Biology subjects

23andMe Research Team,

Publications and source records attributed to 23andMe Research Team,.

At least 19 recordsLinked to original sources

bioRxiv2026

Does bilingualism buffer genetic predispositions to reading difficulties through alterations of structural interhemispheric connectivity? An ABCD Study.

Here, we test the hypothesis that early sustained exposure to complex bilingual environments can positively affect reading development by altering structural interhemispheric connectivity via the corpus callosum (CC). Interhemispheric connectivity has been shown to be inefficient in dyslexia, but also to support compensatory pathways when genetic risk for reading difficulties is present, by enabling the preserved right hemisphere to support a dysfunctional left hemisphere. Mediation models were conducted on children aged 9-10 years (with a 2-year follow-up assessment) from the Adolescent Brain Cognitive Development database (N>10,000). Polygenic scores (PGS) for dyslexia and cognitive performance and continuous bilingualism indices were used as predictors, with reading aloud as the outcome. Bilingualism showed a positive effect on reading partially mediated by the anterior CC, independently of overall brain size. In contrast, genetic predispositions to reading difficulties influenced reading primarily through overall brain size rather than CC connectivity specifically. These two pathways were independent, suggesting that bilingual experience and genetic risk operate through distinct neuroanatomical mechanisms. These findings suggest that recurrent early exposure to complex bilingual environments may shape the brains structural connectivity toward a more balanced and integrated bilateral frontal organisation. The results highlight potential brain compensatory pathways induced by environmental experiences that may support more efficient reading development and mitigate risks for developmental dyslexia.

neuroscience↗

bioRxiv2026

Population-wide variation in connectopic organization of cerebral language hubs

The uniquely human capacity for language is supported by a distributed brain network with cerebral cortical hubs in the left inferior frontal gyrus (LIFG) and left superior temporal sulcus (LSTS). Individual differences in the graded spatial organization of function across these regions may provide sensitive phenotypes for behavioural and genetic association analyses, to better understand the biological underpinnings of language. In 41,437 UK Biobank participants we applied connectopic mapping to the LIFG and LSTS. This approach maps spatial changes across a region in terms of connectivity with the rest of the brain, based on resting state functional imaging. On average, we found that Brodmann Area (BA) 44, BA 45 and the posterior LSTS were strongly connected to the superior and inferior parietal lobe and the dorsal attention and frontoparietal networks, whereas BA 47 and the medial LSTS were strongly connected to superior frontal areas BA 8B and BA 9, and the default mode network more broadly. However, there was extensive population-wide variation in these graded connectopic patterns, which significantly mediated the effects of polygenic scores for reading ability and educational attainment on performance on a vocabulary task. Furthermore we identified 3 genomic loci displaying multivariate association patterns with these connectopic maps, one of which coincides with a long non-coding RNA, LINC01165, with potential relevance in human evolution. Overall, connectopic mapping produced fewer significant genetic association findings and lower heritability (range from 0.3 to 9.4%) than previous parcel-based approaches for mapping language network connectivity. These observations suggest higher genetic complexity and/or prominent roles for environmental or random developmental effects on these connectopic maps.

neuroscience↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2024

Credible set is sensitive to imputation quality and missing variants

Bayesian fine-mapping to obtain credible sets has been widely applied post GWAS to pinpoint causal variants. The calculation of credible sets generally assumes that all variants have been equally well genotyped, which is often not the case when a GWAS has been run on imputed data. In this work, we investigate the behavior of credible sets in imputed datasets utilizing held out genotyped variants to measure accuracy. We show, via simulation, that: i) the coverage of credible sets decreases when using imputed variants in GWAS; ii) rare causal variants often fail to be tagged in credible sets when they are not present in the GWAS variant set. We develop a reweighting approach to take imputation quality into account during fine-mapping that only requires summary statistics, and demonstrate the approach with real data.

genetics↗

bioRxiv2024

Analysis of 3.6 million individuals yields minimal evidence of pairwise genetic interactions for height

Adult height is a highly heritable polygenic trait with heritability attributable to thousands of independent variants. Large-scale studies have been able to detect genetic variants with contributions to height in the range of approximately 1.2 millimetre per allele copy on average. Non-additive genetic interactions may, in part, account for the difference between broad-sense and narrow-sense heritability estimates. However, prior studies have failed to identify variants with non-additive effects, possibly due to the lack of statistical power. Leveraging 3.6M individuals of European genetic ancestry in the 23andMe research cohort, we performed a genome-wide analysis study (GWAS) to select 1,063 independent common SNPs associated with height (p-value < 5e-8), and then screened for evidence of non-additive effects by analysing 564,453 models including a pairwise SNP-SNP interaction term. We identified 69 pairwise models with suggestive evidence of SNP-SNP interaction (p-value < 1e-4) and, for each SNP pair, we evaluated a fully saturated model including additive, dominant, and epistatic (additive-by-additive, additive-by-dominance and dominance-by-dominance) terms. We tested for the presence of epistatic interactions by comparing models with and without epistatic terms using a likelihood ratio test. Assuming a strict Bonferroni-corrected threshold of 8.9e-8 (0.05/564,453), we found no evidence of epistatic interactions (Likelihood ratio test (LRT) p-value < 9e-07 for all models). Our analysis rules out the existence of epistatic interactions between alleles of >1% frequency with effect sizes larger than 2.42mm. Our large-scale analysis provides further evidence of the minimal contribution of non-additivity in the genetic architecture of adult human height.

