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Weight gain and metabolic complications are major adverse effects of many psychotropic drugs. We aimed to understand how socio-economic status (SES), defined as the Swiss socio-economic position (SSEP), is associated with cardiometabolic parameters after initiation of psychotropic medications known to induce weight gain. Cardiometabolic parameters were collected in two Swiss cohorts following the prescription of psychotropic medications. The SSEP integrated neighborhood-based income, education, occupation, and housing condition. The results were then validated in an independent replication sample (UKBiobank), using educational attainment (EA) as a proxy for SES. Adult patients with a low SSEP had a higher risk of developing metabolic syndrome over one year versus patients with a high SSEP (Hazard ratio (95% CI) = 3.1 (1.5–6.5), n = 366). During the first 6 months of follow-up, a significant negative association between SSEP and body mass index (BMI), weight change, and waist circumference change was observed (25 ≤ age < 65, n = 526), which was particularly important in adults receiving medications with the highest risk of weight gain, with a BMI difference of 0.86 kg/m2 between patients with low versus high SSEP (95% CI: 0.03–1.70, n = 99). Eventually, a causal effect of EA on BMI was revealed using Mendelian randomization in the UKBiobank, which was notably strong in high-risk medication users (beta: −0.47 SD EA per 1 SD BMI; 95% CI: −0.46 to −0.27, n = 11,314). An additional aspect of personalized medicine was highlighted, suggesting the patients’ SES represents a significant risk factor. Particular attention should be paid to patients with low SES when initiating high cardiometabolic risk psychotropic medications.

Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity—partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity. Partners are often similar in traits, such as their education and political views, but it is unclear what exactly causes this similarity. Using Mendelian randomization and data on 50,000 partner pairs, the authors show that similarity in different traits can be explained by partner choice, confounding factors and convergence over time.

Epidemiological studies have reported conflicting findings on the potential adverse effects of long-term antihypertensive medication use on cancer risk. Naturally occurring variation in genes encoding antihypertensive drug targets can be used as proxies for these targets to examine the effect of their long-term therapeutic inhibition on disease outcomes. We performed a mendelian randomization analysis to examine the association between genetically proxied inhibition of 3 antihypertensive drug targets and risk of 4 common cancers (breast, colorectal, lung, and prostate). Single-nucleotide polymorphisms (SNPs) in ACE, ADRB1, and SLC12A3 associated (P < 5.0 × 10-8) with systolic blood pressure (SBP) in genome-wide association studies (GWAS) were used to proxy inhibition of angiotensin-converting enzyme (ACE), β-1 adrenergic receptor (ADRB1), and sodium-chloride symporter (NCC), respectively. Summary genetic association estimates for these SNPs were obtained from GWAS consortia for the following cancers: breast (122,977 cases, 105,974 controls), colorectal (58,221 cases, 67,694 controls), lung (29,266 cases, 56,450 controls), and prostate (79,148 cases, 61,106 controls). Replication analyses were performed in the FinnGen consortium (1,573 colorectal cancer cases, 120,006 controls). Cancer GWAS and FinnGen consortia data were restricted to individuals of European ancestry. Inverse-variance weighted random-effects models were used to examine associations between genetically proxied inhibition of these drug targets and risk of cancer. Multivariable mendelian randomization and colocalization analyses were employed to examine robustness of findings to violations of mendelian randomization assumptions. Genetically proxied ACE inhibition equivalent to a 1-mm Hg reduction in SBP was associated with increased odds of colorectal cancer (odds ratio (OR) 1.13, 95% CI 1.06 to 1.22; P = 3.6 × 10-4). This finding was replicated in the FinnGen consortium (OR 1.40, 95% CI 1.02 to 1.92; P = 0.035). There was little evidence of association of genetically proxied ACE inhibition with risk of breast cancer (OR 0.98, 95% CI 0.94 to 1.02, P = 0.35), lung cancer (OR 1.01, 95% CI 0.92 to 1.10; P = 0.93), or prostate cancer (OR 1.06, 95% CI 0.99 to 1.13; P = 0.08). Genetically proxied inhibition of ADRB1 and NCC were not associated with risk of these cancers. The primary limitations of this analysis include the modest statistical power for analyses of drug targets in relation to some less common histological subtypes of cancers examined and the restriction of the majority of analyses to participants of European ancestry. In this study, we observed that genetically proxied long-term ACE inhibition was associated with an increased risk of colorectal cancer, warranting comprehensive evaluation of the safety profiles of ACE inhibitors in clinical trials with adequate follow-up. There was little evidence to support associations across other drug target-cancer risk analyses, consistent with findings from short-term randomized controlled trials for these medications.

Patients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance ( p  < 5 × 10 −8 ): rs7736552 (near MAN2A1 ), rs11074029 (in SLCO3A1 ), rs117496040 (near DEFB1 ) and rs7647863 (in IQSEC1 ). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope ( p  = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.

