Additional file 2 of A meta-analysis of genetic and phenotypic diversity of European local pig breeds reveals genomic regions associated with breed differentiation for production traits
Creators: name: Poklukar, Klavdija
name: Mestre, Camille
name: Škrlep, Martin
name: Čandek-Potokar, Marjeta
name: Ovilo, Cristina
name: Fontanesi, Luca
name: Riquet, Juliette
name: Bovo, Samuele
name: Schiavo, Giuseppina
name: Ribani, Anisa
name: Muñoz, Maria
name: Gallo, Maurizio
name: Bozzi, Ricardo
name: Charneca, Rui
name: Quintanilla, Raquel
name: Kušec, Goran
name: Mercat, Marie-José
name: Zimmer, Christoph
name: Razmaite, Violeta
name: Araujo, Jose P.
name: Radović, Čedomir
name: Savić, Radomir
name: Karolyi, Danijel
name: Servin, Bertrand
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 2: Figure S1. Plot of admixture cross-validation error from K = 2 to 40. Figure S2. A matrix of missing phenotypic variables in a database of European local pig breeds. The yellow color represents missing variables. SFA = saturated fatty acid content, PUFA = polyunsaturated fatty acid content, Piglets W weight = piglets weaning weight, MUFA = monounsaturated fatty acid content, Litter W birth = litter weaning weight, LD IMF = longissimus dorsi intramuscular fat content, M = male, F = female, Death WN = death rate to weaning, BFT = backfat thickness, GM = gluteus medius muscle, ADG1 = Average daily gain during the lactation period, ADG2 = Average daily gain in the growing period from weaning to 30 kg of weight, ADG3 = Average daily gain during fattening period from 30 to 60 kg. Figure S3. Projection of the (A) stature, (B) growth, (C) fatness and (D) reproduction imputed data obtained with iterative principal component algorithm. Figure S4. Admixture plots of European local pig breeds. The circular plot represents admixture plots for K = 24, K = 20, K = 15, K = 10, and K = 6 (from the outside to the inside of the circle). Figure S5. Comparison of SNP array and DNA-pool sequencing data. Left: allele frequencies estimated from SNP array genotypes vs DNA-pool sequencing data for each local breed. R are the correlation coefficients (all p-values &lt; 1e-16). Right: projection of the DNA-pool sequencing data (allele frequencies) on the PCA established with individual genotypes on the SNP genotyping array. The x-axis is the average coordinate of individuals of each population on each of the PC of the genotyped-based PCA, and the y-axis is the coordinate of the allele frequencies of each population projected on each PCA. Figure S6. Principal component analysis showing the relationship between breeds (A) and the traits associated with growth (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds (A, C) are colored according to genetic similarity. Breeds (A, C) in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S7. Principal component analysis showing the relationship between breeds (A) and the traits associated with fatness (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds (A, C) are colored according to genetic similarity. Breeds (A, C) in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S8. Principal component analysis showing the relationship between breeds (A) and the traits associated with reproduction performance (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds (A, C) are colored according to genetic similarity. Breeds (A, C) in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S9. Distribution of the European local pig breeds according to intramuscular fat content in longissimus dorsi (LD) muscle. Higher values on x-axis represent higher intramuscular fat content, while the lower values represent lower intramuscular fat content. Figure S10. Circular plot summarizing regions with candidate genes for phenotypic groups growth, stature, fatness and reproduction. Discovered regions associated with growth are colored in red, with stature are colored in blue, with fatness are colored in purple and with reproductive performance in green. Figure S11. Principal component analysis showing the relationship between breeds (A) and the traits associated with meat quality (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds (A, C) are colored according to genetic similarity. Breeds (A, C) in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque.