Current Genomics - Volume 17, Issue 5, 2016

Complex diseases are often caused by the function of multiple genes, gene-gene (GxG) interactions as well as gene-environment (GxE) interactions. GxG and GxE interactions are ubiquitous in nature. Empirical evidences have shown that the effect of GxG interaction on disease risk could be largely modified by environmental changes. Such a GxGxE triple interaction could be a potential contributing factor to phenotypic plasticity. Although the role of environmental factors moderating genetic influences on disease risk has been broadly recognized, no statistical method has been developed to rigorously assess how environmental changes modify GxG interactions to affect disease risk. To address this issue, we developed a GxGxE triple interaction model in this work. We modeled the environmental modification effect via a varying-coefficient model where the structure of the varying effect is determined by data. Thus the model has the flexibility to assess nonlinear environmental moderation effect on GxG interaction. Simulation and real data analysis were conducted to show the utility of the method. Our approach provides a quantitative framework to assess triple interactions hypothesized in literature.

Identification of multifactor gene-gene (GxG) and gene-environment (GxE) interactions underlying complex traits poses one of the great challenges to today’s genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http:// www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.

Many complex diseases, such as psychiatric and behavioral disorders, are commonly characterized through various measurements that reflect physical, behavioral and psychological aspects of diseases. While it remains a great challenge to find a unified measurement to characterize a disease, the available multiple phenotypes can be analyzed jointly in the genetic association study. Simultaneously testing these phenotypes has many advantages, including considering different aspects of the disease in the analysis, and utilizing correlated phenotypes to improve the power of detecting disease-associated variants. Furthermore, complex diseases are likely caused by the interplay of multiple genetic variants through complicated mechanisms. Considering gene-gene interactions in the joint association analysis of complex diseases could further increase our ability to discover genetic variants involving complex disease pathways. In this article, we propose a stepwise U-test for joint association analysis of multiple loci and multiple phenotypes. Through simulations, we demonstrated that testing multiple phenotypes simultaneously could attain higher power than testing one single phenotype at a time, especially when there are shared genes contributing to multiple phenotypes. We also illustrated the proposed method with an application to Nicotine Dependence (ND), using datasets from the Study of Addition, Genetics and Environment (SAGE). The joint analysis of three ND phenotypes identified two SNPs, rs10508649 and rs2491397, and reached a nominal P-value of 3.79e-13. The association was further replicated in two independent datasets with P-values of 2.37e-05 and 7.46e-05.

Low or high birth weight is one of the main causes for neonatal morbidity and mortality. They are also associated with adulthood chronic illness. Birth weight is a complex trait which is affected by baby’s genes, maternal environments as well as the complex interactions between them. To understand the genetic basis of birth weight, we reanalyzed a genome-wide association study data set which consists of four populations, namely Thai, Afro-Caribbean, European, and Hispanic population with regular linear models. In addition to fit the data with parametric linear models, we fitted the data with a nonparametric varying-coefficient model to identify variants that are nonlinearly modulated by mother’s condition to affect birth weight. For this purpose, we used baby’s cord glucose level as the mother’s environmental variable. At the 10-5 genome-wide threshold, we identified 33 SNP variants in the Thai population, 26 SNPs in the Afro-Caribbean population, 18 SNPs in the European population, and 7 SNPs in the Hispanic population. Some of the variants are significantly modulated by baby’s cord glucose level either linearly or nonlinearly, implying potential interactions between baby’s gene and mother’s glucose level to affect baby’s birth weight. There is no overlap between variants identified in the four populations, indicating strong genetic heterogeneity of birth weight between the four ethnic groups. The findings of this study provide insights into the genetic basis of birth weight and reveal its genetic heterogeneity.

With the rapid accumulation of gene expression data, gene functional module identification has become a widely used approach in functional analysis. However, tools to identify organelle functional modules and analyze their relationships are still missing. We present a soft thresholding approach to construct networks of functional modules using gene expression datasets, in which nodes are strongly co-expressed genes that encode proteins residing in the same subcellular localization, and links represent strong inter-module connections. Our algorithm has three steps. First, we identify functional modules by analyzing gene expression data. Next, we use a self-adaptive approach to construct a mixed network of functional modules and genes. Finally, we link functional modules that are tightly connected in the mixed network. Analysis of experimental data from Arabidopsis demonstrates that our approach is effective in improving the interpretability of high-throughput transcriptomic data and inferring function of unknown genes.

Analytical models usually assume an additive sex effect by treating it as a covariate to identify genetic associations with sex-influenced traits. Their underlying assumptions are violated by ignoring interactions of sex with genetic factors and heterogeneous genetic effects by sex. Methods to deal with the problems are compared and discussed in this article. Especially, heterogeneity of genetic variance by sex can be assessed employing a mixed model with genetic relationship matrix constructed from genome-wide nucleotide variant information. Estimating genetic architecture of each sex would help understand different prevalence, course, and severity of complex diseases between women and men in the era of personalized medicine.

Hox gene collinearity relates the gene order of the Hox cluster in the chromosome (telomeric to centromeric end) with the serial activation of these genes in the ontogenetic units along the Anterior-Posterior embryonic axis. Although this collinearity property is well respected in bilaterians (e.g. vertebrates), it is violated in other animals. The A-P axis is established in the early embryo of the sea urchin. Subsequently, rotational symmetry is superimposed when the vestibula larva is formed. In analogy to the linear A-P case, it is here hypothesized that the circular topology of the ontogenetic modules is associated to the architectural restructuring of the Hox loci where the two discrete ends of the Hox cluster approach each other so that an almost circular DNA contour is created. In the evolutionary process the circular mode undergoes double strand breaks and the generated cluster ends are attached to the open ends of the flanking chromosome. This event may lead to a novel gene ordering associated with an evolutionary innovation. For example, the loss of Hox4 is followed by the formation of a shorter gene circular arrangement. The opening of this contour at the missing Hox4 location and its connection to the chromosomal flanking ends leads to a new diversification namely the creation of the unusual gene order of the sea urchin Hox cluster.

The association of A1513C (rs3751143) polymorphism of P2X7 gene with the risk of extrapulmonary tuberculosis (EPTB) has been extensively analyzed, but no consensus has been achieved. In this study, a meta-analysis was done to assess this precise association. Online web databases, like PubMed (MEDLINE) and EMBASE were searched for pertinent reports showing association of P2X7 A1513C polymorphism with EPTB risk. To assess the strength of this association, we calculated pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). A total of eight reports involving 2237controls and 594 EPTB cases were included in this study. Four genetic models, viz. allele (C vs. A: p=0.011; OR= 1.677, 95% CI = 1.125–2.501), homozygous (CC vs. AA: p = 0.053; OR= 2.362, 95% CI = 0.991–5.632), heterozygous (AC vs. AA: p = 0.003; OR= 1.775, 95% CI = 1.209–2.607) and dominant (CC + AC vs. AA: p = 0.005; OR= 1.890, 95% CI = 1.207–2.962) showed significant associations compared with wild type genotypes. Subgroup analysis stratified by ethnicity was also performed and the results suggested that homozygous and heterozygous genotypes were associated significantly with increased susceptibility of EPTB in Asian population. Similarly, heterozygous and dominant models showed increased EPTB risk in Caucasian population. The present meta-analysis suggests that P2X7 A1513C polymorphism may be an important risk factor for EPTB. Also, our sub-group analysis indicates that P2X7 A1513C polymorphism confers increased EPTB risk among Asians and Caucasians. However, future larger studies are needed to provide more precise conclusion and endorse the present results.