Genome-Wide Association Study-Based Identification of SNPs and Haplotypes Associated With Goose Reproductive Performance and Egg Quality.
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ABSTRACT:
SUBMITTER: Gao G
PROVIDER: S-EPMC7994508 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The lion head goose is one of the most important agricultural resources in China; however, its breeding process is relatively slow. In the present study, a genome-wide association study was performed for the genetic selection of egg production characters in lion head geese. We detected 30 single-nucleotide polymorphisms located in or near 30 genes that might be associated with egg production character, and quantitative real-time polymerase chain reaction was used to verify their expression level in lion head geese. The results showed that the expression levels of CRTC1 (encoding CREB-regulated transcription coactivator 1), FAAH2 (encoding fatty acid amide hydrolase 2), GPC3 (encoding glypican 3), and SERPINC1 (encoding serpin family C member 1) in high egg production population were significantly lower than those in the low egg production populations (*P < 0.05). The expression levels of CLPB (encoding caseinolytic peptidase B protein homolog), GNA12 (encoding guanine nucleotide-binding protein subunit alpha-12), and ZMAT5 (encoding zinc finger, matrin type 5) in the high egg production population were significantly higher than those in the low egg production populations (*P < 0.05). The expression of BMP4 (encoding bone morphogenetic protein 4), FRMPD3 (encoding FERM and PDZ domain containing 3), LIF (encoding leukemia inhibitory factor), and NFYC (encoding nuclear transcription factor Y subunit gamma) in the high egg production population were very significantly lower than those in the low egg production population (**P < 0.01). Our findings provide an insight into the economic traits of lion head goose. These candidate genes might be valuable for future breeding improvement.
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Project description:Albumen quality is a leading economic trait in the chicken industry. Major studies have paid attention to genetic architecture underlying albumen quality. However, the putative quantitative trait locus (QTL) for this trait is still unclear. In this genome-wide association study, we used an F2 resource population to study longitudinal albumen quality. Seven single-nucleotide polymorphism (SNP) loci were found to be significantly ( p<8.43×10-7 ) related to albumen quality by univariate analysis, while 11 SNPs were significantly ( p<8.43×10-7 ) associated with albumen quality by multivariate analysis. A QTL on GGA4 had a pervasive function on albumen quality, including a SNP at the missense of NCAPG, and a SNP at the intergenic region of FGFPB1. It was further found that the putative QTLs at GGA1, GGA2, and GGA7 had the strongest effects on albumen height (AH) at 32 weeks, Haugh units (HU) at 44 weeks, and AH at 55 weeks. Moreover, novel SNPs on GGA5 and GGA3 were associated with AH and HU at 32, 44, and 48 weeks of age. These results confirmed the regions for egg weight that were detected in a previous study and were similar with QTL for albumen quality. These results showed that GGA4 had the strongest effect on albumen quality. Only a few significant loci were detected for most characteristics probably reflecting the attributes of a pleiotropic gene and a minor-polygene in quantitative traits.
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Project description:Yak (Bos grunniens) is an important domestic animal living in high-altitude plateaus. Due to inadequate disease prevention, each year, the yak industry suffers significant economic losses. The identification of causal genes that affect blood- and immunity-related cells could provide preliminary reference guidelines for the prevention of diseases in the population of yaks. The genome-wide association studies (GWASs) utilizing a single-marker or haplotype method were employed to analyze 15 hematological traits in the genome of 315 unrelated yaks. Single-marker GWASs identified a total of 43 significant SNPs, including 35 suggestive and eight genome-wide significant SNPs, associated with nine traits. Haplotype analysis detected nine significant haplotype blocks, including two genome-wide and seven suggestive blocks, associated with seven traits. The study provides data on the genetic variability of hematological traits in the yak. Five essential genes (GPLD1, EDNRA,APOB, HIST1H1E, and HIST1H2BI) were identified, which affect the HCT, HGB, RBC, PDW, PLT, and RDWSD traits and can serve as candidate genes for regulating hematological traits. The results provide a valuable reference to be used in the analysis of blood properties and immune diseases in the yak.
