Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14

Genome-wide association study of stimulant dependence

Stimulant dependence is heritable, but specific genetic factors underlying the trait have not been identified. A genome-wide association study for stimulant dependence was performed in a discovery cohort of African- (AA) and European-ancestry (EA) subjects ascertained for genetic studies of alcohol, opioid, and cocaine use disorders. The sample comprised individuals with DSM-IV stimulant dependence (393 EA cases, 5288 EA controls; 155 AA cases, 5603 AA controls). An independent cohort from the family-based Collaborative Study on the Genetics of Alcoholism (532 EA cases, 7635 EA controls; 53 AA cases, AA 3352 controls) was used for replication. One variant in SLC25A16 (rs2394476, p = 3.42 × 10^-10, odds ratio [OR] = 3.70) was GWS in AAs. Four other loci showed suggestive evidence, including KCNA4 in AAs (rs11500237, p = 2.99 × 10^-7, OR = 2.31) which encodes one of the potassium voltage-gated channel protein that has been linked to several other substance use disorders, and CPVL in the combined population groups (rs1176440, p = 3.05 × 10^-7, OR = 1.35), whose expression was previously shown to be upregulated in the prefrontal cortex from users of cocaine, cannabis, and phencyclidine. Analysis of the top GWAS signals revealed a significant enrichment with nicotinic acetylcholine receptor genes (adjusted p = 0.04) and significant pleiotropy between stimulant dependence and alcohol dependence in EAs (p_adj = 3.6 × 10^-3), an anxiety disorder in EAs (p_adj = 2.1 × 10^-4), and ADHD in both AAs (p_adj = 3.0 × 10^-33) and EAs (p_adj = 6.7 × 10^-35). Our results implicate novel genes and pathways as having roles in the etiology of stimulant dependence.

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.

Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach

Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.

Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol-Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort

BACKGROUND

The underlying molecular mechanisms associated with alcohol use disorder (AUD) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage-based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms (EEG) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach.

METHODS

The current project adopted a bimultivariate data-driven approach, parallel independent component analysis (para-ICA), to derive and explore significant genotype-phenotype associations in a case-control subset of the Collaborative Study on the Genetics of Alcoholism (COGA) dataset. Para-ICA subjects comprised N = 799 self-reported European Americans (367 controls and 432 AUD cases), recruited from COGA, who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism (SNP) data were preprocessed prior to being subjected to para-ICA in order to derive genotype-phenotype relationships.

RESULTS

From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG-genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease-related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls.

CONCLUSIONS

Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low-frequency alpha and theta abnormalities in alcohol addiction.

Neurexin 3 transmembrane and soluble isoform expression and splicing haplotype are associated with neuron inflammasome and Alzheimer's disease

BACKGROUND

Synaptic damage precedes neuron death in Alzheimer's disease (AD). Neurexins, NRXN1, NRXN2, and NRXN3, are presynaptic adhesion molecules that specify neuron synapses and regulate neurotransmitter release. Neurexins and postsynaptic neuroligins interact with amyloid beta oligomer (AβO) deposits in damaged synapses. NRXN3 gene variants have been associated with autism, addiction, and schizophrenia, however, not fully investigated in Alzheimer's disease. In the present study, we investigated an AD association of a 3'-splicing allele of rs8019381 that produces altered expression of transmembrane or soluble NRXN3 isoforms.

METHODS

We carried out RT-PCR (reverse transcription polymerase chain reaction), PCR-RFLP (PCR and restriction fragment length polymorphism), Sanger sequencing, and in situ hybridization (ISH) assays for NRXN3 neuron expression and genotyping. Genetic associations were analyzed by χ² tests, and ISH signals were analyzed by FISH v1.0 module of Indica Labs HALO software.

RESULTS

We previously identified a functional haplotype in the 3' region of neurexin 3 (NRXN3) gene that alters the expression ratios between NRXN3 transmembrane and soluble isoforms. In this study, we found that expression and ratio of transmembrane and soluble NRXN3 isoforms were reduced in AD postmortem brains and inversely correlated with inflammasome component NLRP3 in AD brain regions. The splicing haplotype related to the transmembrane and soluble NRXN3 expression was associated with AD samples with P = 6.3 × 10^-5 (odds ratio = 2.48) and interacted with APOE genotypes.

CONCLUSIONS

We found that the SNP rs8019381 of NRXN3 that is located adjacent to splicing site #5 (SS#5) interacts with the APOE ε4 haplotype and alters NRXN3 transmembrane or soluble isoform expression in AD postmortem cortex. Dysregulation of presynaptic NRXN3 expression and splicing might increase neuron inflammation in AD brain.

Ventral striatal regulation of CREM mediates impulsive action and drug addiction vulnerability

Impulsivity, a multifaceted behavioral hallmark of attention-deficit/hyperactivity disorder (ADHD), strongly influences addiction vulnerability and other psychiatric disorders that incur enormous medical and societal burdens yet the neurobiological underpinnings linking impulsivity to disease remain poorly understood. Here we report the critical role of ventral striatal cAMP-response element modulator (CREM) in mediating impulsivity relevant to drug abuse vulnerability. Using an ADHD rat model, we demonstrate that impulsive animals are neurochemically and behaviorally more sensitive to heroin and exhibit reduced Crem expression in the nucleus accumbens core. Virally increasing Crem levels decreased impulsive action, thus establishing a causal relationship. Genetic studies in seven independent human populations illustrate that a CREM promoter variant at rs12765063 is associated with impulsivity, hyperactivity and addiction-related phenotypes. We also reveal a role of Crem in regulating striatal structural plasticity. Together, these results highlight that ventral striatal CREM mediates impulsivity related to substance abuse and suggest that CREM and its regulated network may be promising therapeutic targets.

Using an Event-History with Risk-Free Model to Study the Genetics of Alcoholism

Case–control genetic association studies typically ignore possible later disease onset in currently healthy subjects and assume that subjects with diseases equally contribute to the likelihood for inference, regardless of their onset age. Therefore, we used an event-history with risk-free model to simultaneously characterize alcoholism susceptibility and onset age in 65 independent non-Hispanic Caucasian males in the Collaborative Study on the Genetics of Alcoholism. Following data quality control, we analysed 22 single nucleotide polymorphisms (SNPs) on 12 candidate genes. The single-SNP analysis showed that the dominant minor allele of rs2134655 on DRD3 increases alcoholism susceptibility; the dominant minor allele of rs1439047 on NTRK2 delays the alcoholism onset age, but the additive minor allele of rs172677 on GRIN2B and the dominant minor allele of rs63319 on ALDH1A1 advance the alcoholism onset age; and the dominant minor allele of rs1079597 on DRD2 shortens the onset age range. Similarly, multiple-SNPs analysis revealed joint effects of rs2134655, rs172677 and rs1079597, with an adjustment for habitual smoking. This study provides a more comprehensive understanding of the genetics of alcoholism than previous case–control studies.

