Context

Women now represent nearly half of all individuals in treatment for pathological gambling (PG), but relatively little is known about the causes of PG among women or potential sex differences in the causes of PG.

Objectives

To (1) investigate the role of genetic and environmental risk factors in the development of disordered gambling (DG) among women and (2) determine the extent to which the genetic and environmental risk of DG among women differs quantitatively or qualitatively from the risk of DG among men. (Disordered gambling refers to the full continuum of gambling-related problems that includes PG disorder.)

Design

Twin study.

Setting

The national community-based Australian Twin Registry.

Participants

Four thousand seven hundred sixty-four individuals from 2889 twin pairs; twins were aged 32 to 43 years and 57% were women.

Main Outcome Measure

Disordered gambling was defined based on lifetime DSM-IV PG symptom counts.

Results

The estimate of the proportion of variation in liability forDGdue to genetic influences was 49.2% (95% confidence interval, 26.7–60.9). There was no evidence for shared environmental influences contributing to variation in DG liability. There was no evidence for quantitative or qualitative sex differences in the causes of variation in DG liability.

Conclusions

This study establishes for the first time that genes are as important in the etiology of DG in women as they are in men and that the susceptibility genes contributing to variation in liability for DG are likely to overlap considerably in men and women.

Background

Gays, lesbians, and bisexuals (i.e. nonheterosexuals) have been found to be at much greater risk for many psychiatric symptoms and disorders, including depression. This may be due in part to prejudice and discrimination experienced by nonheterosexuals, but studies controlling for minority stress, or performed in very socially liberal countries, suggest that other mechanisms must also play a role. Here we test the viability of common cause (shared genetic or environmental etiology) explanations of elevated depression rates in nonheterosexuals.

Method

A community-based sample of adult twins (N=9884 individuals) completed surveys investigating the genetics of psychiatric disorder, and were also asked about their sexual orientation. Large subsets of the sample were asked about adverse childhood experiences such as sexual abuse, physical abuse, and risky family environment, and also about number of older brothers, paternal and maternal age, and number of close friends. Data were analysed using the classical twin design.

Results

Nonheterosexual males and females had higher rates of lifetime depression than their heterosexual counterparts. Genetic factors accounted for 31% and 44% of variation in sexual orientation and depression, respectively. Bivariate analysis revealed that genetic factors accounted for a majority (60%) of the correlation between sexual orientation and depression. In addition, childhood sexual abuse and risky family environment were significant predictors of both sexual orientation and depression, further contributing to their correlation.

Conclusions

Nonheterosexual men and women had elevated rates of lifetime depression, partly due to shared etiological factors, although causality cannot be definitively resolved.

Context

Understanding the relative contributions of genetic and environmental factors to trauma exposure, post-traumatic stress disorder (PTSD), and major depressive disorder (MDD) is critical to developing etiologic models of these conditions and their co-occurrence.

Objectives

To quantify heritable influences on low-risk trauma, high-risk trauma, PTSD, and MDD and to estimate the degree of overlap between genetic and environmental sources of variance in these 4 phenotypes.

Design

Adult twins and their siblings were ascertained from a large population-based sample of female and male twin pairs on the basis of screening items for childhood sexual abuse and physical abuse obtained in a previous assessment of this cohort.

Setting

Structured psychiatric telephone interviews.

Participants

Total sample size of 2591: 996 female and 536 male twins; 625 female and 434 male nontwin siblings.

Main Outcome Measure

Lifetime low- and high-risk trauma exposure, PTSD, and MDD.

Results

In the best-fitting genetic model, 47% of the variance in low-risk trauma exposure and 60% of the variance in high-risk trauma exposure was attributable to additive genetic factors. Heritable influences accounted for 46% of the variance in PTSD and 27% of the variance in MDD. An extremely high degree of genetic overlap was observed between high-risk trauma exposure and both PTSD (r =0.89; 95% CI, 0.78-0.99) and MDD (r =0.89; 95% CI, 0.77-0.98). Complete correlation of genetic factors contributing to PTSD and to MDD (r=1.00) was observed.

Conclusions

The evidence suggests that almost all the heritable influences on high-risk trauma exposure, PTSD, and MDD, can be traced to the same sources; that is, genetic risk is not disorder specific. Individuals with a positive family history of either PTSD or MDD are at elevated risk for both disorders and should be closely monitored after a traumatic experience for symptoms of PTSD and MDD.

