Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.

PURPOSE: Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable. METHODS: We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index. RESULTS: This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways. CONCLUSION: Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

FastLSU: a more practical approach for the Benjamini-Hochberg FDR controlling procedure for huge-scale testing problems.

MOTIVATION: We address a common problem in large-scale data analysis, and especially the field of genetics, the huge-scale testing problem, where millions to billions of hypotheses are tested together creating a computational challenge to control the inflation of the false discovery rate. As a solution we propose an alternative algorithm for the famous Linear Step Up procedure of Benjamini and Hochberg. RESULTS: Our algorithm requires linear time and does not require any P-value ordering. It permits separating huge-scale testing problems arbitrarily into computationally feasible sets or chunks Results from the chunks are combined by our algorithm to produce the same results as the controlling procedure on the entire set of tests, thus controlling the global false discovery rate even when P-values are arbitrarily divided. The practical memory usage may also be determined arbitrarily by the size of available memory. AVAILABILITY AND IMPLEMENTATION: R code is provided in the supplementary material. CONTACT: sbatista@cs.princeton.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix.

We present two novel and explicit parametrizations of Cholesky factor of a nonsingular correlation matrix. One that uses semi-partial correlation coefficients, and a second that utilizes differences between the successive ratios of two determinants. To each, we offer a useful application.

Gene expression in transformed lymphocytes reveals variation in endomembrane and HLA pathways modifying cystic fibrosis pulmonary phenotypes.

Variation in cystic fibrosis (CF) phenotypes, including lung disease severity, age of onset of persistent Pseudomonas aeruginosa (P. aeruginosa) lung infection, and presence of meconium ileus (MI), has been partially explained by genome-wide association studies (GWASs). It is not expected that GWASs alone are sufficiently powered to uncover all heritable traits associated with CF phenotypic diversity. Therefore, we utilized gene expression association from lymphoblastoid cells lines from 754 p.Phe508del CF-affected homozygous individuals to identify genes and pathways. LPAR6, a G protein coupled receptor, associated with lung disease severity (false discovery rate q value = 0.0006). Additional pathway analyses, utilizing a stringent permutation-based approach, identified unique signals for all three phenotypes. Pathways associated with lung disease severity were annotated in three broad categories: (1) endomembrane function, containing p.Phe508del processing genes, providing evidence of the importance of p.Phe508del processing to explain lung phenotype variation; (2) HLA class I genes, extending previous GWAS findings in the HLA region; and (3) endoplasmic reticulum stress response genes. Expression pathways associated with lung disease were concordant for some endosome and HLA pathways, with pathways identified using GWAS associations from 1,978 CF-affected individuals. Pathways associated with age of onset of persistent P. aeruginosa infection were enriched for HLA class II genes, and those associated with MI were related to oxidative phosphorylation. Formal testing demonstrated that genes showing differential expression associated with lung disease severity were enriched for heritable genetic variation and expression quantitative traits. Gene expression provided a powerful tool to identify unrecognized heritable variation, complementing ongoing GWASs in this rare disease.

Heritability and genomics of gene expression in peripheral blood.

We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function or ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which we further examined in a replication set of 1,895 unrelated subjects. A large number of non-redundant local eQTLs (6,756) met replication criteria, whereas a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide a new resource toward understanding the genetic control of transcription.

Sex differences in the human peripheral blood transcriptome.

BACKGROUND: Genomes of men and women differ in only a limited number of genes located on the sex chromosomes, whereas the transcriptome is far more sex-specific. Identification of sex-biased gene expression will contribute to understanding the molecular basis of sex-differences in complex traits and common diseases. RESULTS: Sex differences in the human peripheral blood transcriptome were characterized using microarrays in 5,241 subjects, accounting for menopause status and hormonal contraceptive use. Sex-specific expression was observed for 582 autosomal genes, of which 57.7% was upregulated in women (female-biased genes). Female-biased genes were enriched for several immune system GO categories, genes linked to rheumatoid arthritis (16%) and genes regulated by estrogen (18%). Male-biased genes were enriched for genes linked to renal cancer (9%). Sex-differences in gene expression were smaller in postmenopausal women, larger in women using hormonal contraceptives and not caused by sex-specific eQTLs, confirming the role of estrogen in regulating sex-biased genes. CONCLUSIONS: This study indicates that sex-bias in gene expression is extensive and may underlie sex-differences in the prevalence of common diseases.

seeQTL: a searchable database for human eQTLs.

SUMMARY: seeQTL is a comprehensive and versatile eQTL database, including various eQTL studies and a meta-analysis of HapMap eQTL information. The database presents eQTL association results in a convenient browser, using both segmented local-association plots and genome-wide Manhattan plots. AVAILABILITY AND IMPLEMENTATION: seeQTL is freely available for non-commercial use at http://www.bios.unc.edu/research/genomic_software/seeQTL/. CONTACT: fred_wright@unc.edu; kxia@bios.unc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.