| S-EPMC6627507 | biostudies-literature
Project description:MotivationThe quality control (QC) filtering of single nucleotide polymorphisms (SNPs) is an important step in genome-wide association studies to minimize potential false findings. SNP QC commonly uses expert-guided filters based on QC variables [e.g. Hardy-Weinberg equilibrium, missing proportion (MSP) and minor allele frequency (MAF)] to remove SNPs with insufficient genotyping quality. The rationale of the expert filters is sensible and concrete, but its implementation requires arbitrary thresholds and does not jointly consider all QC features.ResultsWe propose an algorithm that is based on principal component analysis and clustering analysis to identify low-quality SNPs. The method minimizes the use of arbitrary cutoff values, allows a collective consideration of the QC features and provides conditional thresholds contingent on other QC variables (e.g. different MSP thresholds for different MAFs). We apply our method to the seven studies from the Wellcome Trust Case Control Consortium and the major depressive disorder study from the Genetic Association Information Network. We measured the performance of our method compared to the expert filters based on the following criteria: (i) percentage of SNPs excluded due to low quality; (ii) inflation factor of the test statistics (lambda); (iii) number of false associations found in the filtered dataset; and (iv) number of true associations missed in the filtered dataset. The results suggest that with the same or fewer SNPs excluded, the proposed algorithm tends to give a similar or lower value of lambda, a reduced number of false associations, and retains all true associations.AvailabilityThe algorithm is available at http://www4.stat.ncsu.edu/jytzeng/software.php
| S-EPMC2894516 | biostudies-literature
Project description:BackgroundImproving the egg production of goose is a crucial goal of breeding, because genetics is the key factor affecting egg production. Thus, we sequenced the genomes of 55 Chinese indigenous geese from six breeds, which were divided into the high egg-laying group (ZE, HY, and SC) and low egg-laying group (ZD, LH, and ST). Based on the results of the inter-population selection signal analysis, we mined the selected genome regions in the high egg-laying germplasm population to identify the key candidate genes affecting the egg-laying traits.ResultsAccording to the whole-genome sequencing data, the average sequencing depth reached 11.75X. The genetic relationships among those six goose breeds coincided with the breed's geographical location. The six selective signal detection results revealed that the most selected regions were located on Chr2 and Chr12. In total, 12,051 single-nucleotide polymorphism (SNP) sites were selected in all six methods. Using the enrichment results of candidate genes, we detected some pathways involved in cell differentiation, proliferation, and female gonadal development that may cause differences in egg production. Examples of these pathways were the PI3K-Akt signaling pathway (IGF2, COMP, and FGFR4), animal organ morphogenesis (IGF2 and CDX4), and female gonad development (TGFB2).ConclusionOn analyzing the genetic background of six local goose breeds by using re-sequencing data, we found that the kinship was consistent with their geographic location. 107 egg-laying trait-associated candidate genes were mined through six selection signal analysis. Our study provides a critical reference for analyzing the molecular mechanism underlying differences in reproductive traits and molecular breeding of geese.
| S-EPMC10702089 | biostudies-literature
Project description:This study aimed to investigate the effects of dietary yeast culture (YC) supplementation on egg production, egg quality, reproductive performance, immune functions, antioxidant capacity, and intestinal microbial structure of aged hens. A total of 224 Hy-Line Brown layers (54 weeks old) were randomly assigned to two dietary treatments. The control group was fed a basal diet and the YC group was supplemented with YC at 2.0 g/kg of their diet. Each group had seven replicates with 16 hens each. The study was conducted over a period of 8 weeks. Results indicated that YC addition had no significant effect on laying performance. However, it significantly improved egg quality and hatching rate, enhanced ileum crude fat digestibility, increased the serum parameters of lysozyme (LZM) and total antioxidation capacity (T-AOC) (P < 0.05), and reduced serum aspartate aminotransferase (AST) levels (P < 0.05). Using 16S rRNA analysis, we found that addition of YC significantly altered ileum microbial composition. Linear discriminant analysis of effect size (LEfSe) showed significant enrichment of Bacilli and Lactobacilli in the YC group. PICRUSt analysis of the ileal microbiota found that glutathione metabolism, ubiquinone, and other terpenoid-quinone biosynthesis and lipopolysaccharide biosynthesis protein pathways were highly enriched in the YC group compared with the basal diet group. In summary, the addition of YC can improve egg quality, immune functions, antioxidant capacity, reproduction efficiency, and digestive absorption by increasing the abundance of Lactobacilli and Bacilli. Furthermore, it also improves the biosynthesis of lipopolysaccharide proteins, glutathione metabolism, and the synthesis of ubiquinone and other terpenoid-quinone metabolic pathways.
