height and 4 SNPs correlated with chest circumference were identified at the chromosomal significance level. No SNPs were significantly correlated to body length, tail length, and width. Four out of the 9 SNPs were found to be located within the 4 genes. KITLG and CADM2 are considered as candidate functional genes related to body height; MCTP1 and COL4A6 are candidate functional genes related to chest circumference. The 9 SNPs found in GWAS were verified using the G3 generation of the nucleus herd for meat production. Nine products were amplified around the 9 sites, and 29 SNPs were found; 3 mutation sites, G > C mutation at 134 bp downstream of s554331, T > G mutation at 19 bp upstream of s26859.1, and A > G mutation at 81 bp downstream of s26859.1, were significantly correlated to the body height. Dual-luciferase reporter gene experiments showed that the 3 SNPs could significantly impact dual-luciferase and gene transcription activity.
| S-EPMC8173124 | biostudies-literature
Project description:BackgroundCetaceans exhibit an exceptionally wide range of body size, yet in this regard, their genetic basis remains poorly explored. In this study, 20 body-size-related genes for which duplication, mutation, or deficiency can cause body size change in mammals were chosen to preliminarily investigate the evolutionary mechanisms underlying the dramatic body size variation in cetaceans.ResultsWe successfully sequenced 20 body-size-related genes in six representative species of cetaceans. A total of 46 codons from 10 genes were detected and determined to be under strong positive selection, 32 (69.6%) of which were further found to be under radical physiochemical changes; moreover, some of these sites were localized in or near important functional regions. Interestingly, positively selected genes were well matched with body size evolution: for small cetaceans, strong evidence of positive selection was detected at ACAN, OBSL1, and GRB10, within which mutations or duplications could cause short stature; positive selection was found in large cetaceans at CBS and EIF2AK3, which could promote growth, and at the PLOD1 gene, within which mutations could cause tall stature. Importantly, relationship analyses revealed that the evolutionary rate of CBS was positively related to body length and body mass with statistical significance. Additionally, we identified 32 cetacean-specific amino acid changes in 10 genes.ConclusionsThis is the first study to investigate the molecular basis of dramatic body size variation in cetaceans. Our results provide evidence of the positive selection of several body-size-related genes in cetaceans, as well as divergent selection between large or small cetaceans, which suggest cetacean body size variation possibly associated with these genes. In addition, cetacean-specific amino acid changes might have played key roles in body size evolution after the divergence of cetaceans from their terrestrial relatives. Overall, the evolutionary pattern of these body-size-related genes could provide new insights into genetic mechanisms for the body size variation in cetaceans.
| S-EPMC6660953 | biostudies-literature
Project description:This study aimed to identify the genes related to the body size of pigs by conducting genome-wide selection analysis (GWSA). We performed a GWSA scan on 50 pigs belonging to four small-bodied pig populations (Diannan small-eared pig, Bama Xiang pig, Wuzhishan pig, and Jeju black pig from South Korea) and 124 large-bodied pigs. We used the genetic parameters of the pairwise fixation index (FST) and π ratio (case/control) to screen candidate genome regions and genes related to body size. The results revealed 47,339,509 high-quality SNPs obtained from 174 individuals, while 280 interacting candidate regions were obtained from the top 1% signal windows of both parameters, along with 187 genes (e.g., ADCK4, AMDHD2, ASPN, ASS1, and ATP6V0C). The results of the candidate gene (CG) annotation showed that a series of CGs (e.g., MSTN, LTBP4, PDPK1, PKMYT1, ASS1, and STAT6) was enriched into the gene ontology terms. Moreover, molecular pathways, such as the PI3K-Akt, HIF-1, and AMPK signaling pathways, were verified to be related to body development. Overall, we identified a series of key genes that may be closely related to the body size of pigs, further elucidating the heredity basis of body shape determination in pigs and providing a theoretical reference for molecular breeding.
| S-EPMC9312078 | biostudies-literature
Project description:BackgroundBody size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes.ResultsThe 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC.ConclusionsThe results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations.
| S-EPMC5408376 | biostudies-literature
Project description:Porcine 60K BeadChip genotyping arrays (Illumina) are increasingly being applied in pig genomics to validate SNPs identified by re-sequencing or assembly-versus-assembly method. Here we report that more than 98% SNPs identified from the porcine 60K BeadChip genotyping array (Illumina) were consistent with the SNPs identified from the assembly-based method. This result demonstrates that whole-genome de novo assembly is a reliable approach to deriving accurate maps of SNPs.
2016-07-05 | GSE83910 | GEO
Project description:IntroductionThis paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.ResultsThe suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.ConclusionsThe method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.
| S-EPMC4243330 | biostudies-literature
Project description:Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array (GSA)) on 120 DNA samples derived from African and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave- one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data, and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼ 0.5× and higher compared to the Illumina GSA.
2021-01-31 | GSE165845 | GEO
Project description:BACKGROUND: Numerous single nucleotide polymorphisms (SNPs) associated with complex diseases have been identified by genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) studies. However, few of these SNPs have explicit biological functions. Recent studies indicated that the SNPs within the 3'UTR regions of susceptibility genes could affect complex traits/diseases by affecting the function of miRNAs. These 3'UTR SNPs are functional candidates and therefore of interest to GWAS and eQTL researchers. DESCRIPTION: We developed a publicly available online database, MirSNP (http://cmbi.bjmu.edu.cn/mirsnp), which is a collection of human SNPs in predicted miRNA-mRNA binding sites. We identified 414,510 SNPs that might affect miRNA-mRNA binding. Annotations were added to these SNPs to predict whether a SNP within the target site would decrease/break or enhance/create an miRNA-mRNA binding site. By applying MirSNP database to three brain eQTL data sets, we identified four unreported SNPs (rs3087822, rs13042, rs1058381, and rs1058398), which might affect miRNA binding and thus affect the expression of their host genes in the brain. We also applied the MirSNP database to our GWAS for schizophrenia: seven predicted miRNA-related SNPs (p?<?0.0001) were found in the schizophrenia GWAS. Our findings identified the possible functions of these SNP loci, and provide the basis for subsequent functional research. CONCLUSION: MirSNP could identify the putative miRNA-related SNPs from GWAS and eQTLs researches and provide the direction for subsequent functional researches.
| S-EPMC3582533 | biostudies-literature
Project description:Body size is one of the most economically important traits of dairy cattle, as it is significantly associated with cow longevity, production, health, fertility, and environmental adaptation. The identification and application of genetic variants using a novel genetic approach, such as genome-wide association studies (GWASs), may give more insights into the genetic architecture of complex traits. The identification of genes, single nucleotide polymorphisms (SNPs), and pathways associated with the body size traits may offer a contribution to genomic selection and long-term planning for selection in dairy cows. In this study, we performed GWAS analysis to identify the genetic markers and genes associated with four body size traits (body height, body depth, chest width, and angularity) in 1000 Chinese Holstein cows. We performed SNPs genotyping in 1000 individuals, based on the GeneSeek Genomic Profiler Bovine 100 K. In total, we identified 11 significant SNPs in association with body size traits at the threshold of Bonferroni correction (5.90 × 10-7) using the fixed and random model circulating probability unification (FarmCPU) model. Several genes within 200 kb distances (upstream or downstream) of the significant SNPs were identified as candidate genes, including MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L. Moreover, genes within 200 kb of the identified SNPs were significantly enriched (p ≤ 0.05) in 25 Gene Ontology terms and five Kyoto Encyclopedia of Genes and Genomes pathways. We anticipate that these results provide a foundation for understanding the genetic architecture of body size traits. They will also contribute to breeding programs and genomic selection work on Chinese Holstein cattle.
| S-EPMC10044097 | biostudies-literature
Project description:BackgroundLong non-coding RNA (lncRNA) plays an essential role in hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC) occurrence and development. Single nucleotide polymorphism (SNP) may affect HBV-related HCC susceptibility by altering the function of lncRNA. However, the relationship between lncRNA SNPs and HBV-related HCC occurrence and development is still unclear.MethodsIn the present study, based on HBV-related HCC genome-wide association studies, eight potentially functional SNPs from two lncRNAs were predicted using a set of bioinformatics strategies. In 643 HBV-related HCC patients, 549 CHB carriers, and 553 HBV natural clearance subjects from Southern Chinese, we evaluated associations between SNPs and HBV-related HCC occurrence or development with odds ratio (OR) and 95% confidence interval (CI) under credible genetic models.ResultsIn HBV-related HCC patients, rs9908998 was found to significantly increase the risk of lymphatic metastasis under recessive model (Adjusted OR = 1.95, 95% CI = 1.20-3.17). Lnc-RP11-150O12.3 rs2275959, rs1008547, and rs11776545 with cancer family history may show significant multiplicative and additive interactions on HBV-related HCC susceptibility (all pAdjusted < .05). The associations of rs2275959, rs1008547, and rs11776545 with distant metastasis of HBV-related HCC patients were observed in additive model (Adjusted OR = 1.45, 95% CI = 1.06-1.97 for rs2275959; Adjusted OR = 1.45, 95% CI = 1.06-1.98 for rs1008547; Adjusted OR = 1.40, 95% CI = 1.03-1.91 for rs11776545).ConclusionTaken together, lnc-ACACA-1 rs9908998, lnc-RP11-150O12.3 rs2275959, rs1008547, and rs11776545 might be predictors for HBV-related HCC risk or prognosis.
| S-EPMC8077159 | biostudies-literature