Publications by authors named "Naomi R Wray"

259 Publications

Seven short reflections on the notion of schizophrenia.

Schizophr Res 2021 Oct 8. Epub 2021 Oct 8.

Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. Electronic address:

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http://dx.doi.org/10.1016/j.schres.2021.09.026DOI Listing
October 2021

Polygenic burden could explain high rates of affective disorders in a community with restricted founder population.

Am J Med Genet B Neuropsychiatr Genet 2021 Oct 10. Epub 2021 Oct 10.

Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California.

This study investigates if genetic factors could contribute to the high rate of mood disorders reported in a U.S. community known to have a restricted early founder population (confirmed here through runs of homozygosity analysis). Polygenic scores (PGSs) for eight common diseases, disorders, or traits, including psychiatric disorders, were calculated in 274 participants (125 mood disorder cases) who each reported three or four grandparents born in the community. Ancestry-matched controls were selected from the UK Biobank (UKB; three sets of N = 1,822 each). The mean PGSs were significantly higher in the community for major depression PRS (p = 2.1 × 10 , 0.56 SD units), bipolar disorder (p = 2.5 × 10 , 0.56 SD units), and schizophrenia (p = 3.8 × 10 , 0.64 SD units). The PGSs were not significantly different between the community participants and UKB controls for the traits of body mass index, Type 2 diabetes, coronary artery disease, and chronotype. The mean PGSs for height were significantly lower in the community sample compared to controls (-0.21 SD units, p = 1.2 × 10 ). The results are consistent with enrichment of polygenic risk factors for psychiatric disorders in this community.
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http://dx.doi.org/10.1002/ajmg.b.32876DOI Listing
October 2021

Discovery and implications of polygenicity of common diseases.

Science 2021 09 23;373(6562):1468-1473. Epub 2021 Sep 23.

Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia.

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http://dx.doi.org/10.1126/science.abi8206DOI Listing
September 2021

Methylome-wide association study of early life stressors and adult mental health.

Hum Mol Genet 2021 Sep 15. Epub 2021 Sep 15.

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

The environment and events that we are exposed to in utero, during birth and in early childhood influence our future physical and mental health. The underlying mechanisms that lead to these outcomes are unclear, but long-term changes in epigenetic marks, such as DNA methylation, could act as a mediating factor or biomarker. DNA methylation data was assayed at 713522 CpG sites from 9537 participants of the Generation Scotland: Scottish Family Health Study, a family-based cohort with extensive genetic, medical, family history and lifestyle information. Methylome-wide association studies of eight early life environment phenotypes and two adult mental health phenotypes (major depressive disorder and brief resilience scale) were conducted using DNA methylation data collected from adult whole blood samples. Two genes involved with different developmental pathways (PRICKLE2 and ABI1) were annotated to CpG sites associated with preterm birth (P < 1.27 × 10-9). A further two genes important to the development of sensory pathways (SOBP and RPGRIP1) were annotated to sites associated with low birth weight (P < 4.35 × 10-8). The examination of methylation profile scores and genes and gene-sets annotated from associated CpGs sites found no evidence of overlap between the early life environment and mental health conditions. Birth date was associated with a significant difference in estimated lymphocyte and neutrophil counts. Previous studies have shown that early life environments influence the risk of developing mental health disorders later in life; however, this study found no evidence that this is mediated by stable changes to the methylome detectable in peripheral blood.
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http://dx.doi.org/10.1093/hmg/ddab274DOI Listing
September 2021

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Nat Genet 2021 09 6;53(9):1311-1321. Epub 2021 Sep 6.

Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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http://dx.doi.org/10.1038/s41588-021-00923-xDOI Listing
September 2021

Polygenic Risk Scores Derived From Varying Definitions of Depression and Risk of Depression.

JAMA Psychiatry 2021 Oct;78(10):1152-1160

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.

Importance: Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies.

Objective: To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs).

Design, Setting, And Participants: In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021.

Main Outcome And Measures: Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health.

Results: Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress.

Conclusions And Relevance: These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.
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http://dx.doi.org/10.1001/jamapsychiatry.2021.1988DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358814PMC
October 2021

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts.

Biol Psychiatry 2021 Nov 4;90(9):611-620. Epub 2021 May 4.

Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia; Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. Electronic address:

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.

Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.

Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.

Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.
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http://dx.doi.org/10.1016/j.biopsych.2021.04.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500913PMC
November 2021

Genetic risk for chronic pain is associated with lower antidepressant effectiveness: Converging evidence for a depression subtype.

Aust N Z J Psychiatry 2021 Jul 16:48674211031491. Epub 2021 Jul 16.

Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.

Introduction: Chronic pain and depression are highly comorbid and difficult-to-treat disorders. We previously showed this comorbidity is associated with higher depression severity, lower antidepressant treatment effectiveness and poorer prognosis in the Australian Genetics of Depression Study.

Objective: The current study aimed to assess whether a genetic liability to chronic pain is associated with antidepressant effectiveness over and above the effect of genetic factors for depression in a sample of 12,863 Australian Genetics of Depression Study participants.

Methods: Polygenic risk scores were calculated using summary statistics from genome-wide association studies of multisite chronic pain and major depression. Cumulative linked regressions were employed to assess the association between polygenic risk scores and antidepressant treatment effectiveness across 10 different medications.

Results: Mixed-effects logistic regressions showed that individual genetic propensity for chronic pain, but not major depression, was significantly associated with patient-reported chronic pain (Pain OR = 1.17 [1.12, 1.22]; MD OR = 1.01 [0.98, 1.06]). Significant associations were also found between lower antidepressant effectiveness and genetic risk for chronic pain or for major depression. However, a fully adjusted model showed the effect of Pain (adjOR = 0.93 [0.90, 0.96]) was independent of MD (adjOR = 0.96 [0.93, 0.99]). Sensitivity analyses were performed to assess the robustness of these results. After adjusting for depression severity measures (i.e. age of onset; number of depressive episodes; interval between age at study participation and at depression onset), the associations between Pain and patient-reported chronic pain with lower antidepressant effectiveness remained significant (0.95 [0.92, 0.98] and 0.84 [0.78, 0.90], respectively).

Conclusion: These results suggest genetic risk for chronic pain accounted for poorer antidepressant effectiveness, independent of the genetic risk for major depression. Our results, along with independent converging evidence from other studies, point towards a difficult-to-treat depression subtype characterised by comorbid chronic pain. This finding warrants further investigation into the implications for biologically based nosology frameworks in pain medicine and psychiatry.
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http://dx.doi.org/10.1177/00048674211031491DOI Listing
July 2021

Genomic partitioning of inbreeding depression in humans.

Am J Hum Genet 2021 08 1;108(8):1488-1501. Epub 2021 Jul 1.

Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia.

Across species, offspring of related individuals often exhibit significant reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data. We analyzed the phenomes and genomes of ∼350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, significant enrichment of ID within genomic regions with high recombination rates (>21-fold; p < 10), with conserved function across species (>19-fold; p < 10), and within regulatory elements such as DNase I hypersensitive sites (∼5-fold; p = 8.9 × 10). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome. Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases.
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http://dx.doi.org/10.1016/j.ajhg.2021.06.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387293PMC
August 2021

Gene action, genetic variation, and GWAS: A user-friendly web tool.

PLoS Genet 2021 05 20;17(5):e1009548. Epub 2021 May 20.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Fisher's partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.
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http://dx.doi.org/10.1371/journal.pgen.1009548DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136673PMC
May 2021

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction.

Am J Hum Genet 2021 06 7;108(6):1001-1011. Epub 2021 May 7.

The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark. Electronic address:

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.
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http://dx.doi.org/10.1016/j.ajhg.2021.04.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206385PMC
June 2021

Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness.

Front Psychiatry 2021 12;12:643609. Epub 2021 Apr 12.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study ( = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37-2.54]), recent suicide attempt (OR = 1.88 [1.14-3.09]), higher use of tobacco (OR = 1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68-0.83]), escitalopram (OR = 0.75 [0.67-0.85]) and venlafaxine (OR = 0.78 [0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30-0.67]), escitalopram (OR = 0.45 [0.27-0.74]) and citalopram (OR = 0.32 [0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.
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http://dx.doi.org/10.3389/fpsyt.2021.643609DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072020PMC
April 2021

Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia.

Eur J Hum Genet 2021 Apr 27. Epub 2021 Apr 27.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.

Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R) of the case-control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R) of 0.027 (P value = 4.6 × 10), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96-5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS.
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http://dx.doi.org/10.1038/s41431-021-00885-yDOI Listing
April 2021

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals.

