Multivariate genome-wide associations for immune traits in two maternal pig lines

Abstract

Background: Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines.

Results: In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified.

Conclusions: This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

Keywords: Animal Genetics; Genome-wide Association Studies; Immune traits; Immunocompetence; Multivariate; Pigs.

Fig. 1

Bayesian Network for immune trait residuals RBC = Red blood cells, HMG = Hemoglobin, HMT = Hematocrit, MCV = Mean Corpuscular Volume, MCH = Mean Corpuscular Hemoglobin, MCHC = Mean Corpuscular Hemoglobin Concentration, PLT = Platelets, WBC = White blood cells, NEU = Neutrophils, LYM = Lymphocytes, MON = Monocytes, EOS = Eosinophils, BAS = Basophils, HAP = Haptoglobin, IFN = Interferon-γ, IL = Interleukin, TNF = Tumor Necrosis Factor-α. Functional biological networks of phenotypes are illustrated as nodes in pale blue for WBC, light red for RBC, and yellow for cytokines. Node frames are highlighted in red to highlight conditionally independent variables. Colored arrows are used to indicate parental relationships of the nodes in the structured model learned from the data sets

Fig. 2

Venn diagram of different methods used to detect significant multivariate associations summerized for both breeds and significance types PCA = Principal component analysis, CCA = Canonical correlation analysis, TATES = Trait-based Association Test that uses Extended Simes procedure, mvBIMBAM = multivariate Bayesian IMputation-Based Association Mapping. Multiple identical significant SNPs for different immune traits within a method are counted once

Fig. 3

Manhattan plot of SSC 5 for multivariate trait combinations a RBC|HMG: HMT:MCV:MCH in Landrace with CCA, b HMG|MCHC:IL10 in Landrace with CCA, and c WBC|RBC:HAP:IL1b in Large White with mvBIMBAM RBC = Red blood cells, HMG = Hemoglobin, HMT = Hematocrit, MCV = Mean Corpuscular Volume, MCH = Mean Corpuscular Hemoglobin, MCHC = Mean Corpuscular Hemoglobin Concentration, IL = Interleukin, WBC = White blood cells, HAP = Haptoglobin, SNPs of interest are highlighted with green color (a DRGA0005609 (rs80847233), ASGA0025326 (rs80801793, SSC 5: 31.27 Mbp), ALGA0031690 (rs80785563, SSC 5: 33.95 Mbp), MARC0021861 (rs80948498), DRGA0005776 (rs336848545, SSC 5: 43.22 Mbp), b ALGA0031924 (rs80949260, SSC 5: 48.90 Mbp), MARC0001027 (rs81284886, SSC 5: 50.09 Mbp), ALGA0032074 (rs80787531, SSC 5: 58.60 Mbp), and c MARC0013873 (rs80911910)). Protein coding genes within annotated QTLs between 23.93 and 97.48 Mbp are stated in the box

Fig. 4

Genetic markers identified with GWAS approaches: Comparison of different association methods for both investigated breeds Multiple identical significant SNPs for different immune traits within a method are counted a single time. mv = multivariate, uv = univariate, LR = Landrace, LW = Large White