A KCNJ6 gene polymorphism modulates theta oscillations during reward processing

Event related oscillations (EROs) are heritable measures of neurocognitive function that have served as useful phenotype in genetic research. A recent family genome-wide association study (GWAS) by the Collaborative Study on the Genetics of Alcoholism (COGA) found that theta EROs during visual target detection were associated at genome-wide levels with several single nucleotide polymorphisms (SNPs), including a synonymous SNP, rs702859, in the KCNJ6 gene that encodes GIRK2, a G-protein inward rectifying potassium channel that regulates excitability of neuronal networks. The present study examined the effect of the KCNJ6 SNP (rs702859), previously associated with theta ERO to targets in a visual oddball task, on theta EROs during reward processing in a monetary gambling task. The participants were 1601 adolescent and young adult offspring within the age-range of 17-25years (800 males and 801 females) from high-dense alcoholism families as well as control families of the COGA prospective study. Theta ERO power (3.5-7.5Hz, 200-500ms post-stimulus) was compared across genotype groups. ERO theta power at central and parietal regions increased as a function of the minor allele (A) dose in the genotype (AA>AG>GG) in both loss and gain conditions. These findings indicate that variations in the KCNJ6 SNP influence magnitude of theta oscillations at posterior loci during the evaluation of loss and gain, reflecting a genetic influence on neuronal circuits involved in reward-processing. Increased theta power as a function of minor allele dose suggests more efficient cognitive processing in those carrying the minor allele of the KCNJ6 SNPs. Future studies are needed to determine the implications of these genetic effects on posterior theta EROs as possible "protective" factors, or as indices of delays in brain maturation (i.e., lack of frontalization).

Genetic correlates of the development of theta event related oscillations in adolescents and young adults

The developmental trajectories of theta band (4-7Hz) event-related oscillations (EROs), a key neurophysiological constituent of the P3 response, were assessed in 2170 adolescents and young adults ages 12 to 25. The theta EROs occurring in the P3 response, important indicators of neurocognitive function, were elicited during the evaluation of task-relevant target stimuli in visual and auditory oddball tasks. Associations between the theta EROs and genotypic variants of 4 KCNJ6 single nucleotide polymorphisms (SNPs) were found to vary with age, sex, scalp location, and task modality. Three of the four KCNJ6 SNPs studied here were found to be significantly associated with the same theta EROs in adults in a previous family genome wide association study. Since measures of the P3 response have been found to be a useful endophenotypes for the study of a number of clinical and behavioral disorders, studies of genetic effects on its development in adolescents and young adults may illuminate neurophysiological factors contributing to the onset of these conditions.

GABRB1 Single Nucleotide Polymorphism Associated with Altered Brain Responses (but not Performance) during Measures of Impulsivity and Reward Sensitivity in Human Adolescents

Variations in genes encoding several GABA_A receptors have been associated with human drug and alcohol abuse. Among these, a number of human studies have suggested an association between GABRB1, the gene encoding GABA_A receptor β1 subunits, with Alcohol dependence (AD), both on its own and comorbid with other substance dependence and psychiatric illnesses. In the present study, we hypothesized that the GABRB1 genetically-associated increased risk for developing alcoholism may be associated with impaired behavioral control and altered sensitivity to reward, as a consequence of altered brain function. Exploiting the IMAGEN database (Schumann et al., 2010), we explored in a human adolescent population whether possession of the minor (T) variant of the single nucleotide polymorphism (SNP) rs2044081 is associated with performance of tasks measuring aspects of impulsivity, and reward sensitivity that are implicated in drug and alcohol abuse. Allelic variation did not associate with altered performance in either a stop-signal task (SST), measuring one aspect of impulsivity, or a monetary incentive delay (MID) task assessing reward anticipation. However, increased functional magnetic resonance imaging (fMRI) blood-oxygen-level dependent (BOLD) response in the right hemisphere inferior frontal gyrus (IFG), left hemisphere caudate/insula and left hemisphere inferior temporal gyrus (ITG) during MID performance was higher in the minor (T) allelic group. In contrast, during SST performance, the BOLD response found in the right hemisphere supramarginal gyrus, right hemisphere lingual and left hemisphere inferior parietal gyrus indicated reduced responses in the minor genotype. We suggest that β1-containing GABA_A receptors may play a role in excitability of brain regions important in controlling reward-related behavior, which may contribute to susceptibility to addictive behavior.

Deficient Event-Related Theta Oscillations in Individuals at Risk for Alcoholism: A Study of Reward Processing and Impulsivity Features

BACKGROUND

Individuals at high risk to develop alcoholism often manifest neurocognitive deficits as well as increased impulsivity. Event-related oscillations (EROs) have been used to effectively measure brain (dys)function during cognitive tasks in individuals with alcoholism and related disorders and in those at risk to develop these disorders. The current study examines ERO theta power during reward processing as well as impulsivity in adolescent and young adult subjects at high risk for alcoholism.

METHODS

EROs were recorded during a monetary gambling task (MGT) in 12-25 years old participants (N = 1821; males = 48%) from high risk alcoholic families (HR, N = 1534) and comparison low risk community families (LR, N = 287) from the Collaborative Study on the Genetics of Alcoholism (COGA). Impulsivity scores and prevalence of externalizing diagnoses were also compared between LR and HR groups.

RESULTS

HR offspring showed lower theta power and decreased current source density (CSD) activity than LR offspring during loss and gain conditions. Younger males had higher theta power than younger females in both groups, while the older HR females showed more theta power than older HR males. Younger subjects showed higher theta power than older subjects in each comparison. Differences in topography (i.e., frontalization) between groups were also observed. Further, HR subjects across gender had higher impulsivity scores and increased prevalence of externalizing disorders compared to LR subjects.

CONCLUSIONS

As theta power during reward processing is found to be lower not only in alcoholics, but also in HR subjects, it is proposed that reduced reward-related theta power, in addition to impulsivity and externalizing features, may be related in a predisposition to develop alcoholism and related disorders.

Reward processing deficits and impulsivity in high-risk offspring of alcoholics: A study of event-related potentials during a monetary gambling task

BACKGROUND

Individuals at high risk to develop alcoholism often manifest neurocognitive deficits as well as increased impulsivity. The goal of the present study is to elucidate reward processing deficits, externalizing disorders, and impulsivity as elicited by electrophysiological, clinical and behavioral measures in subjects at high risk for alcoholism from families densely affected by alcoholism in the context of brain maturation across age groups and gender.