Am J Hum Genet 2021 05 2;108(5):786-798. Epub 2021 Apr 2.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia. Electronic address:

Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive (h), dominance (δ) and additive-by-additive (η) genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide a new theory to predict standard errors estimated using either least-squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1 M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits h¯=0.208). In contrast, the average estimate of δ¯ across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance η¯ across the traits was 0.055, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.
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http://dx.doi.org/10.1016/j.ajhg.2021.02.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205999PMC
May 2021

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders.

Genome Biol 2021 03 26;22(1):90. Epub 2021 Mar 26.

Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia.

Background: People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease.

Results: We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights.

Conclusions: We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.
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http://dx.doi.org/10.1186/s13059-021-02275-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004462PMC
March 2021

Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum.

Biol Psychiatry 2021 09 8;90(5):317-327. Epub 2021 Jan 8.

Department of Biological Sciences, Purdue University, West Lafayette, Indiana. Electronic address:

Background: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum.

Methods: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers).

Results: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD.

Conclusions: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.
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http://dx.doi.org/10.1016/j.biopsych.2020.12.028DOI Listing
September 2021

Association of Antihypertensive Drug Target Genes With Psychiatric Disorders: A Mendelian Randomization Study.

JAMA Psychiatry 2021 Jun;78(6):623-631

Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia.

Importance: Observational studies have reported associations between antihypertensive medication and psychiatric disorders, although the reported direction of association appears to be dependent on drug class.

Objective: To estimate the potential effect of different antihypertensive drug classes on schizophrenia, bipolar disorder, and major depressive disorder.

Design, Setting, And Participants: This 2-sample mendelian randomization study assessed the association between a single-nucleotide variant (SNV) and drug target gene expression derived from existing expression quantitative trait loci (eQTL) data in blood (sample 1) and the SNV-disease association from published case-control genome-wide association studies (sample 2). Significant associations were corroborated using published brain eQTL and protein QTL data. Participants included 40 675 patients with schizophrenia and 64 643 controls, 20 352 patients with bipolar disorder and 31 358 controls, and 135 458 patients with major depressive disorder and 344 901 controls. Blood eQTL levels were measured in 31 684 individuals from 37 cohorts (eQTLGen consortium); prefrontal cortex eQTLs were measured from the PsychENCODE resource in 1387 individuals; and protein QTLs were measured in cerebral spinal fluid from 544 individuals and plasma from 818 individuals. Data were collected from October 4, 2019, to June 1, 2020, and analyzed from October 14, 2019, to June 6, 2020.

Exposures: Expression levels of antihypertensive drug target genes as proxies for drug exposure, and genetic variants robustly associated with the expression of these genes as mendelian randomization instruments.

Main Outcomes And Measures: Risk for schizophrenia, bipolar disorder, and major depressive disorder.

Results: A 1-SD lower expression of the angiotensin-converting enzyme (ACE) gene in blood was associated with lower systolic blood pressure of 4.0 (95% CI, 2.7-5.3) mm Hg, but increased risk of schizophrenia (odds ratio [OR], 1.75; 95% CI, 1.28-2.38; P = 3.95 × 10-4). A concordant direction of association was also observed between ACE expression in prefrontal cortex (OR, 1.33; 95% CI, 1.13-1.56) and ACE protein levels in cerebral spinal fluid (OR per 1-SD decrease, 1.12; 95% CI, 1.05-1.19) and plasma (OR per 1-SD decrease, 1.04; 95% CI, 1.01-1.07). We found no evidence for an association between genetically estimated SBP and schizophrenia risk.

Conclusions And Relevance: Findings suggest an adverse association of lower ACE messenger RNA and protein levels with schizophrenia risk. These findings warrant greater pharmacovigilance and further investigation into the effect of ACE inhibitors, particularly those that are centrally acting, on psychiatric symptoms in patients with schizophrenia, as well as the role of ACE inhibitor use in late-onset schizophrenia.
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http://dx.doi.org/10.1001/jamapsychiatry.2021.0005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948097PMC
June 2021

Genome-wide gene expression changes in postpartum depression point towards an altered immune landscape.

Transl Psychiatry 2021 03 4;11(1):155. Epub 2021 Mar 4.

Division of Maternal-Fetal Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, 27514, USA.

