Tanushree Haldar

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Despite numerous use cases, enterprise-wide implementations of blockchains have seen limited success. This raises the question of when do firms adopt blockchains and do blockchains benefit supply chains. To answer this, we examine a dyadic supply chain consisting of a buyer and a supplier and analyze their traceability effort and pricing decisions. Our results show that the demand-side, supply-side and reputational factors influencing blockchain adoption are primarily complementary and in the absence of one of them, firms can still adopt blockchain. Furthermore, even in the absence of individual benefits for a supply chain partner, there exist conditions under which blockchain adoption benefits the supply chain that can incentivize players to join blockchain. Overall, we contribute by offering a framework that supply chain players can use to assess the likelihood of blockchain implementation success or failure and address the challenges pertaining to incentives and cost imbalances in blockchain implementation.

Abstract In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genome-wide significance ( SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB , and SMARCA4 / LDLR ). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance ( LPA , APOE , and SLCO1B1 ). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses ( SORT/CELSR2/PSRC1, APOB, SMARCA4 / LDLR ) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change.

It is now well established that population stratification can result in spurious association findings in genetic case-control studies. However, very few studies have addressed similar issues for mapping quantitative traits. Because quantitative phenotypes are often precursors of clinical endpoint traits and carry more information on within-genotype trait variability, it has been argued that studying these quantitative traits may be a more powerful strategy to map genes than the binary clinical endpoints. Thus, it is of interest to evaluate the adverse effects of population stratification on the analyses of quantitative traits. The popular statistical tests of association for quantitative traits using population level data are ANOVA, linear regression with an additive allelic effect and Kruskal-Wallis. We have theoretically studied the marginal effects of genetic heterogeneity and phenotypic heterogeneity as well as their joint effects on the false positive rates of these three tests. We have carried out extensive simulations under different genetic models and probability distributions of quantitative traits to assess the rate of false positives in the presence of population stratification. We find that the rate of false positives increases very quickly with simultaneous increase in differences in the standardized phenotypic means and marker allele frequencies in the subpopulations.