Muscle mass is central to physical function and metabolic health 1 , but can decrease with disease, aging 2 and weight loss interventions 3-5 . As such interventions become more widely used, agents that preserve or increase muscle mass are needed. To identify such therapeutic opportunities, we performed genome-wide association meta-analyses (GWAS) of arm, leg, trunk and total lean mass measured by dual-energy X-ray absorptiometry (DXA), a widely used method to estimate muscle mass. We identified 63 loci, including the rare missense variant in MSTN (p.Ile225Thr, rs143242500), which had the largest effect on leg lean mass (β = 0.28 SD [95% CI: 0.19, 0.37], P = 1.9 × 10 -9 ). MSTN encodes myostatin, a negative regulator of skeletal muscle mass and a therapeutic target for muscle wasting disorders 6 . p.Ile225Thr is the first genome-wide significant association in MSTN in humans with functional consequences and could provide further insight into long-term systemic effects of myostatin inhibition.

The causal effect of lower plasma sclerostin on cardiovascular disease (CVD) risk has previously been examined with the aim of investigating potential side effects of pharmacological sclerostin inhibition for treatment of osteoporosis. We explored the relationship between plasma sclerostin levels and CVDs and bone phenotypes using Mendelian randomization (MR) and correlation between plasma sclerostin levels and these outcomes. We used variants identified in genome-wide association studies of plasma sclerostin levels in large proteomic datasets from the UK Biobank (Olink) and Iceland (SomaScan) as instruments in two separate MR analyses. These analyses did not provide evidence of association between the effects of sequence variants on plasma sclerostin levels and their effects on CVDs and CVD risk factors (P > 0.05). Several of the instruments had heterogenic effects on bone phenotypes and causal estimates in MR were non-significant (P > 0.05/8). Plasma sclerostin levels correlated positively with coronary artery disease, myocardial infarction and CVD risk factors. Our results do not provide evidence supporting the hypothesis that lower plasma sclerostin levels increase CVD risk and suggest that plasma sclerostin levels are not a good surrogate for pharmacological inhibition.

Although information from real-world data can be used to identify factors that aid treatment choice, there are no guidelines for the use of such data. The aim of this Review is to summarise and evaluate definitions of treatment outcomes for antidepressants, antipsychotics, and mood stabilisers when using real-world data, and to suggest standards for the field. Given that no standards for the use of these data in estimating treatment outcomes exist, variability is high for treatment outcome definitions. We make recommendations for different scenarios of available data and highlight the importance of using other sources of information to validate proxy measures such as continued treatment, switching between medications, or polypharmacy of psychotropic medications. Well defined and validated treatment outcome measures that incorporate real-world data could facilitate the development of precision psychiatry approaches and support regulatory decision making regarding psychopharmacological agents.

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 one novel locus (rs1106260) associated with non-response to selective serotonin reuptake inhibitors (SSRIs), and one novel locus (rs60847828) associated with non-response to SSRIs and serotonin-norepinephrine reuptake inhibitors (SNRIs) 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.

There are extensive real-world healthcare data with treatment outcomes that can be applied in data-driven research to improve pharmacotherapy in psychiatry. Given that no standards for estimating treatment outcomes from health registries, hospital records, and self-reports exist, there is high variability, and findings are difficult to compare or meta-analyze. With the growing interest in AI-tools, there is a need for large training and test data from health care with better quality. Standardization of treatment outcome definitions is important for developing prediction and stratification tools for future precision psychiatry.Within a group of experts across several EU and Wellcome Trust projects, as well as ECNP, we have made recommendations for how to leverage real-world data to measure pharmacological treatment outcomes in major psychiatric disorders. Proxy measures of treatment outcomes can be improved by integrating real-world data from various registries and healthcare systems that can be linked to self-reports on medication response and adverse effects. We have made recommendations for different scenarios and available data and highlight the importance of validating proxy phenotypes using other sources of information (Koch et al. 2025, Lancet Psychiatry).We apply these recommendations in large-scale genome-wide association studies (GWAS) on antidepressant treatment outcomes utilizing data from prescription registries and other health registries across nine countries. In all cohorts, non-response to selective serotonin reuptake inhibitors (SSRIs) was defined as switching from an SSRI to another antidepressant between 6-14 weeks or augmentation or combination with another antidepressant or antipsychotic, while response to SSRIs was defined as treatment for ≥6 month with the same SSRI without switching or augmentation. Treatment-resistant depression was defined as ≥2 switches between antidepressants between 6-14 weeks, and the control group was defined by history of treatment with the same antidepressant for ≥6 month and less than 2 switches. Using these phenotypes, we performed GWAS including a total of ∼400,000 individuals. Questionnaire data on self-reported antidepressant response were used to validate the proxy phenotypes and GWAS findings.Leveraging real-world data for well-defined and validated treatment outcome measures can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants, with the potential to form the basis for precision psychiatry approaches.