Maternal postpartum depression (PPD) is a significant public health concern due to the severe negative impact on maternal and child health and well-being. In this study, we aimed to identify genes associated with PPD. To do this, we investigated genome-wide gene expression profiles of pregnant women during their third trimester of pregnancy and tested the association of gene expression with perinatal depressive symptoms. A total of 137 women from a cohort from the University of North Carolina, USA were assessed. The main phenotypes analysed were Edinburgh Postnatal Depression Scale (EPDS) scores at 2 months postpartum and PPD (binary yes/no) based on an EPDS cutoff of 10. Illumina NextSeq500/550 transcriptomic sequencing from whole blood was analysed using the edgeR package. We identified 71 genes significantly associated with postpartum depression scores at 2 months, after correction for multiple testing at 5% FDR. These included several interesting candidates including TNFRSF17, previously reported to be significantly upregulated in women with PPD and MMP8, a matrix metalloproteinase gene, associated with depression in a genome-wide association study. Functional annotation of differentially expressed genes revealed an enrichment of immune response-related biological processes. Additional analysis of genes associated with changes in depressive symptoms from recruitment to 2 months postpartum identified 66 genes significant at an FDR of 5%. Of these genes, 33 genes were also associated with depressive symptoms at 2 months postpartum. Comparing the results with previous studies, we observed that 15.4% of genes associated with PPD in this study overlapped with 700 core maternal genes that showed significant gene expression changes across multiple brain regions (P = 7.9e-05) and 29-53% of the genes were also associated with estradiol changes in a pharmacological model of depression (P values range = 1.2e-4-2.1e-14). In conclusion, we identified novel genes and validated genes previously associated with oestrogen sensitivity in PPD. These results point towards the role of an altered immune transcriptomic landscape as a vulnerability factor for PPD.
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http://dx.doi.org/10.1038/s41398-021-01270-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933180PMC
March 2021

Schizophrenia polygenic risk scores in youth mental health: preliminary associations with diagnosis, clinical stage and functioning.

BJPsych Open 2021 Feb 22;7(2):e58. Epub 2021 Feb 22.

Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Australia.

Background: The schizophrenia polygenic risk score (SCZ-PRS) is an emerging tool in psychiatry.

Aims: We aimed to evaluate the utility of SCZ-PRS in a young, transdiagnostic, clinical cohort.

Method: SCZ-PRSs were calculated for young people who presented to early-intervention youth mental health clinics, including 158 patients of European ancestry, 113 of whom had longitudinal outcome data. We examined associations between SCZ-PRS and diagnosis, clinical stage and functioning at initial assessment, and new-onset psychotic disorder, clinical stage transition and functional course over time in contact with services.

Results: Compared with a control group, patients had elevated PRSs for schizophrenia, bipolar disorder and depression, but not for any non-psychiatric phenotype (for example cardiovascular disease). Higher SCZ-PRSs were elevated in participants with psychotic, bipolar, depressive, anxiety and other disorders. At initial assessment, overall SCZ-PRSs were associated with psychotic disorder (odds ratio (OR) per s.d. increase in SCZ-PRS was 1.68, 95% CI 1.08-2.59, P = 0.020), but not assignment as clinical stage 2+ (i.e. discrete, persistent or recurrent disorder) (OR = 0.90, 95% CI 0.64-1.26, P = 0.53) or functioning (R = 0.03, P = 0.76). Longitudinally, overall SCZ-PRSs were not significantly associated with new-onset psychotic disorder (OR = 0.84, 95% CI 0.34-2.03, P = 0.69), clinical stage transition (OR = 1.02, 95% CI 0.70-1.48, P = 0.92) or persistent functional impairment (OR = 0.84, 95% CI 0.52-1.38, P = 0.50).

Conclusions: In this preliminary study, SCZ-PRSs were associated with psychotic disorder at initial assessment in a young, transdiagnostic, clinical cohort accessing early-intervention services. Larger clinical studies are needed to further evaluate the clinical utility of SCZ-PRSs, especially among individuals with high SCZ-PRS burden.
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http://dx.doi.org/10.1192/bjo.2021.14DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058892PMC
February 2021

GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression.

Nat Commun 2021 02 19;12(1):1146. Epub 2021 Feb 19.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.

