Growth traits and carcass traits are two economically important traits in the poultry industry. However, the conventional method of improving product performance by seed selection; So far in the genetic aspects of chicken, the research for disease resistance traits is less. With the development of biotechnology, marker assisted selection (MAS) are used to support breeding programs and become to be an important aspect of breeding work. Two methods are widely used to select genetic molecular markers:candidate genes and QTL mapping, however, both of them have some deficiencies. SLAF-seq simplifies chicken’s whole genome and then obtains SNPs within the scope of the entire genome. The purpose of SLAF-seq is to found the genetic markers which influence complex traits and the distribution of the genetic markers. Thus, SLAF-seq as one kind of GWAS which has been one of the most effective approaches for identifying related QTLs and functional genes. One of the biggest advantage for GWAS is that GWAS do not need an uncertainty hypothesis, it is a direct association study between molecular markers and economic traits.Therefore, SLAF-seq sequencing were utilized to analyze thirteen carcass traits, ten growth traits and six disease resistance traits of Jinghai Yellow Chicken. The purpose of this study was to identify key SNPs affecting these important traits, discuss distribution characteristics and explore candidate genes. All these results would benefit the molecular researches and provide the good foundation for the breed selection. The main result are shown as follows:1. After SLAF-seq sequencing, we use admixture software to calculate group structure and then use two TASSEL models:general linear model (GLM) and mixed linear model (MLM) were used to analyze the associations between genotype data and growth traits, carcass traits and some disease resistance traits. LD revised bonferoni correction was used to correct the error caused by multiple comparisons. Focus on the SNPs that reached genome-wide significance in GLM or potential genome-wide significance in MLM and use databases such as COG, GO, KEGG to annotate the candidate genes. The results indicate that both two models can well correct population stratification and the SNPs in GLM and MLM are most of the same. What’ more, the same SNP’s P value in MLM are higher than GLM. Our study adopted the GLM and MLM system, two models are compared and supplement to get more accurate and comprehensive results.2. For growth traits, GLM model detected nineteen SNPs that reached genome-wide significance(P<1.87E-06) and nine genes. Some SNPs are significantly associated with several traits such as LDB2, QDPR, INTS6, BOD1L1 and so on. GLM model also detected 102 SNPs that reached potential genome-wide significance(1.87E-06<P<3.75E-05). MLM model found sixteen SNPs reached potential significance(three genome-wide significance) and seven candidate genes such as LDB2, QDPR, FAM124A, NUK1 and so on, all the genes are important. We noticed some genes are important both in GLM and MLM. Some genes have some reports yet there are nine genes such as CHST1, GPR78, VISIG4, HS3ST1, FHIT and so on are newly found genes. The 75.6-80.7Mb region on GGA4 is the main functional region that affected growth traits. We also annotated two important pathways:KEGG:00790 and KEGG:00533 by KEGG database. Finally, according to previous reports and results, we preliminarily determines some SNPs affect Jinghai Yellow Chicken growth traits:rs313973972 in LDB2, rs14491071 near QDPR and so on.3. In the GWAS of carcass traits, GLM identified SNPs reaching genome-wide significance with carcass traits (P<1.87E-06). Seven SNPs in 75.50-76.14Mb region on GGA4 are significantly associated with CW, WW, FW and detected 7 candidate genes such as FAM184B, QDPR, LAP3, ECL1 and so on. Morever,81 SNPs reached potential genome-wide significance(1.87E-06<P<3.75E-05). MLM detected 12 potential significance SNPs (8 reached genome-wide significance) and 8 candidate genes. What’more, all these genes were also detected by GLM. Some parts of the SNPs are associated with several traits. KEGG annotated four pathways:Fatty acid metabolism pathway, folic acid synthesispathway, basal transcription factor pathway and arginine metabolic pathway(ko0033ã€KEGG:00790ã€KEGG:03022 and ko00330). Seven genes such as ECL1, ZNF302, GTF2H5 and so on are newly found genes that associated with carcass traits. Finally, according to previous reports and results, we preliminarily determines some SNPs affect Jinghai Yellow Chicken carcass traits:rs14710787ã€rs16023603 in FAM184B for FW, rs14710787ã€rs13755802 near SKIDA1 for AW, rs14359385ã€rs315486571 near GTF2H5 and rs318008335 near TMEM181 for EW and so on.3. In the GWAS of some disease resistance traits, GLM identified 4 SNPs reached genome-wide significance:1 for AI,2 for ND and 1 for INF-α. They were associated with 3 genes. MLM detected 8 SNPs reached potential genome-wide significance and were related to 5 genes. SETBP1 was also found by GLM. Due to few researches of disease resistance in chicken genetic, all the candidate genes were not reported in chicken, yet we found in other species some of these gene’s mutation may leed to several important signal pathways’ interrupt, which influence many disease processes. Thus we speculate that these genetic mutation may also have important influence on Jinghai Yellow Chicken’s disease resistance traits which pointed out the direction for future work and merit further research. Such as rs31696620ã€rs312624692 in Plexin B1 and rs317837423 in PDGFC for ND, rsl5613786 in Nsun7 for AI and rs313017675 near USP7 for IB. |