Genetic association studies of SLC6A4 (SERT) and obsessive-compulsive disorder (OCD) have been equivocal. We genotyped 1241 individuals in 278 pedigrees from the OCD Collaborative Genetics Study for 13 single-nucleotide polymorphisms, for the linked polymorphic region (LPR) indel with molecular haplotypes at rs25531, for VNTR polymorphisms in introns 2 and 7 and for a 381-bp deletion 3 0 to the LPR. We analyzed using the FamilyBased Association Test (FBAT) under additive, dominant, recessive and genotypic models, using both OCD and sex-stratified OCD as phenotypes. Two-point FBAT analysis detected association between Int2 (P = 0.0089) and Int7 (P = 0.0187) (genotypic model). Sex-stratified two-point analysis showed strong association in females with Int2 (P < 0.0002), significant after correction for linkage disequilibrium, and multiple marker and model testing (P Adj = 0.0069). The SLC6A4 gene is composed of two haplotype blocks (our data and the HapMap); FBAT whole-marker analysis conducted using this structure was not significant. Several noteworthy nonsignificant results have emerged. Unlike Hu et al., we found no evidence for overtransmission of the LPR L A allele (genotype relative risk = 1.11, 95% confidence interval: 0.77-1.60); however, rare individual haplotypes containing L A with P < 0.05 were observed. Similarly, three individuals (two with OCD/OCPD) carried the rare I425V SLC6A4 variant, but none of them passed it on to their six OCD-affected offspring, suggesting that it is unlikely to be solely responsible for the 'OCD plus syndrome', as reported by Ozaki et al. In conclusion, we found evidence of genetic association at the SLC6A4 locus with OCD. A noteworthy lack of association at the LPR, LPR-rs25531 and rare 425V variants suggests that hypotheses about OCD risk need revision to accommodate these new findings, including a possible gender effect.
Link functions and random effects structures are the two important components in building flexible regression models for dependent ordinal data. The power link functions include the commonly used links as special cases but have an additional skewness parameter, making the probability response curves adaptive to the data structure. It overcomes the arbitrary symmetry assumption imposed by the commonly used logistic or probit links as well as the fixed skewness in the complementary log–log or log–log links. By employing Gaussian processes, the regression model can incorporate various dependence structures in the data, such as temporal and spatial correlations. The full Bayesian estimation of the proposed model is conveniently implemented through RStan. Extensive simulation studies are carried out for discussion in model computation, parameterization, and evaluation in terms of estimation bias and overall model performance. The proposed model is applied to the PM2.5 data in Beijing and the Berberis thunbergii abundance data in New England. The results suggest that the proposed model leads to important improvement in estimation and prediction in modeling dependent ordinal response data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.