Additional file 1: of Efficiency of different strategies to mitigate ascertainment bias when using SNP panels in diversity studies

Zip file containing allele frequency spectrum figures of each population. (ZIP 11230 kb

Additional file 2: Table S1. of Efficiency of different strategies to mitigate ascertainment bias when using SNP panels in diversity studies

Proportion of SNPs in genic and non-genic in WGS and array data. Table S2. Population clusters. Table S3. Topological distances between NJ trees of WGS and array data based on Billera method. Table S4. Topological distances between NJ trees of WGS and array data based on Penny and Hendy method. Table S5. Topological distances among the array versions. (DOCX 25 kb

Result of ADMIXTURE structural analysis with null hypothesis of six breeds.

<p>Two rightmost individuals in OG are Gallus gallus gallus, and the third and fourth last individuals are Gallus gallus spadiceus.</p

Name, abbreviation, number of individuals and the egg color for each breed used in this study.

<p>Name, abbreviation, number of individuals and the egg color for each breed used in this study.</p

Population Genomic Analyses Based on 1 Million SNPs in Commercial Egg Layers

<div><p>Identifying signatures of selection can provide valuable insight about the genes or genomic regions that are or have been under selective pressure, which can lead to a better understanding of genotype-phenotype relationships. A common strategy for selection signature detection is to compare samples from several populations and search for genomic regions with outstanding genetic differentiation. Wright's fixation index, F_ST, is a useful index for evaluation of genetic differentiation between populations. The aim of this study was to detect selective signatures between different chicken groups based on SNP-wise F_ST calculation. A total of 96 individuals of three commercial layer breeds and 14 non-commercial fancy breeds were genotyped with three different 600K SNP-chips. After filtering a total of 1 million SNPs were available for F_ST calculation. Averages of F_ST values were calculated for overlapping windows. Comparisons of these were then conducted between commercial egg layers and non-commercial fancy breeds, as well as between white egg layers and brown egg layers. Comparing non-commercial and commercial breeds resulted in the detection of 630 selective signatures, while 656 selective signatures were detected in the comparison between the commercial egg-layer breeds. Annotation of selection signature regions revealed various genes corresponding to productions traits, for which layer breeds were selected. Among them were <i>NCOA1</i>, <i>SREBF2</i> and <i>RALGAPA1</i> associated with reproductive traits, broodiness and egg production. Furthermore, several of the detected genes were associated with growth and carcass traits, including <i>POMC</i>, <i>PRKAB2</i>, <i>SPP1</i>, <i>IGF2</i>, <i>CAPN1</i>, <i>TGFb2</i> and <i>IGFBP2</i>. Our approach demonstrates that including different populations with a specific breeding history can provide a unique opportunity for a better understanding of farm animal selection.</p></div

FST-values of overlapping windows for comparison between commercial layers and out-group.

<p>Red (blue) line indicates the upper (lower) 1% of FST distribution.</p

FST-values of overlapping windows for comparison between brown layers and white layers.

<p>Red (blue line) indicates the upper (lower) 1% of FST distribution.</p

Genes associated to productive traits in both comparisons. ≠ symbol stands for difference between two group and = symbol stand for similarity between two groups.

<p>B and W stand for comparison between brown and white egg layers and L and G stand for comparison between commercial layers and out-group.</p

chicken data

chicken dat

Average F_ST values with standard deviation over all SNPs for all compression.

<p>Average F_ST values with standard deviation over all SNPs for all compression.</p