| S-EPMC8018237 | biostudies-literature
Project description:BackgroundEgg production is the most economically-important trait in layers as it directly influences benefits of the poultry industry. To better understand the genetic architecture of egg production, we measured traits including age at first egg (AFE), weekly egg number (EN) from onset of laying eggs to 80 weeks which was divided into five stage (EN1: from onset of laying eggs to 23 weeks, EN2: from 23 to 37 weeks, EN3: from 37 to 50 weeks, EN4: from 50 to 61 weeks, EN5: from 61 to 80 weeks) based on egg production curve and total egg number across the whole laying period (Total-EN). Then we performed genome-wide association studies (GWAS) in 1078 Rhode Island Red hens using a linear mixed model.ResultsEstimates of pedigree and SNP-based genetic parameter showed that AFE and EN1 exhibited high heritability (0.51 ± 0.09, 0.53 ± 0.08), while the h2 for EN in other stages varied from low (0.07 ± 0.04) to moderate (0.24 ± 0.07) magnitude. Subsequently, seven univariate GWAS for AFE and ENs were carried out independently, from which a total of 161 candidate SNPs located on GGA1, GGA2, GGA5, GGA6, GGA9 and GGA24 were identified. Thirteen SNP located on GGA6 were associated with AFE and an interesting gene PRLHR that may affect AFE through regulating oxytocin secretion in chickens. Sixteen genome-wide significant SNPs associated with EN3 were in a strong linkage disequilibrium (LD) region spanning from 117.87 Mb to 118.36 Mb on GGA1 and the most significant SNP (rs315777735) accounted for 3.57% of phenotypic variance. Genes POLA1, PDK3, PRDX4 and APOO identified by annotating sixteen genome-wide significant SNPs can be considered as candidates associated with EN3. Unfortunately, our study did not find any candidate gene for the total egg number.ConclusionsFindings in our study could provide promising genes and SNP markers to improve egg production performance based on marker-assisted breeding selection, while further functional validation is still needed in other populations.
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Project description:Prior work found the APOL1, 2 and 4 genes, located on chromosome 22q12.3-q13.1, to be upregulated in brains of schizophrenic patients. We performed a family-based association study using 130 SNPs tagging the APOL gene family (APOL1-6). The subjects were 112 African-American (AA), 114 European-American (EA), 109 Chinese (Ch) and 42 Japanese (Jp) families with schizophrenia (377 families, 1161 genotyped members and 647 genotyped affected in total). Seven SNPs had p-values<0.05 in the APOL1, 2 and 4 regions for the AA, EA and combined (AA and EA) samples. In the AA sample, two SNPs, rs9610449 and rs6000200 showed low p-values; and a haplotype which comprised these two SNPs yielded a p-value of 0.00029 using the global test (GT) and the allele specific test (AST). The two SNPs and the haplotype were associated with risk for schizophrenia in African-Americans. In the combined (AA and EA) sample, two SNPs, rs2003813 and rs2157249 showed low p-values; and a three SNP haplotype including these two SNPs was significant using the GT (p=0.0013) and the AST (p=0.000090). The association of this haplotype with schizophrenia was significant for the entire (AA, EA, Ch and Jp) sample using the GT (p=0.00054) and the AST (p=0.00011). Although our study is not definitive, it suggests that the APOL genes should be more extensively studied in schizophrenia.
| S-EPMC3736834 | biostudies-literature
Project description:BACKGROUND:Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. RESULTS:This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed most existing the-state-of-the-art random forests. The top 25 SNPs in Parkinson data set were identified by the proposed model including four interesting genes associated with neurological disorders. CONCLUSION:The presented approach has shown to be effective in selecting informative sub-groups of SNPs potentially associated with diseases that traditional statistical approaches might fail. The new RF works well for the data where the number of case-control objects is much smaller than the number of SNPs, which is a typical problem in gene data and GWAS. Experiment results demonstrated the effectiveness of the proposed RF model that outperformed the state-of-the-art RFs, including Breiman's RF, GRRF and wsRF methods.
| S-EPMC4331719 | biostudies-literature
Project description:The wide use of genomic information has enabled the identification of lethal recessive alleles that are the major genetic causes of reduced conception rates, longer calving intervals, or lower survival for live-born animals. This study was carried out to screen the Nellore cattle genome for lethal recessive haplotypes based on deviation from the expected population homozygosity, and to test SNP markers surrounding the lethal haplotypes region for association with heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY). This approach requires genotypes only from apparently normal individuals and not from affected embryos. A total of 62,022 animals were genotyped and imputed to a high-density panel (777,962 SNP markers). Expected numbers of homozygous individuals were calculated, and the probabilities of observing 0 homozygotes was obtained. Deregressed genomic breeding values [(G)EBVs] were used in a GWAS to identify candidate genes and biological mechanisms affecting HR, STAY and PNM. In the functional analyses, genes within 100 kb down and upstream of each significant SNP marker, were researched. Thirty haplotypes had high expected frequency, while no homozygotes were observed. Most of the alleles present in these haplotypes had a negative mean effect for PNM, HR and STAY. The GWAS revealed significant SNP markers involved in different physiological mechanisms, leading to harmful effect on the three traits. The functional analysis revealed 26 genes enriched for 19 GO terms. Most of the GO terms found for biological processes, molecular functions and pathways were related to tissue development and the immune system. More phenotypes underlying these putative regions in this population could be the subject of future investigation. Tests to find putative lethal haplotype carriers could help breeders to eliminate them from the population or manage matings in order to avoid homozygous.
| S-EPMC10300016 | biostudies-literature