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA ‘skeletons’ derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

Graph theory can be applied to matrices that represent the brain’s anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and “small-world” topology. Network analysis is popular in adult studies of connectivity, but only one study – in just 30 subjects – has examined how network measures change as the brain develops over this period. Here we assessed the developmental trajectory of graph theory metrics of structural brain connectivity in a cross-sectional study of 467 subjects, aged 12 to 30. We computed network measures from 70×70 connectivity matrices of fiber density generated using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). We assessed global efficiency and modularity, and both age and age2 effects were identified. HARDI-based connectivity maps are sensitive to the remodeling and refinement of structural brain connections as the human brain develops.

Human brain connectivity is disrupted in a wide range of disorders – from Alzheimer’s disease to autism – but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1–58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration.

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess around one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their non-twin siblings (mean age: 23.7±2.1 SD years; 193 M/279 F). We combined clustering with genome-wide scanning to find brain systems with common genetic determination. We then filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it more computationally tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions, and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient (IQ) and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

We conducted a genome-wide association (GWA) meta-analysis of 4,604 endometriosis cases and 9,393 controls of Japanese1 and European2 ancestry. We show that rs12700667 on chromosome 7p15.2, previously found in Europeans, replicates in Japanese (P = 3.6 × 10−3), and confirm association of rs7521902 on 1p36.12 near WNT4. In addition, we establish association of rs13394619 in GREB1 on 2p25.1 and identify a novel locus on 12q22 near VEZT (rs10859871). Excluding European cases with minimal or unknown severity, we identified additional novel loci on 2p14 (rs4141819), 6p22.3 (rs7739264) and 9p21.3 (rs1537377). All seven SNP effects were replicated in an independent cohort and produced P < 5 × 10−8 in a combined analysis. Finally, we found a significant overlap in polygenic risk for endometriosis between the European and Japanese GWA cohorts (P = 8.8 × 10−11), indicating that many weakly associated SNPs represent true endometriosis risk loci and risk prediction and future targeted disease therapy may be transferred across these populations.

Earlier studies have found an elevated risk for psychopathology and suicidal behavior associated with childhood sexual abuse (CSA); however the degree to which risk is mediated by depression and post-traumatic stress disorder (PTSD) in women and men remains unclear. We examined these issues in data from a family study of childhood maltreatment (N=2559). We found significant CSA-associated risk for depression, PTSD, and suicidal behavior for women and men. In survival analyses controlling for these disorders, we observed persistent, but somewhat reduced, CSA-associated risk for suicidal ideation and suicide attempt. Our findings thus suggest these disorders partially mediate CSA-associated risk.

Serum butyrylcholinesterase (BCHE) activity is associated with obesity, blood pressure and biomarkers of cardiovascular and diabetes risk. We have conducted a genome-wide association scan to discover genetic variants affecting BCHE activity, and to clarify whether the associations between BCHE activity and cardiometabolic risk factors are caused by variation in BCHE or whether BCHE variation is secondary to the metabolic abnormalities. We measured serum BCHE in adolescents and adults from three cohorts of Australian twin and family studies. The genotypes from ∼2.4 million single-nucleotide polymorphisms (SNPs) were available in 8791 participants with BCHE measurements. We detected significant associations with BCHE activity at three independent groups of SNPs at the BCHE locus (P = 5.8 × 10−262, 7.8 × 10−47, 2.9 × 10−12) and at four other loci: RNPEP (P = 9.4 × 10−16), RAPH1-ABI2 (P = 4.1 × 10−18), UGT1A1 (P = 4.0 × 10−8) and an intergenic region on chromosome 8 (P = 1.4 × 10−8). These loci affecting BCHE activity were not associated with metabolic risk factors. On the other hand, SNPs in genes previously associated with metabolic risk had effects on BCHE activity more often than can be explained by chance. In particular, SNPs within FTO and GCKR were associated with BCHE activity, but their effects were partly mediated by body mass index and triglycerides, respectively. We conclude that variation in BCHE activity is due to multiple variants across the spectrum from uncommon/large effect to common/small effect, and partly results from (rather than causes) metabolic abnormalities.

The NTRK1 gene (also known as TRKA) encodes a high affinity receptor for NGF, a neurotrophin involved in nervous system development and myelination. NTRK1 has been implicated in neurological function via links between the T allele at rs6336 (NTRK1-T) and schizophrenia risk. A variant in the neurotrophin gene, BDNF, was previously associated with white matter integrity in young adults, highlighting the importance of neurotrophins to white matter development. We hypothesized that NTRK1-T would relate to lower FA in healthy adults.

We scanned 391 healthy adult human twins and their siblings (mean age: 23.6 ± 2.2 years; 31 NTRK1-T carriers, 360 non-carriers) using 105-gradient diffusion tensor imaging at 4 Tesla. We evaluated in brain white matter how NTRK1-T and NTRK1 rs4661063 allele A (rs4661063-A, which is in moderate linkage disequilibrium with rs6336) related to voxelwise fractional anisotropy – a common diffusion tensor imaging measure of white matter microstructure. We used mixed-model regression to control for family relatedness, age, and sex. The sample was split in half to test results reproducibility. The false discovery rate method corrected for voxelwise multiple comparisons.

NTRK1-T and rs4661063-A correlated with lower white matter fractional anisotropy, independent of age and sex (multiple comparisons corrected: false discovery rate critical p = 0.038 for NTRK1-T and 0.013 for rs4661063-A). In each half-sample, the NTRK1-T effect was replicated in the cingulum, corpus callosum, superior and inferior longitudinal fasciculi, inferior fronto-occipital fasciculus, superior corona radiata, and uncinate fasciculus. Our results suggest that NTRK1-T is important for developing white matter microstructure.

Human mate choice is central to individuals’ lives and to the evolution of the species, but the basis of variation in mate choice is not well understood. Here we look at a large community-based sample of twins and their partners and parents (N > 20,000 individuals) to test for genetic and family environmental influences on mate choice, with and without controlling for the effects of assortative mating. Key traits are analyzed, including height, body mass index, age, education, income, personality, social attitudes, and religiosity. This revealed near-zero genetic influences on male and female mate choice over all traits and no significant genetic influences on mate choice for any specific trait. A significant family environmental influence was found for the age and income of females’ mate choices, possibly reflecting parental influence over mating decisions. We also tested for evidence of sexual imprinting, where individuals acquire mate-choice criteria during development by using their opposite-sex parent as the template of a desirable mate; there was no such effect for any trait. The main discernable pattern to mate choice was assortative mating; we found that partner similarity was due to initial choice rather than convergence and also due at least in part to phenotypic matching.

Polysaccharide sidechains attached to proteins play important roles in cell–cell and receptor–ligand interactions. Variation in the carbohydrate component has been extensively studied for the iron transport protein transferrin, because serum levels of the transferrin isoforms asialotransferrin + disialotransferrin (carbohydrate-deficient transferrin, CDT) are used as biomarkers of excessive alcohol intake. We conducted a genome-wide association study to assess whether genetic factors affect CDT concentration in serum. CDT was measured in three population-based studies: one in Switzerland (CoLaus study, n = 5181) and two in Australia (n = 1509, n = 775). The first cohort was used as the discovery panel and the latter ones served as replication. Genome-wide single-nucleotide polymorphism (SNP) typing data were used to identify loci with significant associations with CDT as a percentage of total transferrin (CDT%). The top three SNPs in the discovery panel (rs2749097 near PGM1 on chromosome 1, and missense polymorphisms rs1049296, rs1799899 in TF on chromosome 3) were successfully replicated , yielding genome-wide significant combined association with CDT% (P = 1.9 × 10−9, 4 × 10−39, 5.5 × 10−43, respectively) and explain 5.8% of the variation in CDT%. These allelic effects are postulated to be caused by variation in availability of glucose-1-phosphate as a precursor of the glycan (PGM1), and variation in transferrin (TF) structure.

Background

Given moderately strong genetic contributions to variation in alcoholism and heaviness of drinking (50–60% heritability), with high correlation of genetic influences, we have conducted a quantitative trait genomewide association study for phenotypes related to alcohol use and dependence.

Methods

Diagnostic interview and blood/buccal samples were obtained from sibships ascertained through the Australian Twin Registry. Genomewide SNP genotyping was performed with 8754 individuals [2062 alcohol dependent cases] selected for informativeness for alcohol use disorder and associated quantitative traits. Family-based association tests were performed for alcohol dependence, dependence factor score and heaviness of drinking factor score, with confirmatory case-population control comparisons using an unassessed population control series of 3393 Australians with genomewide SNP data.

Results

No findings reached genomewide significance (p=8.4×10−8 for this study), with lowest p-value for primary phenotypes of 1.2×10−7. Convergent findings for quantitative consumption and diagnostic and quantitative dependence measures suggest possible roles for a transmembrane protein gene (TMEM108) and for ANKS1A. The major finding, however, was small effect sizes estimated for individual SNPs, suggesting that hundreds of genetic variants make modest contributions (1/4% of variance or less) to alcohol dependence risk.

Conclusions

We conclude that (i) meta-analyses of consumption data may contribute usefully to gene-discovery; (ii) translation of human alcoholism GWAS results to drug discovery or clinically useful prediction of risk will be challenging; (iii) through accumulation across studies, GWAS data may become valuable for improved genetic risk differentiation in research in biological psychiatry (e.g. prospective high-risk or resilience studies).

Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 females, 119 males, age: 23.4 +/−2.17 SD years) were scanned with 105-gradient high angular diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual’s co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole brain analyses, as well as greater eccentricity (maximum path length) in 60 of 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study to link graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of circuits implicated in risk for autism.

In a previous paper we demonstrated in a large twin study that disordered gambling (DG) as defined by the DSM-IV symptom set runs in families, that about half of the variation in liability for DG was due to familial factors, and that all of this was explained by shared genetic rather than shared environmental influences (Slutske et al., 2010). The purpose of the present study was to extend this work to also include an alternative conceptualization of DG that is provided by the South Oaks Gambling Screen (SOGS) item set in order to (a) compare the magnitude of the familial resemblance obtained when using the two definitions of DG (based on the DSM-IV and the SOGS), (b) examine the extent to which the two definitions tap the same underlying sources of genetic and environmental variation, and (c) examine whether the same results will be obtained among men and women. The results of bivariate twin model-fitting analyses suggested that DG as defined by the DSM-IV and SOGS substantially overlapped at the etiologic level among both men and women, which supports the construct validity of both the DSM and SOGS conceptualizations of DG. This study highlights the utility of twin studies for appraising the validity of the diagnostic nomenclature.

Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and ‘genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ∼4000 and ∼133 000 individuals.

Objective

To refine a previously reported linkage peak for endometriosis on chromosome 10q26, and conduct follow-up analyses and a fine-mapping association study across the region to identify new candidate genes for endometriosis.

Design

Case-control study.

Setting

Academic research.

Subject(s)

Cases = 3,223 women with surgically confirmed endometriosis; Controls = 1,190 women without endometriosis and 7,060 population samples.

Intervention(s)

Analysis of 11,984 SNPs on chromosome 10.

Main outcome measure(s)

Allele frequency differences between cases and controls.

Results

Linkage analyses on families grouped by endometriosis symptoms (primarily subfertility) provided increased evidence for linkage (logarithm of odds (LOD) score = 3.62) near a previously reported linkage peak. Three independent association signals were found at 96.59 Mb (rs11592737, P=4.9 × 10−4), 105.63 Mb (rs1253130, P=2.5 × 10−4) and 124.25 Mb (rs2250804, P=9.7 × 10−4). Analyses including only samples from linkage families supported the association at all three regions. However, only rs11592737 in the cytochrome P450 subfamily C (CYP2C19) gene was replicated in an independent sample of 2,079 cases and 7060 population controls.

Conclusion(s)

The role of the CYP2C19 gene in conferring risk for endometriosis warrants further investigation.

Sex differences in the genetic and environmental influences on childhood conduct disorder and adult antisocial behavior were examined in a large community sample of 6,383 adult male, female, and opposite-sex twins. Retrospective reports of childhood conduct disorder (prior to age 18) were obtained when participants were approximately 30 years old, and lifetime reports of adult antisocial behavior (antisocial behavior after age 17) were obtained eight years later. Results revealed that either the genetic or shared environmental factors influencing childhood conduct disorder differed for males and females (i.e., a qualitative sex difference), but by adulthood, these sex-specific influences on antisocial behavior were no longer apparent. Further, genetic and environmental influences accounted for proportionally the same amount of variance in antisocial behavior for males and females in childhood and adulthood (i.e., no quantitative sex differences). Additionally, the stability of antisocial behavior from childhood to adulthood was slightly greater for males than females. Though familial factors accounted for more of the stability of antisocial behavior for males than females, genetic factors accounted for the majority of the covariation between childhood conduct disorder and adult antisocial behavior for both sexes. The genetic influences on adult antisocial behavior overlapped completely with the genetic influences on childhood conduct disorder for both males and females. Implications for future twin and molecular genetic studies are discussed.

Processing speed is an important cognitive function that is compromised in psychiatric illness (e.g., schizophrenia, depression) and old age; it shares genetic background with complex cognition (e.g., working memory, reasoning). To find genes influencing speed we performed a genome-wide association scan in up to three cohorts: Brisbane (mean age 16 years; N = 1659); LBC1936 (mean age 70 years, N = 992); LBC1921 (mean age 82 years, N = 307), and; HBCS (mean age 64 years, N = 1080). Meta-analysis of the common measures highlighted various suggestively significant (p < 1.21 × 10−5) SNPs and plausible candidate genes (e.g., TRIB3). A biological pathways analysis of the speed factor identified two common pathways from the KEGG database (cell junction, focal adhesion) in two cohorts, while a pathway analysis linked to the GO database revealed common pathways across pairs of speed measures (e.g., receptor binding, cellular metabolic process). These highlighted genes and pathways will be able to inform future research, including results for psychiatric disease.