Testing for direct genetic effects using a screening step in family-based association studies

In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are tested for a direct effect in the family-based study, a screening step can be used to minimize the burden of multiple comparisons in the causal analysis. We propose a 2-stage screening step that can be incorporated into the family-based association test (FBAT) approach similar to the conditional mean model approach in the Van Steen-algorithm (Van Steen et al., 2005). Simulations demonstrate that the type 1 error is preserved and this method is advantageous when multiple markers are tested. This method is illustrated by an application to the Framingham Heart Study

An alternative hypothesis testing strategy for secondary phenotype data in case-control genetic association studies

Motivated by the challenges associated with accounting for the ascertainment when analyzing secondary phenotypes that are correlated with case-control status, Lin and Zeng have proposed a method that properly reflects the case-control sampling (Lin and Zeng, 2009). The Lin and Zeng method has the advantage of accurately estimating effect sizes for secondary phenotypes that are normally distributed or dichotomous. This method can be computationally intensive in practice under the null hypothesis when the likelihood surface that needs to be maximized can be relatively flat. We propose an extension of the Lin and Zeng method for hypothesis testing that uses proportional odds logistic regression to circumvent these computational issues. Through simulation studies, we compare the power and type-1 error rate of our method to standard approaches and Lin and Zeng's approach

A general semi-parametric approach to the analysis of genetic association studies in population-based designs

Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others

Restoring mitofusin balance prevents axonal degeneration in a Charcot-Marie-Tooth type 2A model

Mitofusin-2 (MFN2) is a mitochondrial outer-membrane protein that plays a pivotal role in mitochondrial dynamics in most tissues, yet mutations in MFN2, which cause Charcot-Marie-Tooth disease type 2A (CMT2A), primarily affect the nervous system. We generated a transgenic mouse model of CMT2A that developed severe early onset vision loss and neurological deficits, axonal degeneration without cell body loss, and cytoplasmic and axonal accumulations of fragmented mitochondria. While mitochondrial aggregates were labeled for mitophagy, mutant MFN2 did not inhibit Parkin-mediated degradation, but instead had a dominant negative effect on mitochondrial fusion only when MFN1 was at low levels, as occurs in neurons. Finally, using a transgenic approach, we found that augmenting the level of MFN1 in the nervous system in vivo rescued all phenotypes in mutant MFN2R94Q-expressing mice. These data demonstrate that the MFN1/MFN2 ratio is a key determinant of tissue specificity in CMT2A and indicate that augmentation of MFN1 in the nervous system is a viable therapeutic strategy for the disease

Oxidative costs of reproduction in mouse strains selected for different levels of food intake and which differ in reproductive performance

We are grateful to the animal house staff for their care of the animals. This work was supported in part by the US National Institute of Health grants R01AG043972 to J.R.S. and D.B.A. and P30AG050886 and P30DK056336 to D.B.A. The opinions expressed are those of the authors and do not necessarily represent those of the N.I.H. or any other organization. A.H.A.J. was supported by an Iraqi government student scholarship.Peer reviewedPublisher PD

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy.

BackgroundGlobal gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.MethodsWe used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.ResultsThe SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10- 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10- 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.ConclusionFocusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids

Examining the Effect of Genes on Depression as Mediated by Smoking and Modified by Sex.

Depression is heritable, differs by sex, and has environmental risk factors such as cigarette smoking. However, the effect of single nucleotide polymorphisms (SNPs) on depression through cigarette smoking and the role of sex is unclear. In order to examine the association of SNPs with depression and smoking in the UK Biobank with replication in the COPDGene study, we used counterfactual-based mediation analysis to test the indirect or mediated effect of SNPs on broad depression through the log of pack-years of cigarette smoking, adjusting for age, sex, current smoking status, and genetic ancestry (via principal components). In secondary analyses, we adjusted for age, sex, current smoking status, genetic ancestry (via principal components), income, education, and living status (urban vs. rural). In addition, we examined sex-stratified mediation models and sex-moderated mediation models. For both analyses, we adjusted for age, current smoking status, and genetic ancestry (via principal components). In the UK Biobank, rs6424532 [LOC105378800] had a statistically significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 4.0 × 10−4) among all participants and a marginally significant indirect effect among females (p = 0.02) and males (p = 4.0 × 10−3). Moreover, rs10501696 [GRM5] had a marginally significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 0.01) among all participants and a significant indirect effect among females (p = 2.2 × 10−3). In the secondary analyses, the sex-moderated indirect effect was marginally significant for rs10501696 [GRM5] on broad depression through the log of pack-years of cigarette smoking (p = 0.01). In the COPDGene study, the effect of an SNP (rs10501696) in GRM5 on depressive symptoms and medication was mediated by log of pack-years (p = 0.02); however, no SNPs had a sex-moderated mediated effect on depressive symptoms. In the UK Biobank, we found SNPs in two genes [LOC105378800, GRM5] with an indirect effect on broad depression through the log of pack-years of cigarette smoking. In addition, the indirect effect for GRM5 on broad depression through smoking may be moderated by sex. These results suggest that genetic regions associated with broad depression may be mediated by cigarette smoking and this relationship may be moderated by sex

GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates