Nicholas G. Martin’s research while affiliated with QIMR Berghofer Medical Research Institute and other places

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Publications (840)


Figure 2. Best fitting multivariate autoregression ACE model for the SPHERE somatic distress scale from ages 12 to 35. Note: Illustrated are the latent genetic (A1-A6), shared environment (C1-C2), and non-shared environmental (E1-E6) components and their age-specific genetic, shared environmental, and non-shared environmental innovations, along with transient non-shared environmental influences including measurement error (ε). The genetic, shared, and non-shared environmental autoregression causal coefficients (βa, βc & βe) are each constrained equal across time. 95% confidence intervals are estimated for all free parameters. Age-specific innovation variances are constrained to one, as are factor loadings from each latent 'A', 'C' and 'E' component to their corresponding observed phenotypes. Transient, non-shared environmental influences (ε) are constrained equal across all age intervals for model identification and parsimony.
Multivariate model fitting comparisons between the reference correlated factors (null hypothesis) and the competing models. Best fitting models bolded
Multivariate model fitting comparisons between the ACE autoregression, competing AE, CE and E sub-models, and post hoc analyses for somatic and psychological distress
The impact of genes and environment assessed longitudinally on psychological and somatic distress in twins from ages 15 to 35 years
  • Article
  • Full-text available

February 2025

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11 Reads

Psychological Medicine

Nathan A Gillespie

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Nicholas G Martin

Background Genetically informative twin studies have consistently found that individual differences in anxiety and depression symptoms are stable and primarily attributable to time-invariant genetic influences, with non-shared environmental influences accounting for transient effects. Methods We explored the etiology of psychological and somatic distress in 2279 Australian twins assessed up to six times between ages 12–35. We evaluated autoregressive, latent growth, dual-change, common, and independent pathway models to identify which, if any, best describes the observed longitudinal covariance and accounts for genetic and environmental influences over time. Results An autoregression model best explained both psychological and somatic distress. Familial aggregation was entirely explained by additive genetic influences, which were largely stable from ages 12 to 35. However, small but significant age-dependent genetic influences were observed at ages 20–27 and 32–35 for psychological distress and at ages 16–19 and 24–27 for somatic distress. In contrast, environmental influences were predominantly transient and age-specific. Conclusions The longitudinal trajectory of psychological distress from ages 12 to 35 can thus be largely explained by forward transmission of a stable additive genetic influence, alongside smaller age-specific genetic innovations. This study addresses the limitation of previous research by exhaustively exploring alternative theoretical explanations for the observed patterns in distress symptoms over time, providing a more comprehensive understanding of the genetic and environmental factors influencing psychological and somatic distress across this age range.

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The Eating Disorders Genetics Initiative 2 (EDGI2): Study Protocol

February 2025

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32 Reads

Background: The Eating Disorders Genetics Initiative 2 (EDGI2) is designed to explore the role of genes and environment in anorexia nervosa, bulimia nervosa, binge-eating disorder, and avoidant/restrictive food intake disorder (ARFID) with a focus on diverse populations and severe and/or longstanding illness.Methods: A total of 20,000 new participants (18,700 cases and 1,300 controls) will be ascertained from the United States (US), Mexico (MX), Australia (AU), Aotearoa New Zealand (NZ), Sweden (SE), and Denmark (DK). Comprehensive phenotyping and genotyping will be performed for participants in US, MX, AU, NZ, and SE using the EDGI2 questionnaire battery and participant saliva samples. In DK, case identification and genotyping will be through the National Patient Register and bloodspots archived near birth. Case-control and case-case genome-wide association studies will be conducted within EDGI2 and enhanced via meta-analysis with external data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Additional analyses will explore genetic correlations between eating disorders (EDs) and other psychiatric and metabolic traits, calculate polygenic risk scores (PRS), and leverage functional biology to evaluate clinical outcomes. Moreover, analyzing PRS for patient stratification and linking identified risk loci to clinically relevant phenotypes highlight the potential of EDGI2 for clinical translation. Discussion: EDGI2 is a global expansion of the EDGI study to increase sample size, expand the numbers of participants from non-European ancestry, and to include ARFID. Inclusion of MX will be the first large-scale ED genetics study from Latin America. ED genetics research has historically lagged behind other psychiatric disorders, and EDGI2 is designed to rapidly advance the study of the genetics of the major EDs. Exploring EDs at both the diagnostic level and the symptom level will provide an unprecedented look at the genetic architecture underlying EDs.


