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The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing <t>(for</t> <t>microbiota</t> profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; <t>DNA</t> was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.
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The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing <t>(for</t> <t>microbiota</t> profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; <t>DNA</t> was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.
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The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing <t>(for</t> <t>microbiota</t> profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; <t>DNA</t> was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.
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The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing <t>(for</t> <t>microbiota</t> profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; <t>DNA</t> was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.
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The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing (for microbiota profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; DNA was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.

Journal: Poultry Science

Article Title: Multi-omics analysis identifies key microbial taxa and host genes controlling abdominal fat deposition in chickens

doi: 10.1016/j.psj.2026.106714

Figure Lengend Snippet: The workflow for analyzing chicken abdominal fat weight (AFW) via integrated omics. An experimental cohort of Qiandongnan Xiaoxiang chickens ( n = 205) was established for studying abdominal fat weight. At 70 days of age, AFW phenotypes were measured and samples of whole blood, serum and cecal contents were collected from all individuals; Cecal contents and serum samples were subjected to 16S rRNA gene sequencing (for microbiota profiling) and untargeted metabolomic analysis, respectively. Six serum cytokines were quantified. The two-part model was applied to identify cecal microbiota and serum metabolites associated with AFW and correlation analysis between AFW and cytokines was performed; DNA was subjected to whole-genome resequencing. Heritability estimates were generated for AFW-related cecal microbiota and serum metabolites, with microbiota associated genetic loci identified via microbial Genome-wide association studies (mGWAS); An integrated analysis of host genome, cecal microbiota and serum metabolome was conducted to dissect the interplay between host genetics and cecal microbiota and to explore the mechanisms by which they synergistically regulate metabolites to modulate AFW.

Article Snippet: DNA of cecal microbiota was extracted using the Magnetic Soil and Stool DNA Kit (Tiangen, Beijing, China), with procedures strictly followed the manufacturer’s protocols.

Techniques: Sequencing, Metabolomic, Generated, GWAS