METHODS

Event-related potentials (ERPs) and current source density (CSD) during a monetary gambling task (MGT) were measured in 12-25 year old offspring (N=1864) of families in the Collaborative Study on the Genetics of Alcoholism (COGA) Prospective study; the high risk (HR, N=1569) subjects were from families densely affected with alcoholism and the low risk (LR, N=295) subjects were from community families. Externalizing disorders and impulsivity scores were also compared between LR and HR groups.

RESULTS

HR offspring from older (16-25 years) male and younger (12-15 years) female subgroups showed lower P3 amplitude than LR subjects. The amplitude decrement was most prominent in HR males during the loss condition. Overall, P3 amplitude increase at anterior sites and decrease at posterior areas were seen in older compared to younger subjects, suggesting frontalization during brain maturation. The HR subgroups also exhibited hypofrontality manifested as weaker CSD activity during both loss and gain conditions at frontal regions. Further, the HR subjects had higher impulsivity scores and increased prevalence of externalizing disorders. P3 amplitudes during the gain condition were negatively correlated with impulsivity scores.

CONCLUSIONS

Older male and younger female HR offspring, compared to their LR counterparts, manifested reward processing deficits as indexed by lower P3 amplitude and weaker CSD activity, along with higher prevalence of externalizing disorders and higher impulsivity scores.

SIGNIFICANCE

Reward related P3 is a valuable measure reflecting neurocognitive dysfunction in subjects at risk for alcoholism, as well as to characterize reward processing and brain maturation across gender and age group.

Gender modulates the development of theta event related oscillations in adolescents and young adults

The developmental trajectories of theta band (4-7 Hz) event-related oscillations (EROs), a key neurophysiological constituent of the P3 response, were assessed in 2170 adolescents and young adults ages 12 to 25. The theta EROs occurring in the P3 response, important indicators of neurocognitive function, were elicited during the evaluation of task-relevant target stimuli in visual and auditory oddball tasks. These tasks call upon attentional and working memory resources. Large differences in developmental rates between males and females were found; scalp location and task modality (visual or auditory) differences within males and females were small compared to gender differences. Trajectories of interregional and intermodal correlations between ERO power values exhibited increases with age in both genders, but showed a divergence in development between auditory and visual systems during ages 16 to 21. These results are consistent with previous electrophysiological and imaging studies and provide additional temporal detail about the development of neurophysiological indices of cognitive activity. Since measures of the P3 response has been found to be a useful endophenotypes for the study of a number of clinical and behavioral disorders, studies of its development in adolescents and young adults may illuminate neurophysiological factors contributing to the onset of these conditions.

DSM-IV antisocial personality disorder and conduct disorder: evidence for taxonic structures among individuals with and without substance use disorders in the general population

OBJECTIVE

The categorical-dimensional status of DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) conduct disorder (CD) and antisocial personality disorder (ASPD) is a source of controversy. This study examined whether the underlying structure of DSM-IV CD and ASPD was dimensional or categorical (taxonic) among individuals with and without substance use disorders.

METHOD

Using a national large representative survey of U.S. adults (n = 43,093), taxometric analyses of DSM-IV CD and ASPD diagnostic criteria were conducted on the total sample and among those with and without substance use disorders.

RESULTS

Results of three taxometric procedures were consistent in showing that the structures underlying DSM-IV CD and ASPD were clearly taxonic in the total sample and among individuals with and without substance use disorders. Comparison curve fit indices exceeded 0.57 for each model.

CONCLUSIONS

Taxonic findings of the present study were in contrast to the dimensional results of prior taxometric research among incarcerated samples with substantial comorbidity of antisocial syndromes and substance use disorders. Results supported the categorical representation and diagnostic thresholds of ASPD and CD as defined in DSM-IV and DSM-5. That the structure of ASPD and CD may be taxonic suggests that further research on these disorders use group comparative designs in which samples with and without these disorders are compared in terms of sociodemographic and clinical correlates, comorbidity, and treatment utilization. The taxonic structure of ASPD and CD may contribute to future research on causal processes through which these antisocial syndromes develop.

Mutations in the Gabrb1 gene promote alcohol consumption through increased tonic inhibition

Alcohol-dependence is a common, complex and debilitating disorder with genetic and environmental influences. Here we show that alcohol consumption increases following mutations to the γ-aminobutyric acid_A receptor (GABA_AR) β1 subunit gene (Gabrb1). Using N-ethyl-N-nitrosourea mutagenesis on an alcohol-averse background (F1 BALB/cAnN × C3H/HeH), we develop a mouse model exhibiting strong heritable preference for ethanol resulting from a dominant mutation (L285R) in Gabrb1. The mutation causes spontaneous GABA ion channel opening and increases GABA sensitivity of recombinant GABA_ARs, coupled to increased tonic currents in the nucleus accumbens, a region long-associated with alcohol reward. Mutant mice work harder to obtain ethanol, and are more sensitive to alcohol intoxication. Another spontaneous mutation (P228H) in Gabrb1 also causes high ethanol consumption accompanied by spontaneous GABA ion channel opening and increased accumbal tonic current. Our results provide a new and important link between GABA_AR function and increased alcohol consumption that could underlie some forms of alcohol abuse.

Association and interaction analyses of 5-HT3 receptor and serotonin transporter genes with alcohol, cocaine, and nicotine dependence using the SAGE data

Previous studies have implicated genes encoding the 5-HT3AB receptors (HTR3A and HTR3B) and the serotonin transporter (SLC6A4), both independently and interactively, in alcohol (AD), cocaine (CD), and nicotine dependence (ND). However, whether these genetic effects also exist in subjects with comorbidities remains largely unknown. We used 1,136 African-American (AA) and 2,428 European-American (EA) subjects from the Study of Addiction: Genetics and Environment (SAGE) to determine associations between 88 genotyped or imputed variants within HTR3A, HTR3B, and SLC6A4 and three types of addictions, which were measured by DSM-IV diagnoses of AD, CD, and ND and the Fagerström Test for Nicotine Dependence (FTND), an independent measure of ND commonly used in tobacco research. Individual SNP-based association analysis revealed a significant association of rs2066713 in SLC6A4 with FTND in AA (β = -1.39; P = 1.6E - 04). Haplotype-based association analysis found one major haplotype formed by SNPs rs3891484 and rs3758987 in HTR3B that was significantly associated with AD in the AA sample, and another major haplotype T-T-G, formed by SNPs rs7118530, rs12221649, and rs2085421 in HTR3A, which showed significant association with FTND in the EA sample. Considering the biologic roles of the three genes and their functional relations, we used the GPU-based Generalized Multifactor Dimensionality Reduction (GMDR-GPU) program to test SNP-by-SNP interactions within the three genes and discovered two- to five-variant models that have significant impacts on AD, CD, ND, or FTND. Interestingly, most of the SNPs included in the genetic interaction model(s) for each addictive phenotype are either overlapped or in high linkage disequilibrium for both AA and EA samples, suggesting these detected variants in HTR3A, HTR3B, and SLC6A4 are interactively contributing to etiology of the three addictive phenotypes examined in this study.

