The genetic makeup of a person can be thought of as shaping their propensity to complex diseases, while environmental factors trigger onset of diseases and, together with genetic factors, can modify their progression. Research of my group reflects our continuing involvement in the design and analysis of large-scale genetic and genomic studies. We continue to devise methodology that is useful not only for genetic applications but also generally applicable for analysis of other kinds of multidimensional data where many statistical hypotheses are being evaluated, e.g. it is applicable in studies of epigenetic effects of exposures, in metabolomics studies, and in studies of differential gene expression. Our methods are designed to be general because they work off P‑values, which can result from various tests, including those accounting for environmental exposures and gene‑environment interactions. We have been extending collaborative aspects of our research, striving to bring modern statistical methodology to studies concerned with complex diseases and environmental exposures. In Slade et al (2013) we conducted a case-control genetic association study of relationships between 2925 SNPs and two subtypes of a commonly occurring chronic facial pain condition, temporomandibular disorder (TMD): 1) localized TMD;and 2) TMD with widespread pain. Using our statistical extensions to pathway analysis we found that when compared to healthy controls, cases with localized TMD differed in allelic frequency of SNPs that mapped to a serotonergic receptor pathway, while cases of TMD with widespread pain differed in allelic frequency of SNPs that mapped to a T-cell receptor pathway. A risk index representing combined effects of six SNPs from the serotonergic pathway was associated with greater odds of localized TMD, and the result was reproduced in a replication case-control cohort study. A risk index representing combined effects of eight SNPs from the T-cell receptor pathway was associated with greater odds of TMD with widespread pain. The replicated association between serotonergic signaling and localized TMD represents a novel finding regarding etiology of chronic pain, while the observed association between T cell receptor pathway and TMD with widespread pain corroborates recent studies of the role of T cell-mediated mechanisms in pain. Common genetic variants usually contribute to molecular pathophysiological processes in ways that produce weak associations between individuals SNPs and clinical disease when assessed in human association studies. In this research, we distinguished phenotypically between biologically relevant subtypes of chronic TMD and applied a new analytic approach that searches for combinations of usually small genetic influences on known cellular signaling pathways. We found that anatomically localized and generalized pain represent clinically meaningful subgroups of TMD and that they have distinct molecular profiles with correspondingly-distinct genetic backgrounds. The results reveal a distinct role for serotonergic pathways in pathophysiology of TMD, although further work is needed to uncover the direction of this contribution. Potentially, this major cellular pathway contributing to development of localized TMD might be effectively treated using serotonin receptor 2 selective ligands. The TMD project prompted us to start looking into methodology for designs where the second (e.g. replication) part of the study can take advantage of effect directions observed in the primary study (Kuo et al., 2013). The primary study gives information about which allele at each SNP increases susceptibility to disease (i.e. the effect direction). Thus, for a particular individual in the replication sample, the number of susceptibility alleles can be added for each associated SNP, resulting in a single score. A statistic based on such a score would be independent of an association statistic used in the primary study, even though it borrows effect directions of the primary study, as long as the test utilized in the primary study is two-sided (i.e. oblivious to the effect direction). Therefore, association statistics of the primary and the secondary (e.g. replication) studies can be added (with weights reflecting the study sizes), and the distribution of the weighted sum can be determined. Our previous research suggests that careful pooling of summary statistics from different samples can be essentially as powerful as analysis based on individual-level data. Moreover, it is possible that in some situations, the test based on a combination the two statistics (from the primary and the secondary study) can be more powerful than a single test based on all data. Some reasons for this include incorporation of additional information that results from borrowing effect directions from the primary study and the fact that the score test in the secondary study is a single degree of freedom test. If all effect directions observed in the primary study were always correct, the secondary study test based on a single score would have had a remarkably high statistical power. However, due to statistical uncertainty, some effect directions may be incorrect. The variability in the number of correct determinations is also affected by linkage disequilibrium. Therefore, we investigated to what extent the power would be affected by these uncertainties. We also studied the problem of optimal allocation of subjects to the the primary and the secondary study with regard to power.

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O'Brien, Katie M; Shi, Min; Sandler, Dale P et al. (2016) A family-based, genome-wide association study of young-onset breast cancer: inherited variants and maternally mediated effects. Eur J Hum Genet 24:1316-23
Vsevolozhskaya, Olga A; Zaykin, Dmitri V; Barondess, David A et al. (2016) Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models. Genet Epidemiol 40:210-21
Vsevolozhskaya, Olga A; Greenwood, Mark C; Powell, Scott L et al. (2015) Resampling-based multiple comparison procedure with application to point-wise testing with functional data. Environ Ecol Stat 22:45-59
Weinberg, C R; Zaykin, D (2015) Is bad luck the main cause of cancer? J Natl Cancer Inst 107:
Kuo, Chia-Ling; Vsevolozhskaya, Olga A; Zaykin, Dmitri V (2015) Assessing the Probability that a Finding Is Genuine for Large-Scale Genetic Association Studies. PLoS One 10:e0124107
Wieskopf, Jeffrey S; Mathur, Jayanti; Limapichat, Walrati et al. (2015) The nicotinic α6 subunit gene determines variability in chronic pain sensitivity via cross-inhibition of P2X2/3 receptors. Sci Transl Med 7:287ra72
Weinberg, Clarice R; Zaykin, Dmitri (2015) Response. J Natl Cancer Inst 107:
Meloto, Carolina B; Segall, Samantha K; Smith, Shad et al. (2015) COMT gene locus: new functional variants. Pain 156:2072-83
Cinghu, Senthilkumar; Yellaboina, Sailu; Freudenberg, Johannes M et al. (2014) Integrative framework for identification of key cell identity genes uncovers determinants of ES cell identity and homeostasis. Proc Natl Acad Sci U S A 111:E1581-90
Kuo, Chia-Ling; Zaykin, Dmitri (2013) The ranking probability approach and its usage in design and analysis of large-scale studies. PLoS One 8:e83079

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