Genetic association and epistatic interaction of the interleukin-10 signaling pathway in pediatric inflammatory bowel disease

To study the genetic association and epistatic interaction of the interleukin (IL)-10 and IL-10/STAT3 pathways in pediatric inflammatory bowel disease (IBD). METHODSA total of 159 pediatric inflammatory IBD patients (Crohn’s disease, n = 136; ulcerative colitis, n = 23) and 129 matched controls were studied for genetic association of selected single nucleotide polymorphisms (SNPs) of the IL-10 gene and the genes IL10RA, IL10RB, STAT3, and HO1, from the IL-10/STAT3 signaling pathway. As interactions between SNPs from different loci may significantly affect the associated risk for disease, additive (a) and dominant (d) modeling of SNP interactions was also performed to examine high-order epistasis between combinations of the individual SNPs. RESULTSThe results showed that IL-10 rs304496 was associated with pediatric IBD (P = 0.022), but no association was found for two other IL-10 SNPs, rs1800872 and rs2034498, or for SNPs in genes IL10RA, IL10RB, STAT3, and HO1. However, analysis of epistatic interaction among these genes showed significant interactions: (1) between two IL-10 SNPs rs1800872 and rs3024496 (additive-additive P = 0.00015, Bonferroni P value (Bp) = 0.003); (2) between IL-10RB rs2834167 and HO1 rs2071746 (dominant-additive, P = 0.0018, Bp = 0.039); and (3) among IL-10 rs1800872, IL10RB rs2834167, and HO1 rs2071746 (additive-dominant-additive, P = 0.00015, Bp = 0.005), as well as weak interactions among IL-10 rs1800872, IL-10 rs3024496, and IL-10RA (additive-additive-additive, P = 0.003; Bp = 0.099), and among IL10RA, IL10RB, and HO1 genes (additive-dominant-additive, P = 0.008, Bp = 0.287). CONCLUSIONThese results indicate that both the IL-10 gene itself, and through epistatic interaction with genes within the IL-10/STAT3 signaling pathway, contribute to the risk of pediatric IBD.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

Genome-Wide Interaction-Based Association of Human Diseases—A Survey

Genome-Wide Association Studies（GWASs） aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms（SNPs）. Although traditional single-locus statistical approaches have been standardized and led to many interesting findings, a substantial number of recent GWASs indicate that for most disorders, the individual SNPs explain only a small fraction of the genetic causes. Consequently, exploring multi-SNPs interactions in the hope of discovering more significant associations has attracted more attentions. Due to the huge search space for complicated multilocus interactions, many fast and effective methods have recently been proposed for detecting disease-associated epistatic interactions using GWAS data. In this paper, we provide a critical review and comparison of eight popular methods, i.e., BOOST, TEAM, epi Forest, EDCF, SNPHarvester, epi MODE, MECPM, and MIC, which are used for detecting gene-gene interactions among genetic loci. In views of the assumption model on the data and searching strategies, we divide the methods into seven categories. Moreover, the evaluation methodologies,including detecting powers, disease models for simulation, resources of real GWAS data, and the control of false discover rate, are elaborated as references for new approach developers. At the end of the paper, we summarize the methods and discuss the future directions in genome-wide association studies for detecting epistatic interactions.