Bipolar disorder is a highly heritable psychiatric disorder; genome-wide association studies of bipolar disorder have yielded over 60 risk loci harboring common variants. To harness the information contained in rare loss-of-function (LOF) variants, holding promise for informing on the underlying biology, we performed a variant burden analysis for bipolar disorder using gene-based aggregation of LOF variants in whole-genome sequencing data from Iceland (4,197 cases, more than 200,000 controls) and the UK Biobank (1,881 cases, 426,622 controls). We found that HECTD2 was associated with bipolar disorder and confirmed it using the Bipolar Exome dataset. Meta-analysis with Bipolar Exome also revealed that LOF variants in AKAP11 were associated with bipolar disorder. Both associations with bipolar disorder are new, but AKAP11 has previously been associated with psychosis and schizophrenia. The products of AKAP11 and HECTD2 interact with GSK3β, a protein inhibited by lithium, the most effective mood stabilizer available to treat bipolar disorder. Analysis of whole-genome sequencing data from Iceland and the UK Biobank identifies an excess burden of rare loss-of-function variants in HECTD2 and AKAP11 in individuals diagnosed with bipolar disorder.

Background:Zopiclone and zolpidem are widely prescribed hypnotic medications for insomnia, sharing similar efficacy but differing in side-effect profiles, particularly concerning taste disturbances. Identifying genetic predictors of intolerance to these medications could inform personalized treatment strategies. Methods:We conducted a genome-wide association study to identify genetic variants associated with switching between zopiclone and zolpidem in 57,669 Icelanders, using electronic prescription data from Iceland (2003-2020), and 6590 British individuals from the UK Biobank (1990-2017). We included individuals who had received at least 3 prescriptions of either drug. We also investigated data on bitter taste perception using quinine taste test data from 2238 Icelandic individuals. Results:A common sequence variant, rs6488335-G, within the TAS2R ∗ bitter taste receptor gene locus on chromosome 12, was associated with an increased likelihood of switching from zopiclone to zolpidem (Iceland: odds ratio [OR], 1.29; 95% CI, 1.24 to 1.35; United Kingdom: OR, 1.34; 95% CI, 1.12 to 1.59) and a decreased likelihood of switching in the reverse direction. The effect was more pronounced in women (ORfemales, 1.36; 95% CI, 1.29 to 1.44) than in men (ORmales, 1.19; 95% CI, 1.11 to 1.27). While the variant is associated with bitter taste perception of quinine, conditional analyses suggest that the pharmacogenetic association with drug switching is independent of taste perception. Conclusions:Our findings indicate that carriers of the rs6488335-G variant, particularly homozygous women, were more likely to switch from zopiclone to zolpidem, potentially due to heightened sensitivity to taste-related side effects. Preemptive genetic testing could guide clinicians in prescribing zolpidem over zopiclone for individuals at risk, thereby reducing health care visits and improving treatment adherence.

