Genome-wide association study identified candidate genes for egg production traits in the Longyan Shan-ma duck.

Egg production is an important economic trait in layer ducks and understanding the genetics basis is important for their breeding. In this study, a genome-wide association study (GWAS) for egg production traits in 303 female Longyan Shan-ma ducks was performed based on a genotyping-by-sequencing strategy. Sixty-two single nucleotide polymorphisms (SNPs) associated with egg weight traits were identified (P < 9.48 × 10-5), including 8 SNPs at 5% linkage disequilibrium (LD)-based Bonferroni-corrected genome-wide significance level (P < 4.74 × 10-6). One hundred and nineteen SNPs were associated with egg number traits (P < 9.48 × 10-5), including 13 SNPs with 5% LD-based Bonferroni-corrected genome-wide significance (P < 4.74 × 10-6). These SNPs annotated 146 target genes which contained known candidate genes for egg production traits, such as prolactin and prolactin releasing hormone receptor. This study identified that these associated genes were significantly enriched in egg production-related pathways (P < 0.05), such as the oxytocin signaling, MAPK signaling, and calcium signaling pathways. It was notable that 18 genes were differentially expressed in ovarian tissues between higher and lower egg production in Shan-ma ducks. The identified potential candidate genes and pathways provide insight into the genetic basis underlying the egg production trait of layer ducks.

Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data.

Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.

Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk.

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk.

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility.

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.

Genome-wide association study for the primary feather color trait in a native Chinese duck.

Background: To reveal candidate genes and the molecular genetic mechanism underlying primary feather color trait in ducks, a genome-wide association study (GWAS) for the primary feather color trait was performed based on the genotyping-by-sequencing (GBS) technology for a native Chinese female duck, Longyan Shan-ma ducks. Methods: Blood genomic DNA from 314 female Longyan Shan-ma duck were genotyped using GBS technology. A GWAS for the primary feather color trait with genome variations was performed using an univariate linear mixed model based on all SNPs in autosomes. Results: Seven genome-wide significant single nucleotide polymorphisms (SNPs, Bonferroni-adjusted p-value <8.03 × 10-7) within the introns of the genes STARD9, ZNF106, SLC7A5, and BANP genes were associated with the primary feather color trait. Twenty-two genome-wide suggestive SNPs (Bonferroni-adjusted p-value <1.61 × 10-5) of 17 genes (besides ZNF106 and SLC7A5) were also identified. Seven SNPs were located at one 0.22 Mb region (38.65-38.87 Mb) on chromosome 5, and six SNPs were located at one 0.31 Mb region (19.53-19.84 Mb) on chromosome 11. The functions of STARD9, SLC7A5, BANP, LOC101798015, and IPMK were involved pigmentation and follicle development, especially, STARD9 upregulated expression in black feather (haplotype-CCCC) bulb tissue compared with in pockmarked feather (haplotype-TGTT) bulb tissue, implicating these genes as candidate genes for primary feather color trait. Conclusion: The preliminarily findings suggested candidate genes and regions, and the genetic basis of primary feather color trait in a female duck.

Whole-genome sequencing identifies potential candidate genes for egg production traits in laying ducks (Anas platyrhynchos).

Egg production traits are economically important in laying ducks. Genetic molecular mechanisms and candidate genes underlying these traits remain unclear. In this study, whole genome variants were identified through whole-genome resequencing using three high-egg producing (HEN) and three low-egg producing (LEN) laying ducks. The gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for the genes of common differential variants between HEN and LEN ducks were determined. Frizzled class receptor 6 (FZD6) was further genotyped using the Sequenom MassARRAY iPLEX platform. The association of FZD6 gene polymorphisms with 73 egg production and weight traits in 329 female ducks were estimated. A total of 65,535 single nucleotide polymorphisms (SNPs) and 4,702 indels were identified across the genome. Fourteen GO terms and 14 KEGG pathways were determined for the genes of common differential variants, including MAPK signaling, Wnt signaling, melanogenesis and calcium signaling pathways, which are key functional pathways for poultry egg production reported in previous reports. Further analysis showed that 27 SNPs of FZD6 were associated with three early egg production of duck and egg weight traits, including egg production at 17 weeks (EP17), 18 weeks (EP18) and 19 weeks (EP19) and egg weight at 59 weeks (EW59). The FZD6 should be considered a novel candidate gene for egg production traits in laying ducks.

Effects of Dietary Vitamin E on Intramuscular Fat Deposition and Transcriptome Profile of the Pectoral Muscle of Broilers.

