Stratify or adjust? Dealing with multiple populations when evaluating rare variants

Association of the NEGR1 rs2815752 with obesity and related traits in Pakistani females

Introduction: The variant NEGR1 rs2815752 has recently been linked with obesity in Caucasians. However, a very limited number of studies have examined the association of the NEGR1 rs2815752 with overweight/obesity in non-Caucasians with no such study ever performed in Pakistani population. Therefore, the present study was undertaken to seek the association of the rs2815752 with overweight, obesity, and related traits in Pakistanis.Subjects and methods: The study involved 112 overweight/control pairs (total 224) and 194 obese/control pairs (total 388). Anthropometric parameters were measured by employing standard procedures. Metabolic parameters were determined by biochemical assays. Behavioral information was collected through a questionnaire. The rs2815752 was genotyped via TaqMan allelic discrimination assay. Regression analyses were employed to analyze the data in SPSS software.Results: The study revealed significant gender-specific association of the rs2815752 with obesity (OR 3.03; CI 1.19-7.72, p = 0.020) and some obesity-related anomalous anthropometric traits (weight, BMI, waist circumference, hip circumference, and abdominal and supra-iliac skinfold thicknesses) in females according to dominant model (h = 0.023). However, no association of the rs2815752 with obesity-related behavioral and metabolic parameters was observed.Conclusion: The NEGR1 rs2815752 may be associated with obese phenotype and some of the related anthropometric traits in Pakistani females.

Determining population stratification and subgroup effects in association studies of rare genetic variants for nicotine dependence

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Background

Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of ‘missing heritability’ and have a proven role in dissecting the etiology for human diseases and complex traits.

Methods

We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as ‘str-CMC’ and ‘str-WSS’. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran–Mantel–Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes.

Results

The Cochran–Mantel–Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O.

Conclusions

Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.

Serious suicide attempts in outpatients with multiple substance use disorders

BACKGROUND

Suicide is a major public health concern and suicide attempts (SA) are frequent and burdensome in people suffering from substance use disorders (SUDs). In particular, serious SAs are a preoccupying form of attempt, which remain largely overlooked in these populations, especially regarding basic risk factors such as gender, addictive comorbidity and substance use patterns. Thus, we undertook a gender-specific approach to identify the risk factors for serious SAs in outpatients with multiple SUDs.

MATERIAL AND METHODS

433 Treatment-seeking outpatients were consecutively recruited in specialized care centers and reliably classified as serious, non-serious and non-suicide attempters. We also characterized lifetime exposure to SUDs, including tobacco smoking, with standardized instruments. Current medication, including psychotropic treatments were collected, which informed psychiatric diagnoses. Multinomial regression identified independent factors specifically associated with serious SAs in each gender, separately.

RESULTS

32% Participants (N=139, 47% Women and 27% Men) reported lifetime SA. There were 82 serious attempters (59% of attempters), without significant gender difference. Sedative dependence was an independent risk factor for serious SA compared to non-SA in Women and compared to non-serious SA in Men, respectively. Other risk factors included later onset of daily tobacco smoking in Men and history of psychiatric hospitalizations in Women, whose serious SA risk was conversely lower when reporting opiate use disorder or mood disorder, probably because of treatment issues.

CONCLUSIONS

Despite several study limitations, we identified subgroups for a better-tailored prevention of serious SAs among individuals with SUDs, notably highlighting the need to better prevent and treat sedative dependence.

Linkage analysis merging replicate phenotypes: an application to three quantitative phenotypes in two African samples

We report two approaches for linkage analysis of data consisting of replicate phenotypes. The first approach is specifically designed for the unusual (in human data) replicate structure of the Genetic Analysis Workshop 17 pedigree data. The second approach consists of a standard linkage analysis that, although not specifically tailored to data consisting of replicate genotypes, was envisioned as providing a sounding board against which our novel approach could be assessed. Both approaches are applied to the analysis of three quantitative phenotypes (Q1, Q2, and Q4) in two sets of African families. All analyses were carried out blind to the generating model (i.e., the “answers”). Using both methods, we found numerous significant linkage signals for Q1, although population colocalization was absent for most of these signals. The linkage analysis of Q2 and Q4 failed to reveal any strong linkage signals.

Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini-exome data

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.