A Weighted U Statistic for Genetic Association Analyses of Sequencing Data

With advancements in next generation sequencing technology, a massive amount of sequencing data are generated, offering a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, this poses a great challenge for the statistical analysis of high-dimensional sequencing data. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a weighted U statistic, referred to as WU-seq, for the high-dimensional association analysis of sequencing data. Based on a non-parametric U statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used SKAT method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-seq to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol

From classical mendelian randomization to causal networks for systematic integration of multi-omics

The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data

Mutational landscape of candidate genes in familial prostate cancer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108266/1/pros22849-sm-0001-SupTab-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108266/2/pros22849.pd

Aromatase inhibitors, estrogens and musculoskeletal pain: estrogen-dependent T-cell leukemia 1A (TCL1A) gene-mediated regulation of cytokine expression

Introduction: Arthralgias and myalgias are major side effects associated with aromatase inhibitor (AI) therapy of breast cancer. In a recent genome-wide association study, we identified SNPs - including one that created an estrogen response element near the 3' end of the T-cell leukemia 1A (TCL1A) gene - that were associated with musculoskeletal pain in women on adjuvant AI therapy for breast cancer. We also showed estrogen-dependent, SNP-modulated variation in TCL1A expression and, in preliminary experiments, showed that TCL1A could induce IL-17RA expression. In the present study, we set out to determine whether these SNPs might influence cytokine expression and effect more widely, and, if so, to explore the mechanism of TCL1A-related AI-induced side effects. Methods: The functional genomic experiments performed included determinations of TCL1A, cytokine and cytokine receptor expression in response to estrogen treatment of U2OS cells and lymphoblastoid cell lines that had been stably transfected with estrogen receptor alpha. Changes in mRNA and protein expression after gene knockdown and overexpression were also determined, as was NF-κB transcriptional activity. Results: Estradiol (E2) increased TCL1A expression and, in a TCL1A SNP-dependent fashion, also altered the expression of IL-17, IL-17RA, IL-12, IL-12RB2 and IL-1R2. TCL1A expression was higher in E2-treated lymphoblastoid cell lines with variant SNP genotypes, and induction of the expression of cytokine and cytokine receptor genes was mediated by TCL1A. Finally, estrogen receptor alpha blockade with ICI-182,780 in the presence of E2 resulted in greatly increased NF-κB transcriptional activity, but only in cells that carried variant SNP genotypes. These results linked variant TCL1A SNP sequences that are associated with AI-dependent musculoskeletal pain with increased E2-dependent TCL1A expression and with downstream alterations in cytokine and cytokine receptor expression as well as NF-κB transcriptional activity. Conclusions: SNPs near the 3' terminus of TCL1A were associated with AI-dependent musculoskeletal pain. E2 induced SNP-dependent TCL1A expression, which in turn altered IL-17, IL-17RA, IL-12, IL-12RB2, and IL-1R2 expression as well as NF-κB transcriptional activity. These results provide a pharmacogenomic explanation for a clinically important adverse drug reaction as well as insights into a novel estrogen-dependent mechanism for the modulation of cytokine and cytokine receptor expression

Factor VIII gene inversions causing severe hemophilia A originate almost exclusively in male germ cells

The factor VIII gene, which is defective In hemophilia A, is located in the last megabase of the long arm of the X chromosome. Inversions due to intrachromosomal homologous recombination between mispaired copies of gene A located within intron 22 of the gene and about 500 kb telomeric to it account for nearly half of all cases of severe hemophilia A. We hypothesized that pairing of Xq with its homolog inhibits the Inversion process, and that, therefore, the event originates predominantly in male germ cells. In all 20 informative cases In which the inversion originated in a maternal grandparent, DNA polymorphism analysis determined that it occurred in the male germline. In addition, all but one of 50 mothers of sporadic cases due to an Inversion were carriers. Thus, these data support the hypothesis and Indicate that factor VIII gene inversions leading to severe hemophilia A occur almost exclusively In male germ cell