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Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely., Author summary An objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence directly both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes—educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder—is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.

Massive genetic analysis identifies critical pathways and cell types involved in pathogenesis of amyotrophic lateral sclerosis., Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic cortical interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated five differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

Background Identifying causal risk factors for self-harm is essential to inform preventive interventions. Epidemiological studies have identified risk factors associated with self-harm, but these associations can be subject to confounding. By implementing genetically informed methods to better account for confounding, this study aimed to better identify plausible causal risk factors for self-harm. Methods and findings Using summary statistics from 24 genome-wide association studies (GWASs) comprising 16,067 to 322,154 individuals, polygenic scores (PSs) were generated to index 24 possible individual risk factors for self-harm (i.e., mental health vulnerabilities, substance use, cognitive traits, personality traits, and physical traits) among a subset of UK Biobank participants (N = 125,925, 56.2% female) who completed an online mental health questionnaire in the period from 13 July 2016 to 27 July 2017. In total, 5,520 (4.4%) of these participants reported having self-harmed in their lifetime. In binomial regression models, PSs indexing 6 risk factors (major depressive disorder [MDD], attention deficit/hyperactivity disorder [ADHD], bipolar disorder, schizophrenia, alcohol dependence disorder, and lifetime cannabis use) predicted self-harm, with effect sizes ranging from odds ratio (OR) = 1.05 (95% CI 1.02 to 1.07, q = 0.008) for lifetime cannabis use to OR = 1.20 (95% CI 1.16 to 1.23, q = 1.33 × 10−35) for MDD. No systematic differences emerged between suicidal and non-suicidal self-harm. To further probe causal relationships, two-sample Mendelian randomisation (MR) analyses were conducted, with MDD, ADHD, and schizophrenia emerging as the most plausible causal risk factors for self-harm. The genetic liabilities for MDD and schizophrenia were associated with self-harm independently of diagnosis and medication. Main limitations include the lack of representativeness of the UK Biobank sample, that self-harm was self-reported, and the limited power of some of the included GWASs, potentially leading to possible type II error. Conclusions In addition to confirming the role of MDD, we demonstrate that ADHD and schizophrenia likely play a role in the aetiology of self-harm using multivariate genetic designs for causal inference. Among the many individual risk factors we simultaneously considered, our findings suggest that systematic detection and treatment of core psychiatric symptoms, including psychotic and impulsivity symptoms, may be beneficial among people at risk for self-harm., Kai Xiang Lim and co-workers use genetic instruments to investigate risk factors associated with self-harm., Author summary Why was this study done? Self-harm is an act of self-injury or self-poisoning carried out by an individual and can be subdivided into suicidal and non-suicidal self-harm. Self-harm has been found to be associated with a range of risk factors, but these associations can be subject to confounding. Using genetic information to help strengthen causal inference can help to identify plausibly causal risk factors for self-harm. What did the researchers do and find? We used data from UK Biobank participants, who provided their genetic information and answered questions about their engagement in self-harming. We investigated 24 potential individual risk factors for self-harm. Out of these 24 risk factors, major depressive disorder, attention-deficit hyperactivity disorder and schizophrenia appeared to be the most plausible causal risk factors for self-harm. No difference emerged between risk factors for non-suicidal and suicidal self-harm. What do these findings mean? Using a different design, with different sets of assumptions, than epidemiological studies that are not genetically informed, we showed that psychiatric symptoms related to major depressive disorder, attention deficit/hyperactivity disorder, and schizophrenia plausibly play a role as individual risk factors leading to an increased risk of self-harm. Detecting and treating psychiatric symptoms, such as impulsivity and psychotic symptoms, may be helpful for people at risk for self-harm.

Importance: Exposure to bullying is a prevalent experience with adverse consequences throughout the life span. Individual vulnerabilities and traits, such as preexisting mental health problems, may be associated with increased likelihood of experiencing bullying. Identifying such individual vulnerabilities and traits is essential for a better understanding of the etiology of exposure to bullying and for tailoring effective prevention.Objective: To identify individual vulnerabilities and traits associated with exposure to bullying in childhood and adolescence.Design, Setting, and Participants: For this study, data were drawn from the Avon Longitudinal Study of Parents and Children (ALSPAC), a population-based birth cohort study. The initial ALSPAC sample consisted of 14 062 children born to women residing in Avon, United Kingdom, with an expected date of delivery between April 1, 1991, and December 31, 1992. Collection of the ALSPAC data began in September 6, 1990, and the last follow-up assessment of exposure to bullying was conducted when participants were 13 years of age. Data analysis was conducted from November 1, 2017, to January 1, 2019.Exposures: The polygenic score approach was used to derive genetic proxies that indexed vulnerabilities and traits. A total of 35 polygenic scores were computed for a range of mental health vulnerabilities (eg, depression) and traits related to cognition (eg, intelligence), personality (eg, neuroticism), and physical measures (eg, body mass index), as well as negative controls (eg, osteoporosis).Main Outcomes and Measures: Single and multi-polygenic score regression models were fitted to test the association between indexed traits and exposure to bullying. Children completed the Bullying and Friendship Interview Schedule at the ages of 8, 10, and 13 years. A mean score of exposure to bullying across ages was used as the main outcome.Results: A total of 5028 genotyped individuals (2481 boys and 2547 girls) with data on exposure to bullying were included. Among the 35 initially included polygenic scores, 11 were independently associated with exposure to bullying; no significant association was detected for the 24 remaining scores. In multivariable analyses, 5 polygenic scores were associated with exposure to bullying; the largest associations were present for genetic risk relating to mental health vulnerabilities, including diagnosis of depression (standardized b = 0.065; 95% CI, 0.035-0.095) and attention-deficit/hyperactivity disorder (standardized b = 0.063; 95% CI, 0.035-0.091), followed by risk taking (standardized b = 0.041; 95% CI, 0.013-0.069), body mass index (standardized b = 0.036; 95% CI, 0.008-0.064), and intelligence (standardized b = -0.031; 95% CI, -0.059 to 0.003).Conclusion and Relevance: Using the multi-polygenic score approach, the findings implicate preexisting mental health vulnerabilities as risk factors for exposure to bullying. A mechanistic understanding of how these vulnerabilities link to exposure of bullying is important to inform prevention strategies.

INTRODUCTION: It’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. METHODS: Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. RESULTS: All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. CONCLUSIONS: The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