Vitamin E is an essential micronutrient for animals. The aim of this study was to determine the effect of vitamin E on intramuscular fat (IMF) deposition and the transcriptome profile of the pectoral muscle in broiler chickens. Arbor Acres chickens were divided into five treatment groups fed a basal diet supplemented with 0, 20, 50, 75, and 100 IU/kg dietary DL-α-tocopheryl acetate (vitamin E), respectively. Body weight, carcass performance, and IMF content were recorded. Transcriptome profiles of the pectoral muscles of 35-day-old chickens in the control and treatment groups (100 IU/kg of vitamin E) were obtained by RNA sequencing. The results showed that diets supplemented with 100 IU/kg of vitamin E significantly increased IMF deposition in chickens on day 35. In total, 159 differentially expressed genes (DEGs), including 57 up-regulated and 102 down-regulated genes, were identified in the treatment (100 IU/kg vitamin E) group compared to the control group. These DEGs were significantly enriched in 13 Gene Ontology terms involved in muscle development and lipid metabolism; three signaling pathways, including the mitogen-activated protein kinase and FoxO signaling pathways, which play key roles in muscular and lipid metabolism; 28 biofunctional categories associated with skeletal and muscular system development; 17 lipid metabolism functional categories; and three lipid metabolism and muscle development-related networks. The DEGs, pathways, functional categories, and networks identified in this study provide new insights into the regulatory roles of vitamin E on IMF deposition in broilers. Therefore, diets supplemented with 100 IU/kg of vitamin E will be more beneficial to broiler production.

Colonization and Development of the Fecal Microflora of South China Tiger Cubs (Panthera tigris amoyensis) by Sequencing of the 16S rRNA Gene.

Postnatal colonization and development of the gut microbiota is linked to health and growth. A comprehensive understanding of the postnatal compositional changes and development of the microbial community is helpful to understand the gut health and improve the survival rate of South China tiger cubs (Panthera tigris amoyensis). Fecal samples from three tiger cubs were collected on the day of birth in 2018 (June 17-21 [G0], July 18 [G1], July 31 [G2], and August 7 [G3]). The 16S rRNA genes of the fecal microflora were sequenced. Results showed that 38 phyla, 58 classes, 134 orders, 272 families, and 636 genera of bacteria from 3,059 operational taxonomic units were identified from 12 fecal samples. The diversity and abundance of species of group G0 were significantly higher (p < 0.05 or 0.01) than those of groups G2 and G3. The predominant phylum was Proteobacteria in groups G0 and G1 (38.85% and 48%, respectively) and Firmicutes in groups G2 and G3 (71.42% and 75.29%, respectively). At the phylum level, the abundance of Deinococcus-Thermus was significantly decreased in groups G1, G2, and G3 as compared to group G0 (p < 0.05), while that of Firmicutes was significantly increased in groups G2 and G3 (p < 0.05). At the genus level, the abundance of Faecalibacterium, Ralstonia, and unidentified Rickettsiales was significantly decreased in groups G1, G2, and G3 as compared with group G0 (p < 0.05), while that of Pseudomonas was significantly decreased in groups G2 and G3 (p < 0.05). The composition and structure of fecal microbiota of South China tiger cubs changed after birth.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk.

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes.

BACKGROUND: Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer's disease (AD), but the genes responsible for these associations remain largely unknown. METHODS: We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry. RESULTS: We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10-6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal. CONCLUSIONS: Our study provides new insights into the etiology and underlying genetic architecture of AD.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk.

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity.

PURPOSE: It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19. METHODS: We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes. RESULTS: Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity. CONCLUSION: We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

Identification of Differentially Expressed Genes and Lipid Metabolism Signaling Pathways between Muscle and Fat Tissues in Broiler Chickens.

In this study, signaling pathways and key differentially expressed genes (DEGs) involved in lipid metabolism in muscle and fat tissues were investigated. Muscle and abdominal fat tissues were obtained from 35-day-old female broilers for RNA sequencing. DEGs between muscle and fat tissues were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were performed. A total of 6130 DEGs were identified to be significantly enriched in 365 GO terms, most of which were involved in biological processes, cellular components, and molecular functions in muscle and fat tissues. Three important lipid signaling pathways (pyruvate metabolism, the insulin signaling pathway, and the adipocytokine signaling pathway) were identified among the fat and muscle tissues of broilers. The key common DEGs in these pathways included phosphoenolpyruvate carboxykinase 2 (PCK2), acetyl-CoA carboxylase 1 alpha and beta (ACACA and ACACB), and the mitogen-activated protein kinase (AMPK) gene family. Hence, our findings revealed the pathways and key genes and gene families involved in the regulation of fat deposition in the muscle and fat tissues of broilers.

Effect of Vitamin E Supplementation on Deposition and Gene Expression Profiling of Abdominal Fat in Broiler Chickens.