Identifying markers of chronic kidney disease (CKD) that occur early in the disease process and are specific to loss of kidney function rather than other underlying causes of disease may allow earlier, more accurate identification of patients who will develop CKD. We therefore sought to identify diagnostic blood markers of early CKD that are caused by loss of kidney function by using an innovative \"reverse Mendelian randomization\" (MR) approach. We applied this technique to genetic and biomarker data from 4147 participants in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial, all with known type 2 diabetes, impaired fasting glucose, or impaired glucose tolerance. Two-sample MR was conducted using variants associated with creatinine-based eGFR (eGFR ) from the CKDGen Consortium (n = 133814) to estimate the effect of genetically decreased eGFR on 238 serum biomarkers. With reverse MR, trefoil factor 3 (TFF3) was identified as a protein that is increased owing to decreased eGFR (β = 1.86 SD per SD decrease eGFR ; 95% CI, 0.95-2.76; = 8.0 × 10 ). Reverse MR findings were consistent with epidemiological associations for incident CKD in ORIGIN (OR = 1.28 per SD increase in TFF3; 95% CI, 1.18-1.38; = 4.58 × 10 ). Addition of TFF3 significantly improved discrimination for incident CKD relative to eGFR alone (net reclassification improvement = 0.211; = 9.56 × 10 ) and in models including additional risk factors. Our results suggest TFF3 is a valuable diagnostic marker for early CKD in dysglycemic populations and acts as a proof of concept for the application of this novel MR technique to identify diagnostic biomarkers for other chronic diseases. NCT00069784.

Increased phenotypic similarity between partners, termed assortative mating (AM), has been observed for many traits. However, it is currently unclear if these observations are due to mate choice for certain phenotypes, post-mating convergence, or a result of confounding factors such as shared environment or indirect assortment. To dissect these underlying phenomena, we applied Mendelian randomisation (MR) to 51,664 couples in the UK biobank to a panel of 118 phenotypes under AM. We found that 54% (64 of 118) of the tested traits had a causal relationship between partners, with female-to-male effects on average being larger. Forty traits, including systolic blood pressure, basal metabolic rate, weight and height, showed significantly larger phenotypic correlation than MR-estimates, suggesting the presence of confounders. Subsequent analyses revealed household income, overall health rating, education and tobacco smoking as major overall confounders, accounting for 29.8, 14.1, 11.6, and 4.78%, of cross-partner phenotypic correlations, respectively. We detected limited evidence for couple-correlation convergence (e.g. increased similarity with respect to smoking and medication use), measured by stratifying couples by their time spent together. Finally, mediation analysis revealed that the vast majority (>77%) of causal associations between one trait of an individual and a different trait of their partner is indirect. For example, the causal effect of the BMI of an individual on the overall health rating of their partner is entirely acting through the BMI of their partner. In summary, this study revealed many novel causal effects within couples, shedding light on the impact of confounding on couple phenotypic similarity. Competing Interest Statement The authors have declared no competing interest.

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels. Competing Interest Statement The authors have declared no competing interest.

Tandem DNA repeats vary in the size and sequence of each unit (motif). When expanded, these tandem DNA repeats have been associated with more than 40 monogenic disorders 1 . Their involvement in disorders with complex genetics is largely unknown, as is the extent of their heterogeneity. Here we investigated the genome-wide characteristics of tandem repeats that had motifs with a length of 2–20 base pairs in 17,231 genomes of families containing individuals with autism spectrum disorder (ASD) 2 , 3 and population control individuals 4 . We found extensive polymorphism in the size and sequence of motifs. Many of the tandem repeat loci that we detected correlated with cytogenetic fragile sites. At 2,588 loci, gene-associated expansions of tandem repeats that were rare among population control individuals were significantly more prevalent among individuals with ASD than their siblings without ASD, particularly in exons and near splice junctions, and in genes related to the development of the nervous system and cardiovascular system or muscle. Rare tandem repeat expansions had a prevalence of 23.3% in children with ASD compared with 20.7% in children without ASD, which suggests that tandem repeat expansions make a collective contribution to the risk of ASD of 2.6%. These rare tandem repeat expansions included previously undescribed ASD-linked expansions in DMPK and FXN , which are associated with neuromuscular conditions, and in previously unknown loci such as FGF14 and CACNB1 . Rare tandem repeat expansions were associated with lower IQ and adaptive ability. Our results show that tandem DNA repeat expansions contribute strongly to the genetic aetiology and phenotypic complexity of ASD. Genome-wide analysis of tandem DNA repeats in the genomes of individuals with autism spectrum disorder and control participants reveals a strong contribution of tandem repeat expansions to the genetic aetiology and phenotypic complexity of autism spectrum disorder.

Biomarker assay measurement often consists of a two-stage process where laboratory equipment yields a relative measure which is subsequently transformed to the unit of interest using a calibration curve. The calibration curve establishes the relation between the measured relative units and sample biomarker concentrations using stepped samples of known biomarker concentrations. Samples from epidemiologic studies are often measured in multiple batches or plates, each with independent calibration experiments. Collapsing calibration information across batches before statistical analysis has been shown to reduce measurement error and improves estimation. Additionally, collapsing in practice can also create an additional layer of quality control (QC) and optimization in a part of the laboratory measurement process that is often highly automated. Principled recalibration is demonstrated via. a three-step process of identifying batches where recalibration might be beneficial, forming a collapsed calibration curve and recalibrating identified batches, and using QC data to assess the appropriateness of recalibration. Here, we use inhibin B measured in biospecimens from the BioCycle study using 50 enzyme-linked immunosorbent assay (ELISA) batches (3875 samples) to motivate and display the benefits of collapsing calibration experiments, such as detecting and overcoming faulty calibration experiments, and thus improving assay coefficients of variation from reducing unwanted measurement error variability. Differences in the analysis of inhibin B by testosterone quartile are also demonstrated before and after recalibration. These simple and practical procedures are minor adjustments implemented by study personnel without altering laboratory protocols which could have positive estimation and cost-saving implications especially for population-based studies.