BackgroundMultiple individual vulnerabilities and traits are phenotypically associated with suicidal and non-suicidal self-harm. However, associations between these risk factors and self-harm are subject to confounding. We implemented genetically informed methods to better identify individual risk factors for self-harm.MethodsUsing genotype data and online Mental Health Questionnaire responses in the UK Biobank sample (N = 125,925), polygenic risk scores (PRS) were generated to index 24 plausible individual risk factors for self-harm in the following domains: mental health vulnerabilities, substance use phenotypes, cognitive traits, personality traits and physical traits. PRS were entered as predictors in binomial regression models to predict self-harm. Multinomial regressions were used to model suicidal and non-suicidal self-harm. To further probe the causal nature of these relationships, two-sample Mendelian Randomisation (MR) analyses were conducted for significant risk factors identified in PRS analyses.OutcomesSelf-harm was predicted by PRS indexing six individual risk factors, which are major depressive disorder (MDD), attention deficit/hyperactivity disorder (ADHD), bipolar disorder, schizophrenia, alcohol dependence disorder (ALC) and lifetime cannabis use. Effect sizes ranged from β = 0.044 (95% CI: 0.016 to 0.152) for PRS for lifetime cannabis use, to β = 0.179 (95% CI: 0.152 to 0.207) for PRS for MDD. No systematic distinctions emerged between suicidal and non-suicidal self-harm. In follow-up MR analyses, MDD, ADHD and schizophrenia emerged as plausible causal risk factors for self-harm.InterpretationAmong a range of potential risk factors leading to self-harm, core predictors were found among psychiatric disorders. In addition to MDD, liabilities for schizophrenia and ADHD increased the risk for self-harm. Detection and treatment of core symptoms of these conditions, such as psychotic or impulsivity symptoms, may benefit self-harming patients.FundingLim is funded by King’s International Postgraduate Research Scholarship. Dr Pingault is funded by grant MQ16IP16 from MQ: Transforming Mental Health. Dr Coleman is supported by the UK National Institute of Health Research Maudsley Biomedical Research Centre. MRC grant MR/N015746/1 to CML and PFO’R. Dr Hagenaars is funded by the Medical Research Council (MR/S0151132). Kylie P. Glanville is funded by the UK Medical Research Council (PhD studentship; grant MR/N015746/1). This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Research in ContextEvidence before this studyA search was conducted on PubMed for literature from inception until 1st May 2019 using terms related to suicidal self-harm (SSH) and non-suicidal self-harm (NSSH), as well as polygenic risk scores (PRS), (“self-harm”[All Fields] OR “self-injurious”[All Fields] OR “self-mutilation”[All Fields] OR “suicide”[All Fields]) AND (“polygenic”[All Fields] OR “multifactorial inheritance”[All Fields]). Similar search was done for Mendelian Randomisation (MR), replacing “multifactorial inheritance” and “polygenic” with “Mendelian Randomisation/Randomization”. Evidence was included only if the study had used PRS or MR method to predict self-harm phenotypes using risk factors of self-harm. Ten papers for PRS and no paper for MR were identified.There were mixed results for PRS studies. PRS for MDD predicted SSH in two studies but not in another two studies. PRS for depressive symptoms predicted SSH but not NSSH. PRS for schizophrenia predicted SSH in one but not in another two studies. PRS for bipolar disorder predicted SSH in one study but did not predict SSH nor NSSH in another two studies.Added value of this studyBy using a large population-based sample, we systematically studied individual vulnerabilities and traits that can potentially lead to self-harm, including mental health vulnerabilities, substance use phenotypes, cognitive traits, personality traits and physical traits, summing up to 24 PRS as genetic proxies for 24 risk factors. We conducted MR to strengthen causal inference. We further distinguished non-suicidal self-harm (NSSH) and suicidal self-harm (SSH).Apart from PRS for schizophrenia, MDD and bipolar disorder, novel PRS were also identified to be associated with self-harm, which are PRS for attention-deficit hyperactivity disorder (ADHD), cannabis use and alcohol dependence. A larger sample size allowed us to confirm positive findings from the previously mixed literature regarding the associations between PRS for MDD, bipolar disorder, and schizophrenia with self-harm. Multivariate analyses and MR analyses strengthened the evidence implicating MDD, ADHD and schizophrenia as plausible causal risk factors for self-harm.Implications of all the available evidenceAmong the 24 risk factors considered, plausible causal risk factors for self-harm were identified among psychiatric conditions. Using PRS and MR methods and a number of complementary analyses provided higher confidence to infer causality and nuanced insights into the aetiology of self-harm. From a clinical perspective, detection and treatment of core symptoms of these conditions, such as psychotic or impulsivity symptoms, may prevent individuals from self-harming.

IntroductionIt’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression.MethodsPolygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank.ResultsAll cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression.ConclusionsThe phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

