Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Associations between polygenic liability to psychopathology and non-suicidal versus suicidal self-injury.

Little is known about how non-suicidal and suicidal self-injury are differentially genetically related to psychopathology and related measures. This research was conducted using the UK Biobank Resource, in participants of European ancestry (N = 2320 non-suicidal self-injury [NSSI] only; N = 2648 suicide attempt; 69.18% female). We compared polygenic scores (PGS) for psychopathology and other relevant measures within self-injuring individuals. Logistic regressions and likelihood ratio tests (LRT) were used to identify PGS that were differentially associated with these outcomes. In a multivariable model, PGS for anorexia nervosa (odds ratio [OR] = 1.07; 95% confidence intervals [CI] 1.01; 1.15) and suicidal behavior (OR = 1.06; 95% CI 1.00; 1.12) both differentiated between NSSI and suicide attempt, while the PGS for other phenotypes did not. The LRT between the multivariable and base models was significant (Chi square = 11.38, df = 2, p = 0.003), and the multivariable model explained a larger proportion of variance (Nagelkerke's pseudo-R2 = 0.028 vs. 0.025). While NSSI and suicidal behavior are similarly genetically related to a range of mental health and related outcomes, genetic liability to anorexia nervosa and suicidal behavior is higher among those reporting a suicide attempt than those reporting NSSI-only. Further elucidation of these distinctions is necessary, which will require a nuanced assessment of suicidal versus non-suicidal self-injury in large samples.

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways.

MOTIVATION: Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases. RESULTS: We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes. AVAILABILITY: DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

Relationship between polygenic risk scores and symptom dimensions of schizophrenia and schizotypy in multiplex families with schizophrenia.

BACKGROUND: Psychotic disorders and schizotypal traits aggregate in the relatives of probands with schizophrenia. It is currently unclear how variability in symptom dimensions in schizophrenia probands and their relatives is associated with polygenic liability to psychiatric disorders. AIMS: To investigate whether polygenic risk scores (PRSs) can predict symptom dimensions in members of multiplex families with schizophrenia. METHOD: The largest genome-wide data-sets for schizophrenia, bipolar disorder and major depressive disorder were used to construct PRSs in 861 participants from the Irish Study of High-Density Multiplex Schizophrenia Families. Symptom dimensions were derived using the Operational Criteria Checklist for Psychotic Disorders in participants with a history of a psychotic episode, and the Structured Interview for Schizotypy in participants without a history of a psychotic episode. Mixed-effects linear regression models were used to assess the relationship between PRS and symptom dimensions across the psychosis spectrum. RESULTS: Schizophrenia PRS is significantly associated with the negative/disorganised symptom dimension in participants with a history of a psychotic episode (P = 2.31 × 10-4) and negative dimension in participants without a history of a psychotic episode (P = 1.42 × 10-3). Bipolar disorder PRS is significantly associated with the manic symptom dimension in participants with a history of a psychotic episode (P = 3.70 × 10-4). No association with major depressive disorder PRS was observed. CONCLUSIONS: Polygenic liability to schizophrenia is associated with higher negative/disorganised symptoms in participants with a history of a psychotic episode and negative symptoms in participants without a history of a psychotic episode in multiplex families with schizophrenia. These results provide genetic evidence in support of the spectrum model of schizophrenia, and support the view that negative and disorganised symptoms may have greater genetic basis than positive symptoms, making them better indices of familial liability to schizophrenia.

Differences in genetic correlations between posttraumatic stress disorder and alcohol-related problems phenotypes compared to alcohol consumption-related phenotypes.

BACKGROUND: Posttraumatic Stress Disorder (PTSD) tends to co-occur with greater alcohol consumption as well as alcohol use disorder (AUD). However, it is unknown whether the same etiologic factors that underlie PTSD-alcohol-related problems comorbidity also contribute to PTSD- alcohol consumption. METHODS: We used summary statistics from large-scale genome-wide association studies (GWAS) of European-ancestry (EA) and African-ancestry (AA) participants to estimate genetic correlations between PTSD and a range of alcohol consumption-related and alcohol-related problems phenotypes. RESULTS: In EAs, there were positive genetic correlations between PTSD phenotypes and alcohol-related problems phenotypes (e.g. Alcohol Use Disorders Identification Test (AUDIT) problem score) (rGs: 0.132-0.533, all FDR adjusted p < 0.05). However, the genetic correlations between PTSD phenotypes and alcohol consumption -related phenotypes (e.g. drinks per week) were negatively associated or non-significant (rGs: -0.417 to -0.042, FDR adjusted p: <0.05-NS). For AAs, the direction of correlations was sometimes consistent and sometimes inconsistent with that in EAs, and the ranges were larger (rGs for alcohol-related problems: -0.275 to 0.266, FDR adjusted p: NS, alcohol consumption-related: 0.145-0.699, FDR adjusted p: NS). CONCLUSIONS: These findings illustrate that the genetic associations between consumption and problem alcohol phenotypes and PTSD differ in both strength and direction. Thus, the genetic factors that may lead someone to develop PTSD and high levels of alcohol consumption are not the same as those that lead someone to develop PTSD and alcohol-related problems. Discussion around needing improved methods to better estimate heritabilities and genetic correlations in diverse and admixed ancestry samples is provided.

Genome-wide analysis of schizophrenia and multiple sclerosis identifies shared genomic loci with mixed direction of effects.

Common genetic variants identified in genome-wide association studies (GWAS) show varying degrees of genetic pleiotropy across complex human disorders. Clinical studies of schizophrenia (SCZ) suggest that in addition to neuropsychiatric symptoms, patients with SCZ also show variable immune dysregulation. Epidemiological studies of multiple sclerosis (MS), an autoimmune, neurodegenerative disorder of the central nervous system, suggest that in addition to the manifestation of neuroinflammatory complications, patients with MS may also show co-occurring neuropsychiatric symptoms with disease progression. In this study, we analyzed the largest available GWAS datasets for SCZ (N = 161,405) and MS (N = 41,505) using Gaussian causal mixture modeling (MiXeR) and conditional/conjunctional false discovery rate (condFDR) frameworks to explore and quantify the shared genetic architecture of these two complex disorders at common variant level. Despite detecting only a negligible genetic correlation (rG = 0.057), we observe polygenic overlap between SCZ and MS, and a substantial genetic enrichment in SCZ conditional on associations with MS, and vice versa. By leveraging this cross-disorder enrichment, we identified 36 loci jointly associated with SCZ and MS at conjunctional FDR < 0.05 with mixed direction of effects. Follow-up functional analysis of the shared loci implicates candidate genes and biological processes involved in immune response and B-cell receptor signaling pathways. In conclusion, this study demonstrates the presence of polygenic overlap between SCZ and MS in the absence of a genetic correlation and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

Motivation: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. Availability and implementation: https://github.com/Chatzinakos/JEPEGMIX2. Supplementary information: Supplementary data are available at Bioinformatics online.

Long-Chain ω-3 Levels Are Associated With Increased Alcohol Sensitivity in a Population-Based Sample of Adolescents.

BACKGROUND: The levels of the ω-3 long-chain polyunsaturated fatty acids (ω-3 LC-PUFAs), including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been associated with alcohol sensitivity in vertebrate and invertebrate model systems, but prior studies have not examined this association in human samples despite evidence of associations between ω-3 LC-PUFA levels and alcohol-related phenotypes. Both alcohol sensitivity and ω-3 LC-PUFA levels are impacted by genetic factors, and these influences may contribute to observed associations between phenotypes. Given the potential for using EPA and DHA supplementation in adjuvant care for alcohol misuse and other outcomes, it is important to clarify how ω-3 LC-PUFA levels relate to alcohol sensitivity. METHODS: Analyses were conducted using data from the Avon Longitudinal Study of Parents and Children. Plasma ω-3 LC-PUFA levels were measured at ages 15.5 and 17.5. Participants reported on their initial alcohol sensitivity using the early drinking Self-Rating of the Effects of Alcohol (SRE-5) scale, for which more drinks needed for effects indicates lower levels of response per drink, at ages 15.5, 16.5, and 17.5. Polygenic liability for alcohol consumption, alcohol problems, EPA levels, and DHA levels was derived using summary statistics from large, publicly available datasets. Linear regressions were used to examine the cross-sectional and longitudinal associations between ω-3 LC-PUFA levels and SRE scores. RESULTS: Age 15.5 ω-3 LC-PUFA levels were negatively associated with contemporaneous SRE scores and with age 17.5 SRE scores. One modest association (p = 0.02) between polygenic liability and SRE scores was observed, between alcohol problems-based polygenic risk scores (PRS) and age 16.5 SRE scores. Tests of moderation by genetic liability were not warranted. CONCLUSIONS: Plasma ω-3 LC-PUFA levels may be related to initial sensitivity to alcohol during adolescence. These data indicate that diet-related factors have the potential to impact humans' earliest responses to alcohol exposure.

Polygenic prediction of the phenome, across ancestry, in emerging adulthood.

BACKGROUND: Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on the clinical disorder or cognitive abilities rather than normal-range behavioral outcomes. METHODS: This study examined a sample of emerging adults 18-22 years of age (N = 5947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. Twenty-eight hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes. RESULTS: Polygenic risk for schizophrenia (SZ) in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease. CONCLUSIONS: These results demonstrate the extensive impact of genetic risk for SZ, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.

Meta-Analysis of Genetic Influences on Initial Alcohol Sensitivity.

