Dataset Information

Single SNP- and pathway-based genome-wide association studies for beak deformity in chickens using high-density 600K SNP arrays.

ABSTRACT: BACKGROUND:Beak deformity, typically expressed as the crossing of upper and lower mandibles, is found in several indigenous chicken breeds, including the Beijing-You chickens studied here. Beak deformity severely impairs the birds' growth and welfare. Although previous studies shed some light on the genetic regulation of this complex trait, the genetic basis of this malformation remains incompletely understood. RESULTS:In this study, single SNP- and pathway-based genome-wide association studies (GWASs) were performed using ROADTRIPS and SNP ratio test (SRT), respectively. A total of 48 birds with deformed beaks (case) and 48 normal birds (control) were genotyped using Affymetrix 600 K HD genotyping arrays. As a result, 95 individuals and 429,539 SNPs were obtained after quality control. The P-value was corrected by a Bonferroni adjustment based on linkage disequilibrium pruning. The single SNP-based association study identified one associated SNP with 5% genome-wide significance and seven suggestively associated SNPs. Four high-confidence genes, LOC421892, TDRD3, RET, and STMN1, were identified as the most promising candidate genes underlying this complex trait in view of their positions, functions, and overlaps with previous studies. The pathway-based association study highlighted the association of six pathways with beak deformity, including the calcium signaling pathway. CONCLUSIONS:Potentially useful candidate genes and pathways for beak deformity were identified, which should be the subject of further functional characterization.

SUBMITTER: Bai H

PROVIDER: S-EPMC6022433 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Single SNP- and pathway-based genome-wide association studies for beak deformity in chickens using high-density 600K SNP arrays.

Bai Hao H Sun Yanyan Y Liu Nian N Xue Fuguang F Li Yunlei Y Xu Songshan S Ye Jianhua J Zhang Lei L Chen Yu Y Chen Jilan J

BMC genomics 20180628 1

<h4>Background</h4>Beak deformity, typically expressed as the crossing of upper and lower mandibles, is found in several indigenous chicken breeds, including the Beijing-You chickens studied here. Beak deformity severely impairs the birds' growth and welfare. Although previous studies shed some light on the genetic regulation of this complex trait, the genetic basis of this malformation remains incompletely understood.<h4>Results</h4>In this study, single SNP- and pathway-based genome-wide assoc ...[more]

PMID: 29954329

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Project description:Following chicken domestication, diversified chicken breeds were developed by both natural and artificial selection, which led to the accumulation of abundant genetic and phenotypic variations, making chickens an ideal genetic research model. To better understand the genetic structure of chicken breeds under different selection pressures, we genotyped various chicken populations with specific selection targets, including indigenous, commercial, gamecock, and wild ancestral chickens, using the 600K SNP array. We analyzed the population structure, genetic relationships, run of homozygosity (ROH), effective population number (Ne), and other genetic parameters. The wild ancestral population, red junglefowl (RJF), possessed the highest diversity, in comparison with all other domesticated populations, which was supported by linkage disequilibrium decay (LD), effective population number, and ROH analyses. The gamecock breeds, which were subjected to stronger male-biased selection for fighting-related traits, also presented higher variation than the commercial and indigenous breeds. Admixture analysis also indicated that game breed is a relatively independent branch of Chinese local breeds. Following intense selection for reproductive and productive traits, the commercial lines showed the least diversity. We also observed that the European local chickens had lower genetic variation than the Chinese local breeds, which could be attributed to the shorter history of the European breed. ROH were present in a breed specific manner and 191 ROH island were detected on four groups (commercial, local, game and wild chickens). These ROH islands were involved in egg production, growth and silky feathers and other traits. Moreover, we estimated the effective sex ratio of these breeds to demonstrate the change in the ratio of the two sexes. We found that commercial chickens had a greater sex imbalance between females and males. The commercial lines showed the highest female-to-male ratios. Interestingly, RJF comprised a greater proportion of males than females. Our results show the population genetics of chickens under selection pressures, and can aid in the development of better conservation strategies for different chicken breeds.

Project description:BackgroundHigh density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.ResultsWe report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10-15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.ConclusionsThis Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants.

Project description:BACKGROUND: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. RESULTS: We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. CONCLUSIONS: The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

Dataset Information

Single SNP- and pathway-based genome-wide association studies for beak deformity in chickens using high-density 600K SNP arrays.

Publications

Single SNP- and pathway-based genome-wide association studies for beak deformity in chickens using high-density 600K SNP arrays.

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