Obesity is associated with adverse effects on health and quality of life. Improved understanding of its underlying pathophysiology is essential for developing counteractive measures. To search for sequence variants with large effects on BMI, we perform a multi-ancestry meta-analysis of 13 genome-wide association studies on BMI, including data derived from 1,534,555 individuals of European ancestry, 339,657 of Asian ancestry, and 130,968 of African ancestry. We identify an intergenic 262,760 base pair deletion at the MC4R locus that associates with 4.11 kg/m2 higher BMI per allele, likely through downregulation of MC4R. Moreover, a rare FRS3 missense variant, p.Glu115Lys, only found in individuals from Finland, associates with 1.09 kg/m2 lower BMI per allele. We also detect three other low-frequency FRS3 missense variants that associate with BMI with smaller effects and are enriched in different ancestries. We characterize FRS3 as a BMI-associated gene, encoding an adaptor protein known to act downstream of BDNF and TrkB, which regulate appetite, food intake, and energy expenditure through unknown signaling pathways. The work presented here contributes to the biological foundation of obesity by providing a convincing downstream component of the BDNF-TrkB pathway, which could potentially be targeted for obesity treatment.

Background: Signal transducer and activator of transcription 6 (STAT6) is central to type 2 (T2) inflammation, and common noncoding variants at the STAT6 locus associate with various T2 inflammatory traits, including diseases, and its pathway is widely targeted in asthma treatment. Objective: We sought to test the association of a rare missense variant in STAT6, p.L406P, with T2 inflammatory traits, including the risk of asthma and allergic diseases, and to characterize its functional consequences in cell culture. Methods: The association of p.L406P with plasma protein levels, white blood cell counts, and the risk of asthma and allergic phenotypes was tested. Significant associations in other cohorts were also tested using a burden test. The effects of p.L406P on STAT6 protein function were examined in cell lines and by comparing CD4+ T-cell responses from carriers and noncarriers of the variant. Results: p.L406P associated with reduced plasma levels of STAT6 and IgE as well as with lower eosinophil and basophil counts in blood. It also protected against asthma, mostly driven by severe T2-high asthma. p.L406P led to lower IL-4-induced activation in luciferase reporter assays and lower levels of STAT6 in CD4+ T cells. We identified multiple genes with expression that was affected by the p.L406P genotype on IL-4 treatment of CD4+ T cells; the effect was consistent with a weaker IL-4 response in carriers than in noncarriers of p.L406P. Conclusions: A partial loss-of-function variant in STAT6 resulted in dampened IL-4 responses and protection from T2- high asthma, implicating STAT6 as an attractive therapeutic target.

Genomic prediction of antipsychotic dose and polypharmacy has been difficult, mainly due to limited access to large cohorts with genetic and drug prescription data. In this proof of principle study, we investigated if genetic liability for schizophrenia is associated with high dose requirements of antipsychotics and antipsychotic polypharmacy, using real-world registry and biobank data from five independent Nordic cohorts of a total of N = 21,572 individuals with psychotic disorders (schizophrenia, bipolar disorder, and other psychosis). Within regression models, a polygenic risk score (PRS) for schizophrenia was studied in relation to standardized antipsychotic dose as well as antipsychotic polypharmacy, defined based on longitudinal prescription registry data as well as health records and self-reported data. Meta-analyses across the five cohorts showed that PRS for schizophrenia was significantly positively associated with prescribed (standardized) antipsychotic dose (beta(SE) = 0.0435(0.009), p = 0.0006) and antipsychotic polypharmacy defined as taking ≥2 antipsychotics (OR = 1.10, CI = 1.05–1.21, p = 0.0073). The direction of effect was similar in all five independent cohorts. These findings indicate that genotypes may aid clinically relevant decisions on individual patients´ antipsychotic treatment. Further, the findings illustrate how real-world data have the potential to generate results needed for future precision medicine approaches in psychiatry.

BACKGROUND:Signal transducer and activator of transcription 6 (STAT6) is central to type 2 (T2) inflammation, and common noncoding variants at the STAT6 locus associate with various T2 inflammatory traits, including diseases, and its pathway is widely targeted in asthma treatment. OBJECTIVE:We sought to test the association of a rare missense variant in STAT6, p.L406P, with T2 inflammatory traits, including the risk of asthma and allergic diseases, and to characterize its functional consequences in cell culture. METHODS:The association of p.L406P with plasma protein levels, white blood cell counts, and the risk of asthma and allergic phenotypes was tested. Significant associations in other cohorts were also tested using a burden test. The effects of p.L406P on STAT6 protein function were examined in cell lines and by comparing CD4+ T-cell responses from carriers and noncarriers of the variant. RESULTS:p.L406P associated with reduced plasma levels of STAT6 and IgE as well as with lower eosinophil and basophil counts in blood. It also protected against asthma, mostly driven by severe T2-high asthma. p.L406P led to lower IL-4-induced activation in luciferase reporter assays and lower levels of STAT6 in CD4+ T cells. We identified multiple genes with expression that was affected by the p.L406P genotype on IL-4 treatment of CD4+ T cells; the effect was consistent with a weaker IL-4 response in carriers than in noncarriers of p.L406P. CONCLUSIONS:A partial loss-of-function variant in STAT6 resulted in dampened IL-4 responses and protection from T2-high asthma, implicating STAT6 as an attractive therapeutic target.

Mendelian Randomization studies indicate that BMI contributes to various diseases, but it's unclear if this is entirely mediated by BMI itself. This study examines whether disease risk from BMI-associated sequence variants is mediated through BMI or other mechanisms, using data from Iceland and the UK Biobank. The associations of BMI genetic risk score with diseases like fatty liver disease, knee replacement, and glucose intolerance were fully attenuated when conditioned on BMI, and largely for type 2 diabetes, heart failure, myocardial infarction, atrial fibrillation, and hip replacement. Similar attenuation was observed for chronic kidney disease and stroke, though results varied. Findings were consistent across sexes, except for myocardial infarction. Residual effects may result from temporal BMI changes, pleiotropy, measurement error, non-linear relationships, non-collapsibility, or confounding. The attenuation extent of BMI genetic risk score on disease associations suggests the potential impact of reducing BMI on disease risk. BMI contributes to various diseases, but it was unclear whether the risk was mediated by BMI itself. Here, the authors demonstrate that BMI mediates most of this risk, and suggest an upper bound on disease risk that can be mitigated by lowering BMI.

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.

Clonal hematopoiesis (CH) arises when a substantial proportion of mature blood cells is derived from a single hematopoietic stem cell lineage. Using whole-genome sequencing of 45,510 Icelandic and 130,709 UK Biobank participants combined with a mutational barcode method, we identified 16,306 people with CH. Prevalence approaches 50% in elderly participants. Smoking demonstrates a dosage-dependent impact on risk of CH. CH associates with several smoking-related diseases. Contrary to published claims, we find no evidence that CH is associated with cardiovascular disease. We provide evidence that CH is driven by genes that are commonly mutated in myeloid neoplasia and implicate several new driver genes. The presence and nature of a driver mutation alters the risk profile for hematological disorders. Nevertheless, most CH cases have no known driver mutations. A CH genome-wide association study identified 25 loci, including 19 not implicated previously in CH. Splicing, protein and expression quantitative trait loci were identified for CD164 and TCL1A .

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.

Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.

Migraine is a complex neurovascular disease with a range of severity and symptoms, yet mostly studied as one phenotype in genome-wide association studies (GWAS). Here we combine large GWAS datasets from six European populations to study the main migraine subtypes, migraine with aura (MA) and migraine without aura (MO). We identified four new MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classified 13 MO-associated variants. Rare variants with large effects highlight three genes. A rare frameshift variant in brain-expressed PRRT2 confers large risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function variants in SCN11A, encoding a neuron-expressed sodium channel with a key role in pain sensation, shows strong protection against migraine. Finally, a rare variant with cis-regulatory effects on KCNK5 confers large protection against migraine and brain aneurysms. Our findings offer new insights with therapeutic potential into the complex biology of migraine and its subtypes.