Anti-PSRC1 monoclonal antibody Made to order

We conducted dense linkage disequilibrium (LD) mapping of a series of 25 genes putatively involved in lipid metabolism in 1567 dementia cases [including 1270 with Alzheimer disease (AD)] and 2203 Swedish controls. Across a total of 448 tested genetic markers, the strongest evidence of association was as anticipated for APOE (rs429358 at P similar to 10(-72)) followed by a previously reported association of ABCA1 (rs2230805 at P similar to 10(-8)). In the present study, we report two additional markers near the SREBF1 locus on chromosome 17p that were also significant after multiple testing correction (best P = 3.1 x 10(-6) for marker rs3183702). There was no convincing evidence of association for remaining genes, including candidates highlighted from recent genome-wide association studies of plasma lipids (CELSR2/PSRC1/SORT1, MLXIPL, PCSK9, GALNT2 and GCKR). The associated markers near SREBF1 reside in a large LD block, extending more than 400 kb across seven candidate genes. Secondary analyses of gene expression levels of candidates spanning the LD region together with an investigation of gene network context highlighted two possible susceptibility genes including ATPAF2 and TOM1L2. Several markers in strong LD (r(2) > 0.7) with rs3183702 were found to be significantly associated with AD risk in recent genome-wide association studies with similar effect sizes, providing independent support of the current findings.

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.