A fast algorithm for detecting gene-gene interactions in genome-wide association studies

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS771 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Identification of quantitative trait loci controlling root and shoot traits associated with drought tolerance in a lentil (Lens culinaris Medik.) recombinant inbred line population

Drought is one of the major abiotic stresses limiting lentil productivity in rainfed production systems. Specific rooting patterns can be associated with drought avoidance mechanisms that can be used in lentil breeding programs. In all, 252 co-dominant and dominant markers were used for Quantitative Trait Loci (QTL) analysis on 132 lentil recombinant inbred lines based on greenhouse experiments for root and shoot traits during two seasons under progressive drought-stressed conditions. Eighteen QTLs controlling a total of 14 root and shoot traits were identified. A QTL-hotspot genomic region related to a number of root and shoot characteristics associated with drought tolerance such as dry root biomass, root surface area, lateral root number, dry shoot biomass and shoot length was identified. Interestingly, a QTL (QRSratioIX-2.30) related to root-shoot ratio, an important trait for drought avoidance, explaining the highest phenotypic variance of 27.6 and 28.9% for the two consecutive seasons, respectively, was detected. This QTL was closed to the co-dominant SNP marker TP6337 and also flanked by the two SNP TP518 and TP1280. An important QTL (QLRNIII-98.64) related to lateral root number was found close to TP3371 and flanked by TP5093 and TP6072 SNP markers. Also, a QTL (QSRLIV-61.63) associated with specific root length was identified close to TP1873 and flanked by F7XEM6b SRAP marker and TP1035 SNP marker. These two QTLs were detected in both seasons. Our results could be used for marker-assisted selection in lentil breeding programs targeting root and shoot characteristics conferring drought avoidance as an efficient alternative to slow and labor-intensive conventional breeding methods

Use of easy measurable phenotypic traits as a complementary approach to evaluate the population structure and diversity in a high heterozygous panel of tetraploid clones and cultivars

Diversity in crops is fundamental for plant breeding efforts. An accurate assessment of genetic diversity, using molecular markers, such as single nucleotide polymorphism (SNP), must be able to reveal the structure of the population under study. A characterization of population structure using easy measurable phenotypic traits could be a preliminary and low-cost approach to elucidate the genetic structure of a population. A potato population of 183 genotypes was evaluated using 4859 high-quality SNPs and 19 phenotypic traits commonly recorded in potato breeding programs. A Bayesian approach, Minimum Spanning Tree (MST) and diversity estimator, as well as multivariate analysis based on phenotypic traits, were adopted to assess the population structure. Results: Analysis based on molecular markers showed groups linked to the phylogenetic relationship among the germplasm as well as the link with the breeding program that provided the material. Diversity estimators consistently structured the population according to a priori group estimation. The phenotypic traits only discriminated main groups with contrasting characteristics, as different subspecies, ploidy level or membership in a breeding program, but were not able to discriminate within groups. A joint molecular and phenotypic characterization analysis discriminated groups based on phenotypic classification, taxonomic category, provenance source of genotypes and genetic background. Conclusions: This paper shows the significant level of diversity existing in a parental population of potato as well as the putative phylogenetic relationships among the genotypes. The use of easily measurable phenotypic traits among highly contrasting genotypes could be a reasonable approach to estimate population structure in the initial phases of a potato breeding program.Fil: Tagliotti, Martin Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Deperi, Sofía Irene. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Bedogni, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Zhang, Ruofang. Inner Mongolia University; ChileFil: Manrique Carpintero, Norma C.. Michigan State University; Estados UnidosFil: Coombs, Joseph. Michigan State University; Estados UnidosFil: Douches, David. Michigan State University; Estados UnidosFil: Huarte, Marcelo Atilio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentin