SummaryThe questions addressed in this paper are: What single nucleotide polymorphism (SNP) genotyping errors are most costly, in terms of minimum sample size necessary (MSSN) to maintain constant asymptotic power and significance level, when performing case-control studies of genetic association applying the Cochran-Armitage trend test? And which trend test or χ 2 test is more powerful under standard genetic models with genotyping errors? Our strategy is to expand the non-centrality parameter of the asymptotic distribution of the trend test to approximate the MSSN using a Taylor series linear in the genotyping error rates. We apply our strategy to example scenarios that assume recessive, dominant, additive, or over-dominant disease models.The most costly errors are recording the more common homozygote as the less common homozygote, and the more common homozygote as the heterozygote, with MSSN that become indefinitely large as the minor SNP allele frequency approaches zero. Misclassifying the heterozygote as the less common homozygote is costly when using the recessive trend test on data from a recessive model. The χ 2 test has power close to, but less than, the optimal trend test and is never dominated over all genetic models studied by any specific trend test.

BackgroundKnowledge of the genetic etiology of epilepsy can provide essential prognostic information and influence decisions regarding treatment and management, leading us into the era of precision medicine. However, the genetic basis underlying epileptogenesis or epilepsy pharmacoresistance is not well-understood, particularly in non-familial epilepsies with heterogeneous phenotypes that last until or start in adulthood.MethodsWe sought to determine the contribution of known epilepsy-associated genes (EAGs) to the causation of non-familial epilepsies with heterogeneous phenotypes and to the genetic basis underlying epilepsy pharmacoresistance. We performed a multi-center study for whole exome sequencing-based screening of 178 selected EAGs in 243 non-familial adult patients with primarily focal epilepsy (122 drug-resistant and 121 drug-responsive epilepsies). The pathogenicity of each variant was assessed through a customized stringent filtering process and classified according to the American College of Medical Genetics and Genomics guidelines.ResultsPossible causal genetic variants of epilepsy were uncovered in 13.2% of non-familial patients with primarily focal epilepsy. The diagnostic yield according to the seizure onset age was 25% (2/8) in the neonatal and infantile period, 11.1% (14/126) in childhood and 14.7% (16/109) in adulthood. The higher diagnostic yields were from ion channel-related genes and mTOR pathway-related genes, which does not significantly differ from the results of previous studies on familial or early-onset epilepsies. These potentially pathogenic variants, which were identified in genes that have been mainly associated with early-onset epilepsies with severe phenotypes, were also linked to epilepsies that start in or last until adulthood in this study. This finding suggested the presence of one or more disease-modifying factors that regulate the onset time or severity of epileptogenesis. The target hypothesis of epilepsy pharmacoresistance was not verified in our study. Instead, neurodevelopment-associated epilepsy genes, such as TSC2 or RELN, or structural brain lesions were more strongly associated with epilepsy pharmacoresistance.ConclusionsWe revealed a fraction of possible causal genetic variants of non-familial epilepsies in which genetic testing is usually overlooked. In this study, we highlight the importance of earlier identification of the genetic etiology of non-familial epilepsies, which leads us to the best treatment options in terms of precision medicine and to future neurobiological research for novel drug development. This should be considered a justification for physicians determining the hidden genetics of non-familial epilepsies that last until or start in adulthood.

Heavy cannabis use is associated with a wide array of physical, mental, and functional problems. Therefore, cannabis use disorders (CUDs) may be a major public health concern. Given the adverse health consequences of CUDs, the present study seeks to find possible precursors of CUDs. The current study consisted of 5 waves of data collection from the Harlem Longitudinal Development Study. Among 816 participants, about half are African Americans (52%), and the other half are Puerto Ricans (48%). We used Mplus to obtain the triple trajectories of alcohol use, tobacco use, and depressive symptoms. Logistic regression analyses were then conducted to examine the associations between the trajectory groups and CUDs. The 5 trajectory groups were (1) moderate alcohol use, high tobacco use, and high depressive symptoms (MHH; 12%); (2) moderate alcohol use, high tobacco use, and low depressive symptoms (MHL; 26%); (3) moderate alcohol use, low tobacco use, and low depressive symptoms (MLL; 18%); (4) low alcohol use, no tobacco use, and high depressive symptoms (LNH; 11%); and (5) low alcohol use, no tobacco use, and low depressive symptoms (LNL; 33%). The MHH, MHL, MLL, and LNH trajectory groups were associated with an increased likelihood of having CUDs compared to the LNL trajectory group after controlling for a number of confounding factors (e.g., CUDs in the late 20s). The findings of the current longitudinal study suggest that treatments designed to reduce or quit drinking as well as smoking and to relieve depressive symptoms may reduce the prevalence of CUDs. (PsycINFO Database Record

Recent studies suggest that copy number polymorphisms (CNPs) may play an important role in disease susceptibility and onset. Currently, the detection of CNPs mainly depends on microarray technology. For case-control studies, conventionally, subjects are assigned to a specific CNP category based on the continuous quantitative measure produced by microarray experiments, and cases and controls are then compared using a chi-square test of independence. The purpose of this work is to specify the likelihood ratio test statistic (LRTS) for case-control sampling design based on the underlying continuous quantitative measurement, and to assess its power and relative efficiency (as compared to the chi-square test of independence on CNP counts). The sample size and power formulas of both methods are given. For the latter, the CNPs are classified using the Bayesian classification rule. The LRTS is more powerful than this chi-square test for the alternatives considered, especially alternatives in which the at-risk CNP categories have low frequencies. An example of the application of the LRTS is given for a comparison of CNP distributions in individuals of Caucasian or Taiwanese ethnicity, where the LRTS appears to be more powerful than the chi-square test, possibly due to misclassification of the most common CNP category into a less common category.