BACKGROUND: Previous studies indicate that low initial sensitivity to alcohol may be a risk factor for later alcohol misuse. Evidence suggests that initial sensitivity is influenced by genetic factors, but few molecular genetic studies have been reported. METHODS: We conducted a meta-analysis of 2 population-based genome-wide association studies of the Self-Rating of the Effects of Alcohol scale. Our final sample consisted of 7,339 individuals (82.3% of European descent; 59.2% female) who reported having used alcohol at least 5 times. In addition, we estimated single nucleotide polymorphism (SNP)-based heritability and conducted a series of secondary aggregate genetic analyses. RESULTS: No individual locus reached genome-wide significance. Gene and set based analyses, both overall and using tissue-specific expression data, yielded largely null results, and genes previously implicated in alcohol problems and consumption were overall not associated with initial sensitivity. Only 1 gene set, related to hormone signaling and including core clock genes, survived correction for multiple testing. A meta-analysis of SNP-based heritability resulted in a modest estimate of hSNP2  = 0.19 (SE = 0.10), though this was driven by 1 sample (N = 3,683, hSNP2  = 0.36, SE = 0.14, p = 0.04). No significant genetic correlations with other relevant outcomes were observed. CONCLUSIONS: Findings yielded only modest support for a genetic component underlying initial alcohol sensitivity. Results suggest that its biological underpinnings may diverge somewhat from that of other alcohol outcomes and may be related to core clock genes or other aspects of hormone signaling. Larger samples, ideally of prospectively assessed samples, are likely necessary to improve gene identification efforts and confirm the current findings.

Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples.

BACKGROUND: Despite consistent evidence of the heritability of alcohol use disorders (AUDs), few specific genes with an etiological role have been identified. It is likely that AUDs are highly polygenic; however, the etiological pathways and genetic variants involved may differ between populations. The aim of this study was thus to evaluate whether aggregate genetic risk for AUDs differed between clinically ascertained and population-based epidemiological samples. METHODS: Four independent samples were obtained: 2 from unselected birth cohorts (Avon Longitudinal Study of Parents and Children [ALSPAC], N = 4,304; FinnTwin12 [FT12], N = 1,135) and 2 from families densely affected with AUDs, identified from treatment-seeking patients (Collaborative Study on the Genetics of Alcoholism, N = 2,097; Irish Affected Sib Pair Study of Alcohol Dependence, N = 706). AUD symptoms were assessed with clinical interviews, and participants of European ancestry were genotyped. Genomewide association was conducted separately in each sample, and the resulting association weights were used to create polygenic risk scores in each of the other samples (12 total discovery-validation pairs), and from meta-analyses within sample type. We then tested how well these aggregate genetic scores predicted AUD outcomes within and across sample types. RESULTS: Polygenic scores derived from 1 population-based sample (ALSPAC) significantly predicted AUD symptoms in another population-based sample (FT12), but not in either clinically ascertained sample. Trend-level associations (uncorrected p < 0.05) were found for polygenic score predictions within sample types but no or negative predictions across sample types. Polygenic scores accounted for 0 to 1% of the variance in AUD symptoms. CONCLUSIONS: Though preliminary, these results provide suggestive evidence of differences in the genetic etiology of AUDs based on sample characteristics such as treatment-seeking status, which may index other important clinical or demographic factors that moderate genetic influences. Although the variance accounted for by genomewide polygenic scores remains low, future studies could improve gene identification efforts by amassing very large samples, or reducing genetic heterogeneity by informing analyses with other phenotypic information such as sample characteristics. Multiple complementary approaches may be needed to make progress in gene identification for this complex disorder.

Replication of the Interaction of PRKG1 and Trauma Exposure on Alcohol Misuse in an Independent African American Sample.

In the present study, we sought to replicate recent findings of Polimanti et al. (2017), who conducted a genome-wide gene-by-environment interaction study (GEWIS) and identified a gene-by-trauma interaction that predicts alcohol misuse among African Americans.  Consistent with the findings published by Polimanti and colleagues, results of the current study demonstrated an interaction effect, b = 0.41, of trauma exposure and rs1729578 in the intron of PRKG1 on alcohol misuse in a subsample of ancestral African Americans. The minor allele (rs1729578*C) was positively associated with increased alcohol use disorder symptoms in trauma-exposed subjects and negatively associated in non-trauma-exposed subjects.  This effect, however, was only significant for one out of three alcohol outcome measures we investigated, suggesting the interaction may be most salient when predicting higher severity of alcohol misuse. Additionally, the effect did not remain significant after we accounted for testing the effect on three different outcome variables. Also in line with the original study, the gene-by-environment effect was not demonstrated among the ancestral European subsample.  The findings suggest this gene variant may increase an individual's susceptibility to environmental influences, both adverse and supportive.

