Population genomics · RNA-seq · Regulatory variation
How multi-omics helps investigate chicken adaptation
Different layers of biological data answer different questions. Their value comes from connecting those questions while retaining the limits of each analysis.
Population genomics: where to look
Comparing genetic variation across populations can identify genomic regions associated with different histories and environments. In chicken adaptation research, selection scans help prioritize candidate regions for closer investigation. Population structure and demographic history remain important when interpreting these patterns.
Expression: what tissues are doing
Gene-expression data offer a view of biological activity in sampled tissues and conditions. They complement genomic scans by asking whether candidate genes or pathways also appear in a relevant expression context. Tissue-resolved evidence matters because physiological functions differ among organs.
Regulatory variation: connecting the layers
Expression quantitative trait locus analysis investigates associations between genetic variants and gene expression. It can connect sequence variation with regulatory differences, providing another line of evidence alongside population-genomic and expression analyses.
Ali Hassan Nawaz and colleagues’ 2026 Poultry Science study combined selective scans across 14 geographically and climatically diverse chicken breeds with expression and regulatory analyses. The work investigated a candidate cardio-neuroendocrine signaling network associated with heat adaptation. The publication record and its correction are linked below.
What integration can and cannot establish
When several analyses point to a related biological process, they can strengthen the rationale for targeted follow-up. They do not automatically establish a causal chain or demonstrate that changing one candidate gene will improve heat tolerance. The appropriate next experiment depends on which part of the hypothesis remains unresolved.
Nawaz and colleagues’ 2025 review extends the multi-omics perspective to chicken growth regulation and emerging genetic tools. Together, these publications show how genomic methods can organize biological evidence and guide testable research questions.