Figure 1. Comparing genetic correlations (r g ) for DSM-NicDep, FTND, ICD-TUD, and PTU 292 with other traits in European ancestry data. Traits include other substance use disorders 293 (CanUD = cannabis use disorder 25 ; OUD = opioid use disorder 26 ; PAU = problematic alcohol 294 use 24 , ICD-TUD = ICD-based tobacco use disorder 6 ), substance use behaviors (CanUse = 295 cannabis ever-use 33 ; DPW = drinks per week 3 ; SmkInit = smoking initiation 3 , SmkCessation = 296 smoking cessation 3 , CPD = cigarettes per day 3 ), psychiatric disorders and other mental health 297
Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits

January 2025

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23 Reads

Genetic research on nicotine dependence has utilized multiple assessments that are in weak agreement. We conducted a genome-wide association study of nicotine dependence defined using the Diagnostic and Statistical Manual of Mental Disorders (DSM-NicDep) in 61,861 individuals (47,884 of European ancestry, 10,231 of African ancestry, 3,746 of East Asian ancestry) and compared the results to other nicotine-related phenotypes. We replicated the well-known association at the CHRNA5 locus (lead SNP: rs147144681, p =1.27E-11 in European ancestry; lead SNP = rs2036527, p = 6.49e-13 in cross-ancestry analysis). DSM-NicDep showed strong positive genetic correlations with cannabis use disorder, opioid use disorder, problematic alcohol use, lung cancer, material deprivation, and several psychiatric disorders, and negative correlations with respiratory function and educational attainment. A polygenic score of DSM-NicDep predicted DSM-5 tobacco use disorder and 6 of 11 individual diagnostic criteria, but none of the Fagerström Test for Nicotine Dependence (FTND) items, in the independent NESARC-III sample. In genomic structural equation models, DSM-NicDep loaded more strongly on a previously identified factor of general addiction liability than did a "problematic tobacco use" factor (a combination of cigarettes per day and nicotine dependence defined by the FTND). Finally, DSM-NicDep was strongly genetically correlated with a GWAS of tobacco use disorder as defined in electronic health records, suggesting that combining the wide availability of diagnostic EHR data with nuanced criterion-level analyses of DSM tobacco use disorder may produce new insights into the genetics of this disorder.


Genetic correlation and bivariate MiXeR estimates for the genetic overlap of BD ascertainment and subtypes
Trait-influencing genetic variants shared between each pair (grey) and unique to each trait (colours) are shown. The numbers within the Venn diagrams indicate the estimated number of trait-influencing variants (and standard errors; in thousands) that explain 90% of SNP-h² in each phenotype. The size of the circles reflects the polygenicity of each trait, with larger circles corresponding to greater polygenicity. The estimated genetic correlation (rg) and standard error between BD and each trait of interest from LDSC are shown below the corresponding Venn diagram. Clinical and community samples were stratified into BDI and BDII subtypes if subtype data were available. Model fit statistics indicated that MiXeR-modelled overlap for bivariate comparisons including the BD subtypes (BDI and BDII) were not distinguishable from minimal or maximal possible overlap, and therefore are to be interpreted with caution (see Supplementary Table 4).
Genetic correlations (with standard errors) between BD and other psychiatric disorders
The y axis (trait 2) is ordered based on the significance and magnitude of genetic correlation of each trait with BDI. P values were calculated from the two-sided z-statistics computed by dividing the estimated genetic correlation by the estimated standard error, without adjustment. The standard error for a genetic correlation was estimated using a ratio block jackknife over 200 blocks. The triangles indicate significant results passing the Bonferroni-corrected significance threshold of two-sided P < 3.6 × 10⁻⁵. Error bars represent the standard error of the estimate. The year indicated in parentheses after each trait refers to the year in which the GWAS was published. Details are provided in Supplementary Table 13. PTSD, post-traumatic stress disorder.
Phenotypic variance in BD in EUR cohorts explained by PRSs derived from the multi-ancestry and EUR meta-analyses (with and without self-reported data)
Variance explained is presented on the liability scale, assuming a 2% population prevalence of BD. The results (all cohorts) are the median weighted liability R² values across all 55 EUR cohorts (40,992 cases and 80,215 controls; neff = 46,725). Similarly, BDI, BDII, clinical and community panels show the results across 36 BDI cohorts (12,419 cases and 33,148 controls; neff = 14,607), 21 BDII cohorts (2,549 cases and 23,385 controls; neff = 4,021), 48 clinical cohorts (27,833 cases and 46,623 controls; neff = 29,543) and 7 community cohorts (13,159 cases and 36,592 controls; neff = 17,178). All analyses were weighted by the effective n per cohort. The median liability R² is represented as a horizontal black line.
Supercluster-level SNP-h² enrichment for BD
The t-distributed stochastic neighbour embedding (tSNE) plot (naming convention and source of the single-cell data from Siletti et al.²³; left) is coloured by the enrichment z-score. Grey indicates non-significantly enriched superclusters (false discovery rate > 0.05). The bar plot (right) shows the nine significantly enriched superclusters. CGE, caudal ganglionic eminence; MGE, medial ganglionic eminence.
Genomics yields biological and phenotypic insights into bipolar disorder

January 2025

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292 Reads

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4 Citations

Nature

Bipolar disorder is a leading contributor to the global burden of disease¹. Despite high heritability (60–80%), the majority of the underlying genetic determinants remain unknown². We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings³, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder⁴, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.





Genome-wide meta-analyses of non-response to antidepressants identify novel loci and potential drugs

November 2024

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39 Reads

Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants (25,255 non-responders and 110,216 responders). We performed genome-wide association meta-analyses, genetic correlation analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci (rs1106260 and rs60847828) associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. Genetic correlation analyses show positive associations between non-response to antidepressants and most psychiatric traits, and negative associations with cognitive traits and subjective well-being. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.


Figure 4. Comparison of genetic correlations estimated from population effects (x-axis) and direct genetic effects (y-axis). The shading gives the density of points from 435 pairs of phenotypes. We have marked and labeled the trait pairs where the genetic correlations are statistically distinguishable (FDR<0.01, two-sided test). The diagonal line is the identity. Errors bars indicate 95% confidence intervals. Trait abbreviations: BMI, body mass index; EA, educational attainment (years); FEV1, forced expiratory volume in 1 second; Non-HDL, total cholesterol minus high density lipoprotein cholesterol; Ever-smoker, whether an individual has ever smoked.
Figure 6. Out-of-sample polygenic prediction analysis using the educational attainment (EA) direct genetic effect (DGE) PGI. Familybased PGI analysis was performed on education and cognitiveperformance-related outcomes. Error bars give 95% confidence intervals. Outcome phenotypes: Avg. Eng. & Math GCSE Score (Supplementary Note Section 4); educational attainment outcome as defined in Okbay et al. 18 ; word Activity score from MCS Sweep 6 (age 14); cognitive assessment outcome from MCS Sweep 7 (age 17); fluid intelligence score from UK Biobank. Full descriptions of outcome phenotypes can be found in Supplementary Table 8. An expanded set of numerical results is available in Supplementary Table 9.
Family-GWAS reveals effects of environment and mating on genetic associations

October 2024

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152 Reads

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2 Citations

Genome–wide association studies (GWAS) have discovered thousands of replicable genetic associations, guiding drug target discovery and powering genetic prediction of human phenotypes and diseases. However, genetic associations can be affected by gene–environment correlations and non–random mating, which can lead to biased inferences in downstream analyses. Family–based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations. Here, we report results from an FGWAS meta–analysis of 34 phenotypes from 17 cohorts. We found evidence that factors uncorrelated with DGEs make substantial contributions to genetic associations for 27 phenotypes, with population stratification confounding — a form of gene–environment correlation — likely the major cause. By estimating SNP heritability and genetic correlations using DGEs, we found evidence that assortative mating has led to overestimation of SNP heritability for 5 phenotypes and overestimation of the degree of shared genetic effects (pleiotropy) between 22 pairs of phenotypes. Polygenic predictors constructed from DGEs are particularly useful for studying natural selection, assortative mating, and indirect genetic effects (effects of relatives′ genes mediated through the family environment). We validate our meta–analysis results by predicting phenotypes in hold–out samples using polygenic predictors constructed from DGEs, achieving statistically significant out–of–sample prediction for 24 phenotypes with little attenuation of predictive power within–families. We provide FGWAS summary statistics for 34 phenotypes that can be used for downstream analyses. Our study provides both a template for performing FGWAS and an argument for its value for debiasing inferences and understanding the impact of environment and mating patterns.



Citations (58)


... To summarize the overall relationship between BD-subtype, the bicluster-informed PRS and the population-wide PRS, we pool the subjects across the replication-arms and convert the combined AUC-values into R 2 -values on a liability-scale [71] using prevalences of 2% for BD, and 1% for BDI and BDII [72]. Using notation analogous to the AUC-values, we denote these liability-scores as R 2 wide ðpÞ, R 2 widejBDI ðpÞ and R 2 widejBDII ðpÞ, as well as R 2 bicl ði;pÞ, R 2 bicljBDI ði;pÞ and R 2 bicljBDII ði;pÞ, respectively. ...