Genetic factors are recognized to contribute to peptic ulcer disease (PUD) and other gastrointestinal diseases, such as gastro-oesophageal reflux disease (GORD), irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). Here, genome-wide association study (GWAS) analyses based on 456,327 UK Biobank (UKB) individuals identify 8 independent and significant loci for PUD at, or near, genes MUC1, MUC6, FUT2, PSCA, ABO, CDX2, GAST and CCKBR. There are previously established roles in susceptibility to Helicobacter pylori infection, response to counteract infection-related damage, gastric acid secretion or gastrointestinal motility for these genes. Only two associations have been previously reported for duodenal ulcer, here replicated trans-ancestrally. The results highlight the role of host genetic susceptibility to infection. Post-GWAS analyses for PUD, GORD, IBS and IBD add insights into relationships between these gastrointestinal diseases and their relationships with depression, a commonly comorbid disorder.
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http://dx.doi.org/10.1038/s41467-021-21280-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895976PMC
February 2021

Widespread signatures of natural selection across human complex traits and functional genomic categories.

Nat Commun 2021 02 19;12(1):1164. Epub 2021 Feb 19.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k-547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.
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http://dx.doi.org/10.1038/s41467-021-21446-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896067PMC
February 2021

Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals.

Nat Commun 2021 02 16;12(1):1050. Epub 2021 Feb 16.

Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.

Attributing the similarity between individuals to genetic and non-genetic factors is central to genetic analyses. In this paper we use the genomic relationship ([Formula: see text]) among 417,060 individuals to investigate the phenotypic covariance between pairs of individuals for 32 traits across the spectrum of relatedness, from unrelated pairs through to identical twins. We find linear relationships between phenotypic covariance and [Formula: see text] that agree with the SNP-based heritability ([Formula: see text]) in unrelated pairs ([Formula: see text]), and with pedigree-estimated heritability in close relatives ([Formula: see text]). The covariance increases faster than [Formula: see text] in distant relatives ([Formula: see text]), and we attribute this to imperfect linkage disequilibrium between causal variants and the common variants used to construct [Formula: see text]. We also examine the effect of assortative mating on heritability estimates from different experimental designs. We find that full-sib identity-by-descent regression estimates for height (0.66 s.e. 0.07) are consistent with estimates from close relatives (0.82 s.e. 0.04) after accounting for the effect of assortative mating.
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http://dx.doi.org/10.1038/s41467-021-21283-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886899PMC
February 2021

Analysis of common genetic variation and rare CNVs in the Australian Autism Biobank.

Mol Autism 2021 02 10;12(1):12. Epub 2021 Feb 10.

Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism.

Methods: Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain.

Results: The ASD (p = 6.1e-13), sibling (p = 4.9e-3) and unrelated (p = 3.0e-3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height-a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e-3) and parents (r = 0.17, p = 8.0e-7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e-3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants.

Limitations: This dataset is modest in size, and the publicly-available genome-wide-association-study (GWAS) summary statistics used to calculate PGS for ASD and other traits are relatively underpowered.

Conclusions: We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens (blood, urine, stool, hair).
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http://dx.doi.org/10.1186/s13229-020-00407-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874616PMC
February 2021

Publisher Correction: Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes.

Nat Commun 2021 Feb 8;12(1):988. Epub 2021 Feb 8.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.

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http://dx.doi.org/10.1038/s41467-021-21294-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870948PMC
February 2021

Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status.

JAMA Psychiatry 2021 Apr;78(4):387-397

Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark.

Importance: Combining information on polygenic risk scores (PRSs) with other known risk factors could potentially improve the identification of risk of depression in the general population. However, to our knowledge, no study has estimated the association of PRS with the absolute risk of depression, and few have examined combinations of the PRS and other important risk factors, including parental history of psychiatric disorders and socioeconomic status (SES), in the identification of depression risk.

Objective: To assess the individual and joint associations of PRS, parental history, and SES with relative and absolute risk of early-onset depression.

Design, Setting, And Participants: This case-cohort study included participants from the iPSYCH2012 sample, a case-cohort sample of all singletons born in Denmark between May 1, 1981, and December 31, 2005. Hazard ratios (HRs) and absolute risks were estimated using Cox proportional hazards regression for case-cohort designs.

Exposures: The PRS for depression; SES measured using maternal educational level, maternal marital status, and paternal employment; and parental history of psychiatric disorders (major depression, bipolar disorder, other mood or psychotic disorders, and other psychiatric diagnoses).

Main Outcomes And Measures: Hospital-based diagnosis of depression from inpatient, outpatient, or emergency settings.

