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Background: Angel wing is a developmental wing deformity that can influence breeding and reproduction in the commercial duck industry. Therefore, genetic diversity studies and detection of the genomic region related to angel wings in duck populations are essential. In this regard, powerful tools, such as next-generation sequencing technology, have made it possible to decode genome information in this species. The genome-wide association study (GWAS) has been a powerful tool in detecting loci associated with complex traits and diseases; however, it also has some limitations. Complex traits are controlled by many genes, and hence, significant SNPs in general represent only a small fraction of genetic variation. Moreover, studies often report only the most significant SNPs and their neighboring genes, hence some smaller genetic variants and disease risks are unlikely to be detected. Alternatively, pathway-based analysis has been proposed as a complementary approach to investigate complex traits from a genetic and biological perspective. In contrast to a GWAS, pathway-based analysis considers factors that contribute simultaneously to the complex trait and looks beyond the most significant SNPs and genes. To complement GWAS studies, it is becoming common to use gene-set enrichment and pathway analyses. Such an approach helps alleviate problems related to GWAS (e.g., GWAS ignores the fact that genes work together in networks in various biological pathways), and to deepen the understanding of the biological pathways affecting quantitative traits. Integration of F, GWAS, and pathways analyses might address some aforementioned issues and has been already used in human studies, whereas its potential application in livestock breeding and genetics remains still unexplored. In addition, studies are available that performed GWAS or GWAS plus pathway analysis. Methods: A total of 63 adult purebred Pekin ducks from the same population were selected for this study, of which 33 were ducks that could be identified as having angel wings (case) and 30 were ducks with normal wings (control). Genomic DNA was extracted from blood samples by DNA extraction using a kit (QIAampR DNA Blood Mini Kit; QIAGEN), following the manufacturer's protocol. Whole-genome re-sequencing data were generated on the Illumina Hiseq 4000 platform with 150 bp paired-end reads. Single-nucleotide polymorphism (SNP) calling was performed using the GATK (v4.1), and all parameters were kept at default settings, except for stand_callconf 30. VCFTOOLS (v0.1.16) and plink (v 1.90) were used for the quality control of the data. The 14 064 984 SNPs passed quality control that excluded SNPs using the following criteria: --min- alleles 2, --max- alleles 2, --minDP 3 –minQ 30 with VCFtools, minor allele frequency >0.01, and SNP call rate ≥ 0.95 with plink. An independent SNP set was used via the plink command --indep- pairwise 50 5 0.2 for principal component analysis. After quality control, 686 449 SNPs were used for the GWAS. The gene set analysis consists basically of three different steps: (i) the assignment of SNPs to genes, (ii) the assignment of genes to functional categories, and (iii) an association analysis between each functional category and the phenotype of interest. 1. The SNPs were assigned to bovine genes based on the CAU_duck1.0 duck genome sequence assembly using the Bioconductor R package biomaRt2. A given SNP was assigned to a particular gene if it was located within the gene or at most 15 kb either upstream or downstream of the gene. An arbitrary threshold of P-value ≤ 0.005 was used to define significant SNPs (based on the results of the GWAS); in this context, significant genes were defined as those genes that contained at least one significant SNP. 2. The databases Gene Ontology (GO) and Medical Subject Headings (MeSH) were used to define functional categories of genes. The idea is that genes assigned to the same functional category can be considered the members of a group of genes that share some particular properties, typically their involvement in the same biological or molecular process. 3. The significant association of a given term with angle wing was analyzed using Fisher’s exact test. Finally, a gene enrichment analysis was performed with the GOstats Bioconductor from R software for the assignment of the genes to functional categories. Results: The random effect was estimated from the groups clustered based on the kinship among all accessions, and the first two PCs, including PC1 with normal wings and PC2 with angel wings derived from whole-genome SNPs, were used as fixed effects in the mixed model to correct for stratification. In this research, SNP markers were identified on chromosomes 1, 2, 3, 6, 8, 11, 18, 20, 27, and 31. Different sets of candidate genes related to the angle wing trait, namely ATP11A, UBE2E2, ITPR2, GUCA1C, ATP2C1, PLCG1, and BMPR1A, were also identified in ducks. Some of the found genes are consistent with some of the previous studies related to wing traits. According to pathway analysis, 21 pathways from GO and biological pathways were associated with the angle wing trait. Some of the detected genes are consistent with some previous studies and are involved in biological pathways related to skeletal muscle growth and development, calcium ion response, bone growth and development, bone minerals, and calcium signaling pathway activation. Conclusion: The results of our research can be used to understand the genetic mechanism controlling the angle wing trait. This study supports previous results from the GWAS of reproductive traits, revealing additional regions. Using these findings could potentially be useful for genetic selection in ducks. [ABSTRACT FROM AUTHOR]