Reference:

Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder
Genomics yields biological and phenotypic insights into bipolar disorder

Nature

... Height-direct. Tan et al. (2024) carried a family-based GWAS (FGWAS) on 34 phenotypes, including body height. We used the within-family ('direct') beta of 565 independent SNPs significant at p < 5 × 10^-8. ...

Family-GWAS reveals effects of environment and mating on genetic associations

... Although the precise cause of bibliophobia is unknown, it has been associated with anxiety disorders, fear of failure, and unpleasant reading experiences . Ciulkinyte et al. (2024) define dyslexia as a neurological disorder with a hereditary predisposition. It is crucial to remember that these are broad classifications, and children can occasionally have dyslexia and bibliophobia together. ...

Genetic neurodevelopmental clustering and dyslexia

Molecular Psychiatry

... pathways. In particular, gene set enrichment analysis of the head circumference variants found several enriched gene sets in various cancers and the p53, Wnt, and ErbB signaling pathways (Supplementary Table S1) [4]. In addition, a few studies have previously reported an association between head size at birth and the risk of developing certain types of cancer later in life [5][6][7]. ...

Genetic variants for head size share genes and pathways with cancer

Cell Reports Medicine

... This complexity is underscored by a growing body of research indicating that the disorder does not stem from a singular genetic anomaly [4] but rather from a constellation of genetic susceptibilities [5][6][7][8] that interact with environmental stressors and epigenetic modifications. Genome-wide association studies (GWAS), such as those by Mullins et al. [9], have illuminated the polygenic nature of BD, identifying numerous risk alleles [10,11] that, in concert with life events and other external influences, can precipitate the onset of the disorder. Moreover, gene-environment interactions have also been highlighted [12], demonstrating how specific genetic predispositions may render individuals more susceptible to environmental triggers, thereby exacerbating the risk of developing BD. ...

Fine-mapping genomic loci refines bipolar disorder risk genes

... Meta-analyses have shown significantly lower concentrations of healthy microbes (i.e., Actinobacteria, Lentisphaerae, and Verrucomicrobia) (Petakh et al., 2024;Hemmings et al., 2017) and higher concentrations in unhealthy microbes (i.e., Enterococcus, Escherichia, and Shigella) (Hemmings et al., 2017) in individuals with PTSD. These changes may be the result of chronic stress and response to cortisol (Tetel et al., 2018) and stress hormones or inflammatory pathways, or the changes may occur due to the activation of pre-existing epigenetic factors within the gut (He et al., 2024). Additionally, individuals with significant dysbiosis experience more severe traumatic symptomatology (Hemmings et al., 2017). ...

Potential causal association between gut microbiome and posttraumatic stress disorder

Translational Psychiatry

... In turn, research on educational fields can enrich genomic studies of education and social stratification. Genomic research on attainment (defined simply as the number of years a person spends in education; EA), income 28 and occupational status 29,30 is valuable. However, the focus on these conventional measures of a person's position in a socioeconomic hierarchy, ignores the diversity of preferences, traits, and skills that educational pathways entail. ...

Associations between common genetic variants and income provide insights about the socioeconomic health gradient

... While an emphasis on such high-risk cohorts is attractive, and often feasible, this is tempered by the reality that most people within these 'high-risk' groups will not develop depression (10). So, an additional consideration is whether other individual factors, notably polygenic risk scores (PRS) or other independent markers, may collectively prove to add substantive value to such efforts (11). ...

Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults
  • Citing Article
  • October 2023

Early Intervention in Psychiatry

... While PTSD can only develop if a traumatic event has been experienced, the risk of developing it, is partly determined by genetic liability. Twin-based studies estimated PTSD heritability between 40-60% and genome-wide association studies (GWAS) demonstrated that PTSD is characterized by high polygenicity, signifying that the risk of developing the disorder involves the influence of numerous genes [2,3]. ...

Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders

... Two other studies provide strong support for the idea that internalizing psychopathology and internalizing-related trait measures tap very similar genetic influences. First, a Genomic SEM investigation of depression using data from clinical and community cohorts found that the best-fitting model was one with factors based on symptom type (e.g., appetite, vegetative, cognitive/mood, gating) and a mix of clinical and community items included on each (Adams et al., 2023). Second, a twin study demonstrated that an internalizing psychopathology factor at mean age 41 (based on Diagnostic and Statistical Manual of Mental Disorders, 3rd edition, revised [DSM-III-R] GAD, MDD, and PTSD symptom counts) was strongly associated with internalizing-related traits (based on self-reported depression, anxiety, and/or PTSD symptom questionnaires) in the same individuals at mean ages 56 (r g = .66, ...

Genetic structure of major depression symptoms across clinical and community cohorts