The aim of this study was to study the regulation of abdominal fat deposition by DL-α-tocopherol acetate (vitamin E) in broilers. Diets supplemented with 50 IU vitamin E significantly diminished abdominal fat deposition in broilers at day 35. Transcriptome sequencing results for abdominal fat tissues of the control (FC) and 50 IU vitamin E-supplemented (FT) groups identified 602 differentially expressed genes (DEGs), which were enriched in cellular process, cell and cell part, and binding Gene Ontology terms. Pathway functional analysis revealed that the DEGs were enriched in 42 metabolic pathways. Notably, the most enriched pathway, fatty acid biosynthesis, was found to play a key role in lipid metabolism. Further, the key regulators of lipid metabolism, including fatty acid synthase, acetyl-CoA carboxylase alpha, and acyl-CoA synthetase long-chain family member 1, demonstrated decreased expression following vitamin E supplementation. Herein, we have identified pathways and genes regulated by vitamin E, thereby providing novel insights into the nutrients regulating abdominal fat deposition in broilers.

Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. METHODS: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. RESULTS: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein-protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. CONCLUSION: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1, and PGAP3) and six novel genes not yet reported for pancreatic cancer risk [6q27: SFT2D1 OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: MTMR6 OR (95% CI), 0.78 (0.70-0.88); 14q24.3: ACOT2 OR (95% CI), 1.35 (1.17-1.56); 17q12: STARD3 OR (95% CI), 6.49 (2.96-14.27); 17q21.1: GSDMB OR (95% CI), 1.94 (1.45-2.58); and 20p13: ADAM33 OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1, and PGAP3) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk.

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Genome-Wide Linkage Analysis Identifies Loci for Testicle and Ovary Traits in Chickens.

Development of testes or ovaries is critical to chicken breeders. Understanding the genetic mechanisms influencing the development of the testes and ovaries could enhance selection efforts which target reproductive traits. The linkage analysis was conducted within an F2 population derived from Beijing-You chickens and a commercial broiler line. The results have identified one quantitative trait loci (QTL, designated T1) for bilateral testicular weight (TW) and the percentage of TW to carcass weight, and five QTLs (designated O1-O5) for ovary weight (follicle-free, OW) and the percentage of OW to carcass weight. For the testes traits, QTL T1 is located between 6.55 and 8.56 Mb on GGA13. Especially, the gene gamma-amino butyric acid A receptor, alpha 1 (GABRA1) located near the T1 peak. For ovarian traits, QTL O2 was located at 29.31 Mb on GGA7. G protein-coupled receptor 39 (GPR39) present at the O2 peak was expressed at higher levels within the reproductive tract. It is also involved in the regulation of several reproductive functions. Other QTL peaks and the genes' function in the ovary and testes need to be evaluated. The QTLs and the genes identified in this study could provide valuable information for establishing reproductive traits in chickens, and need further investigation.

Genome-Wide Linkage Analysis Identifies Loci for Physical Appearance Traits in Chickens.

Physical appearance traits, such as feather-crested head, comb size and type, beard, wattles size, and feathered feet, are used to distinguish between breeds of chicken and also may be associated with economic traits. In this study, a genome-wide linkage analysis was used to identify candidate regions and genes for physical appearance traits and to potentially provide further knowledge of the molecular mechanisms that underlie these traits. The linkage analysis was conducted with an F2 population derived from Beijing-You chickens and a commercial broiler line. Single-nucleotide polymorphisms were analyzed using the Illumina 60K Chicken SNP Beadchip. The data were used to map quantitative trait loci and genes for six physical appearance traits. A 10-cM/0.51-Mb region (0.0-10.0 cM/0.00-0.51 Mb) with 1% genome-wide significant level on LGE22C19W28_E50C23 linkage group (LGE22) for crest trait was identified, which is likely very closely linked to the HOXC8. A QTL with 5% chromosome-wide significant level for comb weight, which partly overlaps with a region identified in a previous study, was identified at 74 cM/25.55 Mb on chicken (Gallus gallus; GG) chromosome 3 (i.e., GGA3). For beard and wattles traits, an identical region 11 cM/2.23 Mb (0.0-11.0 cM/0.00-2.23 Mb) including WNT3 and GH genes on GGA27 was identified. Two QTL with 1% genome-wide significant level for feathered feet trait, one 9-cM/2.80-Mb (48.0-57.0/13.40-16.20 Mb) region on GGA13, and another 12-cM/1.45-Mb (41.0-53.0 cM/11.37-12.82 Mb) region on GGA15 were identified. These candidate regions and genes provide important genetic information for the physical appearance traits in chicken.