Background & Aims Hepatitis B virus (HBV) integration is common in HBV-associated hepatocellular carcinoma (HCC) and may play an important pathogenic role through the production of chimeric HBV-human transcripts. We aimed to screen the transcriptome for HBV integrations in HCCs. Methods Transcriptome sequencing was performed on paired HBV-associated HCCs and corresponding non-tumorous liver tissues to identify viral-human chimeric sites. Validation was further performed in an expanded cohort of human HCCs. Results Here we report the discovery of a novel pre-mRNA splicing mechanism in generating HBV-human chimeric protein. This mechanism was exemplified by the formation of a recurrent HBV-cyclin A2 (CCNA2) chimeric transcript (A2S), as detected in 12.5% (6 of 48) of HCC patients, but in none of the 22 non-HCC HBV-associated cirrhotic liver samples examined. Upon the integration of HBV into the intron of the CCNA2 gene, the mammalian splicing machinery utilized the foreign splice sites at 282nt. and 458nt. of the HBV genome to generate a pseudo-exon, forming an in-frame chimeric fusion with CCNA2 . The A2S chimeric protein gained a non-degradable property and promoted cell cycle progression, demonstrating its potential oncogenic functions. Conclusions A pre-mRNA splicing mechanism is involved in the formation of HBV-human chimeric proteins. This represents a novel and possibly common mechanism underlying the formation of HBV-human chimeric transcripts from intronically integrated HBV genome with functional impact. Lay summary HBV is involved in the mammalian pre-mRNA splicing machinery in the generation of potential tumorigenic HBV-human chimeras. This study also provided insight on the impact of intronic HBV integration with the gain of splice sites in the development of HBV-associated HCC.

Following publication of this article, the authors noticed an error in Supplementary Table 1. In the original Supplementary Table 1, one of the criteria for control participants was incorrectly given as ‘Report extensive recent symptoms of depression: less than 14 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’. This has now been corrected to: ‘Report extensive recent symptoms of depression: less than 5 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’.

BackgroundThe prevalence of depression is higher in individuals suffering from autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Epidemiological findings point to a bi-directional relationship - that depression increases the risk of developing an autoimmune disease, and vice-versa. Shared genetic etiology is a plausible explanation for the overlap between depression and autoimmune diseases. In this study we tested whether genetic variation in the Major Histocompatibility Complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.MethodWe fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 Human Leukocyte Antigen (HLA) alleles and four Complement Component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium (PGC) Major Depressive Disorder (MDD) working group and the UK Biobank (UKB). In the 26 PGC-MDD studies, cases met a lifetime diagnosis of MDD, determined by a structured diagnostic interview. In the UKB, cases and controls were identified from an online mental health questionnaire. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants in each study and performed an inverse-variance weighted meta-analysis across the PGC-MDD and UKB samples, applying both a conservative region-wide significance threshold (3.9-e6) and a candidate threshold (1.6e-4).ResultsNo HLA alleles or C4 haplotypes were associated with depression at the conservative threshold in the PGC, UKB or meta-analysis. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold in the meta-analysis. Under the conservative threshold, 70 SNPs were detected in the UKB and 143 SNPs were detected in the meta-analysis, mirroring previous findings from highly powered GWAS of depression.DiscussionWe found no evidence that HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia, confer risk for depression. These results indicate that autoimmune diseases and depression do not share common risk loci of moderate or large effect in the MHC.

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD between individuals reporting and not reporting trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD was greater in participants reporting trauma exposure (24%) than in individuals not reporting trauma exposure (12%), taking into account the strong, positive genetic correlation observed between MDD and reported trauma exposure. The genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8×10-7 versus rg = −0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3×10-4). Our results suggest that the genetic contribution to MDD is greater when additional risk factors are present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

In this paper, we report a novel heterozygous mutation of A285V codon conversion on exon 4 of the desmin (DES), using whole exome sequencing (WES) in an isolated proband with documented dilated cardiomyopathy (DCM). This mutation is predicted to cause three-dimensional structure changes of DES. Immunohistological and electron microscopy studies demonstrated diffuse abnormal DES aggregations in DCM-induced-pluripotent stem cell (iPSC)-derived cardiomyocytes, and control-iPSC-derived cardiomyocytes transduced with A285V-DES. DCM-iPSC-derived cardiomyocytes also exhibited functional abnormalities in vitro. This is the first demonstration that patient-specific iPSC-derived cardiomyocytes can be used to provide histological and functional confirmation of a suspected genetic basis for DCM identified by WES.