Efficient generalized least squares method for mixed population and family-based samples in genome-wide association studies

Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.

A family-based robust multivariate association test using maximum statistic

For characterizing the genetic mechanisms of complex diseases familial data with multiple correlated quantitative traits are usually collected in genetic studies. To analyze such data, various multivariate tests have been proposed to investigate the association between the underlying disease genes and the multiple traits. Although these multivariate association tests may have better power performance than the univariate association tests, they suffer from loss of testing power when the genetic models of the putative genes are misspecified. To address the problem, in this paper we aim to develop a family-based robust multivariate association test. We will first establish the optimal multivariate score tests for the recessive, additive, and dominant genetic models. Based on these optimal tests, a maximum-type robust multivariate association test is then obtained. Simulations are conducted to compare the power of our method with that of other existing multivariate methods. The results show that the robust multivariate test does manifest the robustness in power over all plausible genetic models. A practical data set is applied to demonstrate the applicability of our approach. The results suggest that the robust multivariate test is more powerful than the robust univariate test when dealing with multiple quantitative traits.

Genetic and neurophysiological correlates of the age of onset of alcohol use disorders in adolescents and young adults

Discrete time survival analysis was used to assess the age-specific association of event-related oscillations (EROs) and CHRM2 gene variants on the onset of regular alcohol use and alcohol dependence. The subjects were 2,938 adolescents and young adults ages 12-25. Results showed that the CHRM2 gene variants and ERO risk factors had hazards which varied considerably with age. The bulk of the significant age-specific associations occurred in those whose age of onset was under 16. These associations were concentrated in those subjects who at some time took an illicit drug. These results are consistent with studies which associate greater rates of alcohol dependence among those who begin drinking at an early age. The age specificity of the genetic and neurophysiological factors is consistent with recent studies of adolescent brain development, which locate an interval of heightened vulnerability to substance use disorders in the early to mid teens.

Genome-wide significant association signals in IPO11-HTR1A region specific for alcohol and nicotine codependence

BACKGROUND

Alcohol and nicotine codependence can be considered as a more severe subtype of alcohol dependence. A portion of its risk may be attributable to genetic factors.

METHODS

We searched for significant risk genomic regions specific for this disorder using a genome-wide association study. A total of 8,847 subjects underwent gene-disease association analysis, including (i) a discovery cohort of 818 European American cases with alcohol and nicotine codependence and 1,396 European American controls, (ii) a replication cohort of 5,704 Australian family subjects with 907 affected offspring, and (iii) a replication cohort of 449 African American cases and 480 African American controls. Additionally, a total of 38,714 subjects of European or African descent in 18 independent cohorts with 10 other nonalcoholism neuropsychiatric disorders were analyzed as contrast. Furthermore, 90 unrelated HapMap CEU individuals, 93 European brain tissue samples, and 80 European peripheral blood mononuclear cell samples underwent cis-acting expression quantitative locus (cis-eQTL) analysis.

RESULTS

We identified a significant risk region for alcohol and nicotine codependence between IPO11 and HTR1A on chromosome 5q that was reported to be suggestively associated with alcohol dependence previously. In the European American discovery cohort, 381 single nucleotide polymorphisms (SNPs) in this region were nominally associated with alcohol and nicotine codependence (p < 0.05); 57 associations of them survived region- and cohort-wide correction (α = 3.6 × 10(-6) ); and the top SNP (rs7445832) was significantly associated with alcohol and nicotine codependence at the genome-wide significance level (p = 6.2 × 10(-9) ). Furthermore, associations for 34 and 11 SNPs were replicated in the Australian and African American replication cohorts, respectively. Among these replicable associations, 4 reached genome-wide significance level in the meta-analysis of European Americans and European Australians: rs7445832 (p = 9.6 × 10(-10) ), rs13361996 (p = 8.2 × 10(-9) ), rs62380518 (p = 2.3 × 10(-8) ), and rs7714850 (p = 3.4 × 10(-8) ). Cis-eQTL analysis showed that many risk SNPs in this region had nominally significant cis-acting regulatory effects on HTR1A or IPO11 mRNA expression. Finally, no markers were significantly associated with any other neuropsychiatric disorder examined.

CONCLUSIONS

We speculate that this IPO11-HTR1A region might harbor a causal variant for alcohol and nicotine codependence.

A powerful and efficient set test for genetic markers that handles confounders

MOTIVATION

Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power.

RESULTS

We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computational speedup for two random-effects models that makes this approach feasible even for extremely large cohorts. Using this model with both the likelihood ratio test and score test, we find that the former yields more power while controlling type I error. Application of our approach to richly structured Genetic Analysis Workshop 14 data demonstrates that our method successfully corrects for population structure and family relatedness, whereas application of our method to a 15 000 individual Crohn's disease case-control cohort demonstrates that it additionally recovers genes not recoverable by univariate analysis.

AVAILABILITY

A Python-based library implementing our approach is available at http://mscompbio.codeplex.com.

SVM-based generalized multifactor dimensionality reduction approaches for detecting gene-gene interactions in family studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

Genome-wide search for replicable risk gene regions in alcohol and nicotine co-dependence

The present study searched for replicable risk genomic regions for alcohol and nicotine co-dependence using a genome-wide association strategy. The data contained a total of 3,143 subjects including 818 European-American (EA) cases with alcohol and nicotine co-dependence, 1,396 EA controls, 449 African-American (AA) cases, and 480 AA controls. We performed separate genome-wide association analyses in EAs and AAs and a meta-analysis to derive combined P-values, and calculated the genome-wide false discovery rate (FDR) for each SNP. Regions with P < 5 × 10(-7) together with FDR < 0.05 in the meta-analysis were examined to detect all replicable risk SNPs across EAs, AAs, and meta-analysis. These SNPs were followed with a series of functional expression quantitative trait locus (eQTL) analyses. We found a unique genome-wide significant gene region--SH3BP5-NR2C2--that was enriched with 11 replicable risk SNPs for alcohol and nicotine co-dependence. The distributions of -log(P) values for all SNP-disease associations within this region were consistent across EAs, AAs, and meta-analysis (0.315 ≤ r ≤ 0.868; 8.1 × 10(-52) ≤ P ≤ 3.6 × 10(-5)). In the meta-analysis, this region was the only association peak throughout chromosome 3 at P < 0.0001. All replicable risk markers available for eQTL analysis had nominal cis- and trans-acting regulatory effects on gene expression. The transcript expression of the genes in this region was regulated partly by several nicotine dependence (ND)-related genes and significantly correlated with transcript expression of many alcohol dependence- and ND-related genes. We concluded that the SH3BP5-NR2C2 region on Chromosome 3 might harbor causal loci for alcohol and nicotine co-dependence.