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

MOTIVATION: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts. RESULTS: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia. AVAILABILITY AND IMPLEMENTATION: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/. CONTACT: donghyung.lee@vcuhealth.org SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Age of onset and family history as indicators of polygenic risk for major depression.

BACKGROUND: The extent to which earlier age of onset (AO) is a reflection of increased genetic risk for major depression (MD) is still unknown. Previous biometrical research has provided mixed empirical evidence for the genetic overlap of AO with MD. If AO is demonstrated to be relevant to molecular polygenic risk for MD, incorporation of AO as a phenotype could enhance future genetic studies. METHODS: This research estimated the SNP-based heritability of AO in the China, Oxford and VCU Experimental Research on Genetic Epidemiology (CONVERGE) case-control sample (N = 9,854; MD case, n = 4,927). Common single nucleotide polymorphism heritability of MD was also examined across both high and low median-split AO groups, and best linear unbiased predictor (BLUP) scores of polygenic risk, in split-halves, were used to predict AO. Distributions of genetic risk across early and late AO were compared, and presence of self-reported family history (FH) of MD was also examined as a predictor of AO. RESULTS: AO was not significantly heritable and polygenic risk derived from the aggregated effects of common genetic variants did not significantly predict AO in any analysis. AO was modestly but significantly lower in cases with a first-degree genetic FH of MD. CONCLUSIONS: Findings indicate that AO is associated with greater self-reported genetic risk for MD in cases, yet not associated with common variant polygenic risk for MD. Future studies of early MD may benefit more from the examination of important moderating variables such as early life events.

The utility of empirically assigning ancestry groups in cross-population genetic studies of addiction.

BACKGROUND AND OBJECTIVES: Given moderate heritability and significant heterogeneity among addiction phenotypes, successful genome-wide association studies (GWAS) are expected to need very large samples. As sample sizes grow, so can genetic diversity leading to challenges in analyzing these data. Methods for empirically assigning individuals to genetically informed ancestry groups are needed. METHODS: We describe a strategy for empirically assigning ancestry groups in ethnically diverse GWAS data including extensions of principal component analysis (PCA) and population matching through minimum Mahalanobis distance. We apply these methods to data from Spit for Science (S4S): the University Student Survey, a study following college students longitudinally that includes genetic and environmental data on substance use and mental health (n = 7,603). RESULTS: The genetic-based population assignments for S4S were 48.7% European, 22.5% African, 10.4% Americas, 9.2% East Asian, and 9.2% South Asian descent. Self-reported census categories "More than one race" and "Unknown"as well as "Hawaiian/Pacific Islander" and "American-Indian/Native Alaskan" were empirically assigned representing a +9% sample retention over conventional methods. Although there was high concordance between self-reported race and empirical population-match (+.924), there was reduction in variance for most ancestry PCs for genetic-based population assignments. CONCLUSIONS: We were able to create more genetically homogenous groups and reduce sample and marker loss through cross-ancestry meta-analysis, potentially increasing power to detect etiologically relevant variation. Our approach provides a framework for empirically assigning genetic ancestry groups which can be applied to other ethnically diverse genetic studies. SCIENTIFIC SIGNIFICANCE: Given the important public health impact and demonstrable gains in statistical power from studying diverse populations, empirically sound practices for genetic studies are needed. (Am J Addict 2017;26:494-501).

CHRONICITY OF DEPRESSION AND MOLECULAR MARKERS IN A LARGE SAMPLE OF HAN CHINESE WOMEN.

BACKGROUND: Major depressive disorder (MDD) has been associated with changes in mean telomere length and mitochondrial DNA (mtDNA) copy number. This study investigates if clinical features of MDD differentially impact these molecular markers. METHODS: Data from a large, clinically ascertained sample of Han Chinese women with recurrent MDD were used to examine whether symptom presentation, severity, and comorbidity were related to salivary telomere length and/or mtDNA copy number (maximum N = 5,284 for both molecular and phenotypic data). RESULTS: Structural equation modeling revealed that duration of longest episode was positively associated with mtDNA copy number, while earlier age of onset of most severe episode and a history of dysthymia were associated with shorter telomeres. Other factors, such as symptom presentation, family history of depression, and other comorbid internalizing disorders, were not associated with these molecular markers. CONCLUSIONS: Chronicity of depressive symptoms is related to more pronounced telomere shortening and increased mtDNA copy number among individuals with a history of recurrent MDD. As these molecular markers have previously been implicated in physiological aging and morbidity, individuals who experience prolonged depressive symptoms are potentially at greater risk of adverse medical outcomes.

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis.

BACKGROUND: As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation. RESULTS: The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p=4.3×10(-16)) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p=0.003, frequency=16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR=3.1, p=0.009, frequency 1.2%) and 5q13.2 deletions (OR=1.5, p=0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p=3.15×10(-18)). CONCLUSION: Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.