Results: Participants included 17 098 patients with depression (11 748 [68.7%] female) and 18 582 (9429 [50.7%] male) individuals randomly selected from the base population. The PRS, parental history, and lower SES were all significantly associated with increased risk of depression, with HRs ranging from 1.32 (95% CI, 1.29-1.35) per 1-SD increase in PRS to 2.23 (95% CI, 1.81-2.64) for maternal history of mood or psychotic disorders. Fully adjusted models had similar effect sizes, suggesting that these risk factors do not confound one another. Absolute risk of depression by the age of 30 years differed substantially, depending on an individual's combination of risk factors, ranging from 1.0% (95% CI, 0.1%-2.0%) among men with high SES in the bottom 2% of the PRS distribution to 23.7% (95% CI, 16.6%-30.2%) among women in the top 2% of PRS distribution with a parental history of psychiatric disorders.

Conclusions And Relevance: This study suggests that current PRSs for depression are not more likely to be associated with major depressive disorder than are other known risk factors; however, they may be useful for the identification of risk in conjunction with other risk factors.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.4172DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807393PMC
April 2021

Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes.

Nat Commun 2021 01 7;12(1):20211. Epub 2021 Jan 7.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.

Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption levels, and propose a correction procedure to mitigate the MLC-induced biases. The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.
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http://dx.doi.org/10.1038/s41467-020-20237-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804181PMC
January 2021

Genome-wide Meta-analysis Finds the ACSL5-ZDHHC6 Locus Is Associated with ALS and Links Weight Loss to the Disease Genetics.

Cell Rep 2020 10;33(4):108323

Centre for Clinical Research, The University of Queensland, Brisbane QLD, Australia; Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane QLD, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane QLD, Australia.

We meta-analyze amyotrophic lateral sclerosis (ALS) genome-wide association study (GWAS) data of European and Chinese populations (84,694 individuals). We find an additional significant association between rs58854276 spanning ACSL5-ZDHHC6 with ALS (p = 8.3 × 10), with replication in an independent Australian cohort (1,502 individuals; p = 0.037). Moreover, B4GALNT1, G2E3-SCFD1, and TRIP11-ATXN3 are identified using a gene-based analysis. ACSL5 has been associated with rapid weight loss, as has another ALS-associated gene, GPX3. Weight loss is frequent in ALS patients and is associated with shorter survival. We investigate the effect of the ACSL5 and GPX3 single-nucleotide polymorphisms (SNPs), using longitudinal body composition and weight data of 77 patients and 77 controls. In patients' fat-free mass, although not significant, we observe an effect in the expected direction (rs58854276: -2.1 ± 1.3 kg/A allele, p = 0.053; rs3828599: -1.0 ± 1.3 kg/A allele, p = 0.22). No effect was observed in controls. Our findings support the increasing interest in lipid metabolism in ALS and link the disease genetics to weight loss in patients.
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http://dx.doi.org/10.1016/j.celrep.2020.108323DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610013PMC
October 2020

Could Polygenic Risk Scores Be Useful in Psychiatry?: A Review.

JAMA Psychiatry 2021 Feb;78(2):210-219

Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia.

Importance: Polygenic risk scores (PRS) are predictors of the genetic susceptibility to diseases, calculated for individuals as weighted counts of thousands of risk variants in which the risk variants and their weights have been identified in genome-wide association studies. Polygenic risk scores show promise in aiding clinical decision-making in many areas of medical practice. This review evaluates the potential use of PRS in psychiatry.

Observations: On their own, PRS will never be able to establish or definitively predict a diagnosis of common complex conditions (eg, mental health disorders), because genetic factors only contribute part of the risk and PRS will only ever capture part of the genetic contribution. Combining PRS with other risk factors has potential to improve outcome prediction and aid clinical decision-making (eg, determining follow-up options for individuals seeking help who are at clinical risk of future illness). Prognostication of adverse physical health outcomes or response to treatment in clinical populations are of great interest for psychiatric practice, but data from larger samples are needed to develop and evaluate PRS.

Conclusions And Relevance: Polygenic risk scores will contribute to risk assessment in clinical psychiatry as it evolves to combine information from molecular, clinical, and lifestyle metrics. The genome-wide genotype data needed to calculate PRS are inexpensive to generate and could become available to psychiatrists as a by-product of practices in other medical specialties. The utility of PRS in clinical psychiatry, as well as ethical issues associated with their use, should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. Clinical psychiatry has lagged behind other fields of health care in its use of new technologies and routine clinical data for research. Now is the time to catch up.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.3042DOI Listing
February 2021
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