XM: association testing on the X-chromosome in case-control samples with related individuals

Genetic variants on the X-chromosome could potentially play an important role in some complex traits. However, development of methods for detecting association with X-linked markers has lagged behind that for autosomal markers. We propose methods for case-control association testing with X-chromosome markers in samples with related individuals. Our method, XM, appropriately adjusts for both correlation among relatives and male-female allele copy number differences. Features of XM include: (1) it is applicable to and computationally feasible for completely general combinations of family and case-control designs; (2) it allows for both unaffected controls and controls of unknown phenotype to be included in the same analysis; (3) it can incorporate phenotype information on relatives with missing genotype data; and (4) it adjusts for sex-specific trait prevalence values. We propose two other tests, Xχ and XW, which can also be useful in certain contexts. We derive the best linear unbiased estimator of allele frequency, and its variance, for X-linked markers. In simulation studies with related individuals, we demonstrate the power and validity of the proposed methods. We apply the methods to X-chromosome association analysis of (1) asthma in a Hutterite sample and (2) alcohol dependence in the GAW 14 COGA data. In analysis (1), we demonstrate computational feasibility of XM and the applicability of our robust variance estimator. In analysis (2), we detect significant association, after Bonferroni correction, between alcohol dependence and single nucleotide polymorphism rs979606 in the monoamine oxidases A gene, where this gene has previously been found to be associated with substance abuse and antisocial behavior.

Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau()

Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.

Partitioning transcript variation in Drosophila: abundance, isoforms, and alleles

Multilevel analysis of transcription is facilitated by a new array design that includes modules for assessment of differential expression, isoform usage, and allelic imbalance in Drosophila. The ∼2.5 million feature chip incorporates a large number of controls, and it contains 18,769 3' expression probe sets and 61,919 exon probe sets with probe sequences from Drosophila melanogaster and 60,118 SNP probe sets focused on Drosophila simulans. An experiment in D. simulans identified genes differentially expressed between males and females (34% in the 3' expression module; 32% in the exon module). These proportions are consistent with previous reports, and there was good agreement (κ = 0.63) between the modules. Alternative isoform usage between the sexes was identified for 164 genes. The SNP module was verified with resequencing data. Concordance between resequencing and the chip design was greater than 99%. The design also proved apt in separating alleles based upon hybridization intensity. Concordance between the highest hybridization signals and the expected alleles in the genotype was greater than 96%. Intriguingly, allelic imbalance was detected for 37% of 6579 probe sets examined that contained heterozygous SNP loci. The large number of probes and multiple probe sets per gene in the 3' expression and exon modules allows the array to be used in D. melanogaster and in closely related species. The SNP module can be used for allele specific expression and genotyping of D. simulans.

Association of ADAM10 and CAMK2A polymorphisms with conduct disorder: evidence from family-based studies

Twin and family studies have shown that genetic factors play a role in the development of conduct disorder (CD). The purpose of this study was to identify genetic variants associated with CD using a family-based association study. We used 4,720 single nucleotide polymorphisms (SNPs) from the Illumina Panel and 11,120 SNPs from the Affymetrix 10K GeneChips genotyped in 155 Caucasian nuclear families from Genetic Analysis Workshop (GAW) 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). 20 SNPs had suggestive associations with CD (p<10(-3)), nine of which were located in known genes, including ADAM10 (rs383902, p=0.00036) and CAMK2A (rs2053053, p=0.00098). Our results were verified using the International Multi-Center ADHD Genetics Project (IMAGE) dataset. In conclusion, we identified several loci associated with CD. Especially, the two genes (ADAM10 and CAMK2A) have been reported to be associated with Alzheimer's disease, bipolar disorder and depression. These findings may serve as a resource for replication in other populations.

Genetic analysis of cortical thickness and fractional anisotropy of water diffusion in the brain

OBJECTIVES

The thickness of the brain's cortical gray matter (GM) and the fractional anisotropy (FA) of the cerebral white matter (WM) each follow an inverted U-shape trajectory with age. The two measures are positively correlated and may be modulated by common biological mechanisms. We employed four types of genetic analyses to localize individual genes acting pleiotropically upon these phenotypes.

METHODS

Whole-brain and regional GM thickness and FA values were measured from high-resolution anatomical and diffusion tensor MR images collected from 712, Mexican American participants (438 females, age = 47.9 ± 13.2 years) recruited from 73 (9.7 ± 9.3 individuals/family) large families. The significance of the correlation between two traits was estimated using a bivariate genetic correlation analysis. Localization of chromosomal regions that jointly influenced both traits was performed using whole-genome quantitative trait loci (QTL) analysis. Gene localization was performed using SNP genotyping on Illumina 1M chip and correlation with leukocyte-based gene-expression analyses. The gene-expressions were measured using the Illumina BeadChip. These data were available for 371 subjects.

RESULTS

Significant genetic correlation was observed among GM thickness and FA values. Significant logarithm of odds (LOD ≥ 3.0) QTLs were localized within chromosome 15q22-23. More detailed localization reported no significant association (p < 5·10(-5)) for 1565 SNPs located within the QTLs. Post hoc analysis indicated that 40% of the potentially significant (p ≤ 10(-3)) SNPs were localized to the related orphan receptor alpha (RORA) and NARG2 genes. A potentially significant association was observed for the rs2456930 polymorphism reported as a significant GWAS finding in Alzheimer's disease neuroimaging initiative subjects. The expression levels for RORA and ADAM10 genes were significantly (p < 0.05) correlated with both FA and GM thickness. NARG2 expressions were significantly correlated with GM thickness (p < 0.05) but failed to show a significant correlation (p = 0.09) with FA.

DISCUSSION

This study identified a novel, significant QTL at 15q22-23. SNP correlation with gene-expression analyses indicated that RORA, NARG2, and ADAM10 jointly influence GM thickness and WM-FA values.

FaST linear mixed models for genome-wide association studies

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.

Conditional random fields for fast, large-scale genome-wide association studies

Understanding the role of genetic variation in human diseases remains an important problem to be solved in genomics. An important component of such variation consist of variations at single sites in DNA, or single nucleotide polymorphisms (SNPs). Typically, the problem of associating particular SNPs to phenotypes has been confounded by hidden factors such as the presence of population structure, family structure or cryptic relatedness in the sample of individuals being analyzed. Such confounding factors lead to a large number of spurious associations and missed associations. Various statistical methods have been proposed to account for such confounding factors such as linear mixed-effect models (LMMs) or methods that adjust data based on a principal components analysis (PCA), but these methods either suffer from low power or cease to be tractable for larger numbers of individuals in the sample. Here we present a statistical model for conducting genome-wide association studies (GWAS) that accounts for such confounding factors. Our method scales in runtime quadratic in the number of individuals being studied with only a modest loss in statistical power as compared to LMM-based and PCA-based methods when testing on synthetic data that was generated from a generalized LMM. Applying our method to both real and synthetic human genotype/phenotype data, we demonstrate the ability of our model to correct for confounding factors while requiring significantly less runtime relative to LMMs. We have implemented methods for fitting these models, which are available at http://www.microsoft.com/science.

Measures of current alcohol consumption and problems: two independent twin studies suggest a complex genetic architecture

BACKGROUND

Twin studies demonstrate that measures of alcohol consumption (AC) show evidence of genetic influence, suggesting they may be useful in gene identification efforts. The extent to which these phenotypes will be informative in identifying susceptibility genes involved in alcohol dependence depends on the extent to which genetic influences are shared across measures of AC and alcohol problems. Previous studies have demonstrated that AC reported for the period of heaviest lifetime drinking shows a large degree of genetic overlap with alcohol dependence; however, many studies with genetic material assess current AC. Further, there are many different aspects of AC that can be assessed (e.g., frequency of use, quantity of use, and frequency of intoxication).

METHODS

Here, we use data from 2 large, independent, population-based twin samples, FinnTwin 16 and The Virginia Adult Twin Study of Psychiatric and Substance Use Disorders, to examine the extent to which genetic influences are shared across many different measures of AC and alcohol problems.

RESULTS

Genetic correlations across current AC measures and alcohol problems were high across both samples. However, both samples suggest a complex genetic architecture with many different genetic factors influencing various aspects of current AC and problems.

CONCLUSIONS

These results suggest that careful attention must be paid to the phenotype in efforts to "replicate" genetic effects across samples or combine samples for meta-analyses of genetic effects influencing susceptibility to alcohol-related outcomes.

Polymorphisms in ABLIM1 are associated with personality traits and alcohol dependence

Personality traits like novelty seeking (NS), harm avoidance (HA), and reward dependence (RD) are known to be moderately heritable (30-60%). These personality traits and their comorbidities, such as alcohol dependence (AD), may share genetic components. We examined 11,120 single nucleotide polymorphisms (SNPs) genotyped in 292 nuclear families from the Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A family-based association analysis was performed using the FBAT program. NS, HA, and RD were treated as quantitative traits and AD as a binary trait. Based on a multivariate association test of three quantitative traits in FBAT, we observed 20 SNPs with p < 10(-3). Interestingly, several genes (TESK2, TIPARP, THEMIS, ABLIM1, RFX4, STON2 and LILRA1) are associated with three personality traits with p < 10(-3) using single trait analysis and AD. Especially, SNP rs727532 within ABLIM1 gene at 10q25 showed the most significant association (p = 6.4 × 10(-5)) in the multivariate test and strong associations with NS, HA, RD, and AD (p = 4.48 × 10(-4), 1.2 × 10(-5), 5.6 × 10(-5), 3.12 × 10(-4), respectively) in the COGA sample. In addition, the association of rs727532 with AD was confirmed in a replication study. This study reports some newly recognized associations between several genetic loci and both AD and three personality traits.

Family-based association analysis of alcohol dependence in the COGA sample and replication in the Australian twin-family study

Family, twin, and adoption studies have indicated that genetic and environmental factors contribute to the development of alcohol dependence (AD). We conducted a low-density genome-wide association analysis to identify genetic variants influencing AD. We used 11,120 SNPs from the Affymetrix 10K Genechips genotyped in 116 Caucasian pedigrees (272 nuclear families) from Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). Family-based association analyses for AD were performed by the PBAT program for autosomal SNPs and by the FBAT program for X-chromosome SNPs. We identified 37 SNPs associated with AD (P < 10(-3)), thirteen of which were located in known genes. The most significant association with AD was observed with SNP rs1986644 (P = 8.51 × 10(-6)) at 13q22 near EDNRB gene. The next best signal was at 1q41 in USH2A (rs532342, P = 1.07 × 10(-5)) and the third region was at 3q25.31 in TIPARP (rs1367311, P = 2.31 × 10(-5)). Furthermore, we found support for association of MAOA gene (P = 4.14 × 10(-4) for rs979606). Six of the 37 AD associated SNPs were confirmed to be associated with AD in Australian twin-family study sample (P < 0.05). Interestingly, four SNPs in DSCAML1 at 11q23 reached the genome-wide significance (the top SNP is rs10892169 with P = 5.31 × 10(-9)), while rs637547 in NKAIN2 at 6q21 showed strong association with AD (P = 5.11 × 10(-7)) in the replication sample. These findings offer the potential for new insights into the pathogenesis of AD and will serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in AD.

Direct assessment of multiple testing correction in case-control association studies with related individuals

Genome-wide association studies typically test large numbers of genetic variants in association with trait values. It is well known that linkage disequilibrium (LD) between nearby markers tends to introduce correlation among association tests. Failure to properly adjust for multiple comparisons can lead to false-positive results or missing true-positive signals. The Bonferroni correction is generally conservative in the presence of LD. The permutation procedure, although has been widely employed to adjust for correlated tests, is not applicable when related individuals are included in case-control samples. With related individuals, the dependence among relatives' genotypes can also contribute to the correlation between tests. We present a new method P(norm) to correct for multiple hypothesis testing in case-control association studies in which some individuals are related. The adjustment with P(norm) simultaneously accounts for two sources of correlations of the test statistics: (1) LD among genetic markers (2) dependence among genotypes across related individuals. Using simulated data based on the International HapMap Project, we demonstrate that it has better control of type I error and is more powerful than some of the recently developed methods. We apply the method to a genome-wide association study of alcoholism in the GAW 14 COGA data set and detect genome-wide significant association.

NTM and NR3C2 polymorphisms influencing intelligence: family-based association studies

Family, twin, and adoption studies have indicated that human intelligence quotient (IQ) has significant genetic components. We performed a low-density genome-wide association analysis with a family-based association test to identify genetic variants influencing IQ, as measured by Wechsler Adult Intelligence Scale full-score IQ (FSIQ). We examined 11,120 single-nucleotide polymorphisms (SNPs) from the Affymetrix GeneChips 10K mapping array genotyped in 292 nuclear families from Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A replication analysis was performed using part of International Multi-Center ADHD Genetics Project (IMAGE) dataset. Twenty-two SNPs were identified as having suggestive associations with IQ (p<10(-3)) in the COGA sample and eleven of the SNPs were located within known genes. In particular, NTM at 11q25 (rs411280, p = 0.000764) and NR3C2 at 4q31.1 (rs3846329, p = 0.000675) were two novel genes which have not been associated with IQ in other studies. It has been reported that NTM might play a role in late-onset Alzheimer disease while NR3C2 may be associated with cognitive function and major depression. The associations of these two genes were well-replicated by single-marker and haplotype analyses in the IMAGE sample. In conclusion, our findings provide evidence that chromosome regions of 11q25 and 4q31.1 contain genes affecting IQ. This study will serve as a resource for replication in other populations.

Statistical Analysis in Genetic Studies of Mental Illnesses

Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.

Evidence for genes on chromosome 2 contributing to alcohol dependence with conduct disorder and suicide attempts

Twin studies provide strong evidence that there is a shared genetic liability that predisposes to a number of different psychiatric outcomes related to behavioral disinhibition. Further, alcohol dependence comorbid with other disinhibitory disorders is particularly heritable. Chromosome 2p14-2q14.3 has been linked to multiple psychiatric conditions related to behavioral undercontrol. In the Collaborative Study on the Genetics of Alcoholism (COGA), we previously reported linkage to this region with alcohol dependence (AD), suicide attempts (SUI), and conduct disorder (CD). In this study, we follow-up on these previous reports of linkage by combining the phenotypes in analyses that jointly consider the presence of multiple conditions. Linkage analyses of the combined phenotype of AD with CD or SUI results in a maximum LOD score of 5.4 in this region. In addition to this primary linkage peak, independent samples have reported linkage to other alcohol-related phenotypes across chromosome 2. Accordingly, we followed-up these linkage signals by testing for association with SNPs across chromosome 2 in a case-control sample, in which a subset of the cases consisted of alcohol-dependent probands from the linkage sample. We find evidence of association with the combined AD with CD or SUI phenotype, with 23 genes surviving permutation testing. The number of associated genes across the chromosome may explain the persistent linkage findings reported on chromosome 2 across a number of independent studies of alcohol and disinhibitory phenotypes. Further, none of the genes were located directly under the primary COGA linkage peak, which has implications for association tests following-up linkage peaks.

Incorporation of covariates in simultaneous localization of two linked loci using affected relative pairs

Background

Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. We extended the robust multipoint Identity-by-Descent (IBD) approach with incorporation of covariates developed previously to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs).

Results

We showed that the efficiency was enhanced by incorporating a quantitative covariate parametrically or non-parametrically while localizing two disease loci using ARPs. In addition to its help in identifying factors associated with the disease and in improving the efficiency in estimating disease loci, this extension also allows investigators to account for heterogeneity in risk-ratios for different ARPs. Data released from the collaborative study on the genetics of alcoholism (COGA) for Genetic Analysis Workshop 14 (GAW 14) were used to illustrate the application of this extended method.

Conclusions

The simulation studies and example illustrated that the efficiency in estimating disease loci was demonstratively enhanced by incorporating a quantitative covariate and by using all relative pairs while mapping two linked loci simultaneously.

Neuropsychological performance of South African treatment-naïve adolescents with alcohol dependence

BACKGROUND

Alcohol dependence (AD) in developmentally vulnerable adolescents is ubiquitous and confers a risk for long-term neurocognitive sequelae, yet comorbid substance use disorders and psychopathology can complicate interpretations. Here, we compare cognitive functioning in adolescents with and without AD, who are free from comorbid disorders.

METHODS

English- and Afrikaans-speaking adolescents (13-15 years) of mixed ancestry and low socio-economic status were recruited from the Cape Town region of South Africa. Adolescents with psychiatric, developmental, or other substance use disorders (SUDs) were excluded. AD (n=26) and control (n=26) groups were matched on age, gender, language, and level of education. Neuropsychological testing in participants' home language followed detailed medical/psychiatric evaluation.

RESULTS

Although our sample included participants who smoked tobacco, lifetime dosage of other drugs was negligible. When tobacco and other drug use as well as demographic variables were controlled, adolescents with AD performed more poorly on measures of Verbal Story Memory, Self-Monitoring, and Psychomotor Speed and Coordination.

CONCLUSIONS

These preliminary results, although relatively subtle, suggest that adolescents with AD may be at increased risk for failure to reach optimal levels of neuromaturation, and may be susceptible to cognitive problems associated with protracted alcohol consumption.

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure

Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for unknown population and pedigree structure while maintaining high power by taking advantage of known pedigree information when it is available. ROADTRIPS can incorporate data on arbitrary combinations of related and unrelated individuals and is computationally feasible for the analysis of genetic studies with millions of markers. In simulations with related individuals and population structure, including admixture, we demonstrate that ROADTRIPS provides a substantial improvement over existing methods in terms of power and type 1 error. The ROADTRIPS method can be used across a variety of study designs, ranging from studies that have a combination of unrelated individuals and small pedigrees to studies of isolated founder populations with partially known or completely unknown pedigrees. We apply the method to analyze two data sets: a study of rheumatoid arthritis in small UK pedigrees, from Genetic Analysis Workshop 15, and data from the Collaborative Study of the Genetics of Alcoholism on alcohol dependence in a sample of moderate-size pedigrees of European descent, from Genetic Analysis Workshop 14. We detect genome-wide significant association, after Bonferroni correction, in both studies.

An Incomplete-Data Quasi-likelihood Approach to Haplotype-Based Genetic Association Studies on Related Individuals

We propose an incomplete-data, quasi-likelihood framework, for estimation and score tests, which accommodates both dependent and partially-observed data. The motivation comes from genetic association studies, where we address the problems of estimating haplotype frequencies and testing association between a disease and haplotypes of multiple tightly-linked genetic markers, using case-control samples containing related individuals. We consider a more general setting in which the complete data are dependent with marginal distributions following a generalized linear model. We form a vector Z whose elements are conditional expectations of the elements of the complete-data vector, given selected functions of the incomplete data. Assuming that the covariance matrix of Z is available, we form an optimal linear estimating function based on Z, which we solve by an iterative method. This approach addresses key difficulties in the haplotype frequency estimation and testing problems in related individuals: (1) dependence that is known but can be complicated; (2) data that are incomplete for structural reasons, as well as possibly missing, with different amounts of information for different observations; (3) the need for computational speed in order to analyze large numbers of markers; (4) a well-established null model, but an alternative model that is unknown and is problematic to fully specify in related individuals. For haplotype analysis, we give sufficient conditions for consistency and asymptotic normality of the estimator and asymptotic χ(2) null distribution of the score test. We apply the method to test for association of haplotypes with alcoholism in the GAW 14 COGA data set.

Testing association in the presence of linkage using the GRE and multiple markers

It has recently been shown that testing for association in the presence of linkage using a score test based on a gamma random effects (GRE) model is substantially more powerful than using the Family-Based Association Test. A reason for the increased power lies in better specification of the within family correlation structure, induced by linkage. The GRE, as presented in (Jonasdottir et al. 2007 Genet Epidemiol. 31:528-540), only considers one marker at a time and does not readily handle missing parental information. Here we extend the GRE to incorporate information from more than one marker. This extension leads to a haplotype GRE test and also to efficient handling of missing data on parental genotypes. We show that the haplotype GRE, the H-GRE, is substantially more powerful than the haplotype FBAT, the Haplotype-Based-Association Test. We demonstrate the usefulness of the extended GRE, by reanalyzing the collaborative study on the genetics of alcoholism data, allowing for missing parental information.

Kernel-based association test

Association mapping (i.e., linkage disequilibrium mapping) is a powerful tool for positional cloning of disease genes. We propose a kernel-based association test (KBAT), which is a composite function of "P-values of single-locus association tests" and "kernel weights related to intermarker distances and/or linkage disequilibria." The KBAT is a general form of some current test statistics. This method can be applied to the study of candidate genes and can scan each chromosome using a moving average procedure. We evaluated the performance of the KBAT through simulation studies that considered evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, empirical P-value estimations, and genetic/physical maps. The results showed that the KBAT had a well-controlled false positive rate and high power compared to existing methods. In addition, the KBAT was also applied to analyze a genomewide data set from the Collaborative Study on the Genetics of Alcoholism. Important genes associated with alcoholism dependence were identified. In summary, the merits of the KBAT are multifold: the KBAT is robust against the inclusion of nuisance markers, is invariant to the map scale, and accommodates different types of genomic data, study designs, and study purposes. The proposed methods are packaged in the user-friendly software, KBAT, available at http://www.stat.sinica.edu.tw/hsinchou/genetics/association/KBAT.htm.

Examining the effect of linkage disequilibrium between markers on the Type I error rate and power of nonparametric multipoint linkage analysis of two-generation and multigenerational pedigrees in the presence of missing genotype data

Because most multipoint linkage analysis programs currently assume linkage equilibrium between markers when inferring parental haplotypes, ignoring linkage disequilibrium (LD) may inflate the Type I error rate. We investigated the effect of LD on the Type I error rate and power of nonparametric multipoint linkage analysis of two-generation and multigenerational multiplex families. Using genome-wide single nucleotide polymorphism (SNP) data from the Collaborative Study of the Genetics of Alcoholism, we modified the original data set into 30 total data sets in order to consider six different patterns of missing data for five different levels of SNP density. To assess power, we designed simulated traits based on existing marker genotypes. For the Type I error rate, we simulated 1,000 qualitative traits from random distributions, unlinked to any of the marker data. Overall, the different levels of SNP density examined here had only small effects on power (except sibpair data). Missing data had a substantial effect on power, with more completely genotyped pedigrees yielding the highest power (except sibpair data). Most of the missing data patterns did not cause large increases in the Type I error rate if the SNP markers were more than 0.3 cM apart. However, in a dense 0.25-cM map, removing genotypes on founders and/or founders and parents in the middle generation caused substantial inflation of the Type I error rate, which corresponded to the increasing proportion of persons with missing data. Results also showed that long high-LD blocks have severe effects on Type I error rates.

Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce

Background

To explore the potential value of high-throughput genotyping assays in the analysis of large and complex genomes, we designed two highly multiplexed Illumina bead arrays using the GoldenGate SNP assay for gene mapping in white spruce (Picea glauca [Moench] Voss) and black spruce (Picea mariana [Mill.] B.S.P.).

Results

Each array included 768 SNPs, identified by resequencing genomic DNA from parents of each mapping population. For white spruce and black spruce, respectively, 69.2% and 77.1% of genotyped SNPs had valid GoldenGate assay scores and segregated in the mapping populations. For each of these successful SNPs, on average, valid genotyping scores were obtained for over 99% of progeny. SNP data were integrated to pre-existing ALFP, ESTP, and SSR markers to construct two individual linkage maps and a composite map for white spruce and black spruce genomes. The white spruce composite map contained 821 markers including 348 gene loci. Also, 835 markers including 328 gene loci were positioned on the black spruce composite map. In total, 215 anchor markers (mostly gene markers) were shared between the two species. Considering lineage divergence at least 10 Myr ago between the two spruces, interspecific comparison of homoeologous linkage groups revealed remarkable synteny and marker colinearity.

Conclusion

The design of customized highly multiplexed Illumina SNP arrays appears as an efficient procedure to enhance the mapping of expressed genes and make linkage maps more informative and powerful in such species with poorly known genomes. This genotyping approach will open new avenues for co-localizing candidate genes and QTLs, partial genome sequencing, and comparative mapping across conifers.

Linkage scan for quantitative traits identifies new regions of interest for substance dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) sample

Dependence on alcohol and illicit drugs frequently co-occur. Results from a number of twin studies suggest that heritable influences on alcohol dependence and drug dependence may substantially overlap. Using large, genetically informative pedigrees from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed quantitative linkage analyses using a panel of 1717 SNPs. Genome-wide linkage analyses were conducted for quantitative measures of DSM-IV alcohol dependence criteria, cannabis dependence criteria and dependence criteria across any illicit drug (including cannabis) individually and in combination as an average score across alcohol and illicit drug dependence criteria. For alcohol dependence, LOD scores exceeding 2.0 were noted on chromosome 1 (2.0 at 213 cM), 2 (3.4 at 234 cM) and 10 (3.7 at 60 cM). For cannabis dependence, a maximum LOD of 1.9 was noted at 95 cM on chromosome 14. For any illicit drug dependence, LODs of 2.0 and 2.4 were observed on chromosome 10 (116 cM) and 13 (64 cM) respectively. Finally, the combined alcohol and/or drug dependence symptoms yielded LODs >2.0 on chromosome 2 (3.2, 234 cM), 10 (2.4 and 2.6 at 60 cM and 116 cM) and 13 (2.1 at 64 cM). These regions may harbor genes that contribute to the biological basis of alcohol and drug dependence.

Case-control association testing with related individuals: a more powerful quasi-likelihood score test

We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the MQLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the MQLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the MQLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 (P=5.9x10(-7)), tsc1750530 (P=4.0x10(-7)), tsc0046696 (P=4.7x10(-7)), and tsc0057290 (P=5.2x10(-7)) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.