genomics↗

bioRxiv2024

Phasing millions of samples achieves near perfect accuracy, enabling parent-of-origin classification of variants

Haplotype phasing, the process of determining which genetic variants are physically located on the same chromosome, is crucial for various genetic analyses. In this study, we first benchmark SHAPEIT and Beagle, two state-of-the-art phasing methods, on two large datasets: > 8 million diverse, research-consented 23andMe, Inc. customers and the UK Biobank (UKB). We find that both perform exceptionally well. Beagles median switch error rate (SER) (after excluding single SNP switches) in white British trios from UKB is 0.026% compared to 0.00% for European ancestry 23andMe research participants; 55.6% of European ancestry 23andMe research participants have zero non-single SNP switches, compared to 42.4% of white British trios. South Asian ancestry 23andMe research participants have the highest median SER amongst the 23andMe populations, but it is still remarkably low at 0.46%. We also investigate the relationship between identity-by-descent (IBD) and SER, finding that switch errors tend to occur in regions of little or no IBD segment coverage. SHAPEIT and Beagle excel at intra-chromosomal phasing, but lack the ability to phase across chromosomes, motivating us to develop an inter-chromosomal phasing method, called HAPTIC (HAPlotype TIling and Clustering), that assigns paternal and maternal variants discretely genome-wide. Our approach uses identity-by-descent (IBD) segments to phase blocks of variants on different chromosomes. HAPTIC represents the segments a focal individual shares with their relatives as nodes in a signed graph and performs bipartite clustering on the signed graph using spectral clustering. We test HAPTIC on 1022 UKB trios, yielding a median phase error of 0.08% in regions covered by IBD segments (33.5% of sites). We also ran HAPTIC in the 23andMe database and found a median phase error rate (the rate of mismatching alleles between the inferred and true phase) of 0.92% in Europeans (93.8% of sites) and 0.09% in admixed Africans (92.7% of sites). HAPTICs precision depends heavily on data from relatives, so will increase as datasets grow larger and more diverse. HAPTIC enables analyses that require the parent-of-origin of variants, such as association studies and ancestry inference of untyped parents.

bioinformatics↗

bioRxiv2023

The shared genetic architecture and evolution of human language and musical rhythm

Rhythm and language-related traits are phenotypically correlated, but their genetic overlap is largely unknown. Here, we leveraged two large-scale genome-wide association studies performed to shed light on the shared genetics of rhythm (N=606,825) and dyslexia (N=1,138,870). Our results reveal an intricate shared genetic and neurobiological architecture, and lay groundwork for resolving longstanding debates about the potential co-evolution of human language and musical traits.

genomics↗

bioRxiv2023

ME-Bayes SL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.

genetics↗

bioRxiv2023

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of [L]1 (lasso) and [L]2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

genetics↗

bioRxiv2022

Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 3.7 Million Individuals of Diverse Ancestry

Polygenic risk scores (PRS) increasingly predict complex traits, however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRS using ancestry-specific GWAS summary statistics from multi-ancestry training samples, integrating clumping and thresholding, empirical Bayes and super learning. We evaluate CT-SLEB and nine-alternatives methods with large-scale simulated GWAS ([~]19 million common variants) and datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across thirteen complex traits. Results demonstrate that CT-SLEB significantly improves PRS performance in non-European populations compared to simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offer insights into sample size requirements and SNP density effects on multi-ancestry risk prediction.

genetics↗

bioRxiv2022

A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

genetics↗

bioRxiv2020

Shared and distinct genetic influences between cognitive domains and psychiatric disorder risk based on genome-wide data

Group-level cognitive performance differences are found in psychiatric disorders ranging from depression to autism to schizophrenia. To investigate the genetics of individual differences in fluid and crystallized cognitive abilities and their associations with psychiatric disorder risk, we conducted genome-wide association studies (GWAS) of a total of 335,227 consented 23andMe customers of European descent between the ages of 50 and 85, who completed at least one online test of crystallized cognitive ability (vocabulary knowledge, N=188,434) and/or fluid cognitive ability (visual change detection, N=158 888; digit-symbol substitution, N=132,807). All cognitive measures were significantly heritable (h2=0.10-0.16), and GWAS identified 25 novel genome-wide significant loci. Genetic correlation analyses highlight variable profiles of genetic relationships across tasks and disorders. While schizophrenia had moderate negative genetic correlations with tests of fluid cognition (visual change detection rg=-0.27, p<9.2e-24; digit-symbol substitution rg=-0.26, p<5.2e-27), it was only weakly negatively associated with crystalized cognition (vocabulary knowledge rg=-0.07, p<0.004). Autism, in contrast, showed a robust positive genetic correlation with vocabulary knowledge (rg=0.30, p<5.6e-13) and little to no genetic correlation with either fluid cognition task (rgs<0.08, ps>0.005). Crystalized and fluid cognitive abilities thus have correlated but distinct genetic architectures that relate to those of psychiatric disorders. Understanding the genetic underpinnings of specific cognitive abilities, and their relationships to psychiatric disorder risk, can inform the understanding of disease biology nosology and etiology.

genetics↗

bioRxiv2020

Genome-wide association analysis and replication in 810,625 individuals identifies novel therapeutic targets for varicose veins

BackgroundVaricose veins (VVs) affect one-third of Western society, with a significant subset of patients developing venous ulceration, and ongoing management of venous leg ulcers costing around $14.9 billion annually in the USA. There is no current medical management for VVs, with approaches limited to compression stockings, ablation techniques, or open surgery for more advanced disease. A significant proportion of patients report a positive family history, and heritability is ~17%, suggesting a strong genetic component. We aimed to identify novel therapeutic targets by improving our understanding of the aetiopathology and genetic architecture of VVs. MethodsWe performed the largest two-stage genome-wide association study of VVs in 401,656 subjects from UK Biobank, and replication in 408,969 subjects from 23andMe (total 135,514 varicose veins cases and 675,111 controls). We constructed a genetic risk score for VVs to investigate its use as a prognostic tool. Genes and pathways were prioritised using a suite of bioinformatic tools, and therapeutic targets identified using the Open Targets Platform. ResultsWe discovered 49 signals at 46 susceptibility loci associated with VVs, including 29 previously unreported genetic associations (28 susceptibility loci). We demonstrated that patients with VVs requiring surgery have a higher genetic risk score than those managed non-surgically. We map 237 genes to these loci, many of which are biologically relevant and tractable to therapeutic targeting or repurposing (notably VEGFA, COL27A1, EFEMP1, PPP3R1 and NFATC2). Tissue enrichment analyses implicated vascular tissue, and several genes were enriched in biological pathways relating to extracellular matrix biology, inflammation, angiogenesis, lymphangiogenesis, vascular smooth muscle cell migration, and apoptosis. ConclusionsGenes and pathways identified represent biologically plausible contributors to the pathobiology of VVs, identifying promising candidates for further investigation of venous biology and potential therapeutic targets. We have provided the proof-of-principle that genetic risk score correlates with disease severity, which represents a first step in personalised medicine approaches to varicose veins.

genetics↗

bioRxiv2020

Genetic analyses identify widespread sex-differential participation bias

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of this participation bias is challenging as it requires the genotypes of unseen individuals. However, we demonstrate that it is possible to estimate comparative biases by performing GWAS contrasting one subgroup versus another. For example, we show that sex exhibits autosomal heritability in the presence of sex-differential participation bias. By performing a GWAS of sex in ~3.3 million males and females, we identify over 158 autosomal loci significantly associated with sex and highlight complex traits underpinning differences in study participation between sexes. For example, the body mass index (BMI) increasing allele at the FTO locus was observed at higher frequency in males compared to females (OR 1.02 [1.02-1.03], P=4.4x10-36). Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

genetics↗

bioRxiv2020

Genome-wide study identifies association between HLA-B*55:01 and penicillin allergy

BackgroundHypersensitivity reactions to drugs are often unpredictable and can be life-threatening, underscoring a need for understanding their underlying mechanisms and risk factors. The extent to which germline genetic variation influences the risk of commonly reported drug allergies such as penicillin allergy remains largely unknown. MethodsWe extracted data from the electronic health records of 52,000 Estonian and 500,000 UK biobank participants to study the role of genetic variation in the occurrence of penicillin hypersensitivity reactions. We used imputed SNP to HLA typing data from up to 22,554 and 488,377 individuals from the Estonian and UK cohorts, respectively, to further fine-map the human leukocyte antigen (HLA) association and replicated our results in two additional cohorts involving a total of 1.14 million individuals. ResultsGenome-wide meta-analysis of penicillin allergy revealed a significant association located in the HLA region on chromosome 6. The signal was further fine-mapped to the HLA-B*55:01 allele (OR 1.47 95% CI 1.37-1.58, P-value 4.63x10-26) and confirmed by independent replication in two cohorts. The meta-analysis of all four cohorts in the study revealed a strong association of HLA-B*55:01 allele with penicillin allergy (OR 1.33 95% CI 1.29-1.37, P-value 2.23x10-72). In silico follow-up suggests a potential effect on T lymphocytes at HLA-B*55:01. ConclusionWe present the first robust evidence for the role of an allele of the major histocompatibility complex (MHC) I gene HLA-B in the occurrence of penicillin allergy.

genetics↗

bioRxiv2019

Genome-wide association study of pain sensitivity assessed by questionnaire and the cold pressor test

Altered pain sensitivity is believed to play an important role in the development of chronic pain, a common debilitating condition affecting an estimated 1 in 5 adults. Pain sensitivity varies broadly between individuals, and it is notoriously difficult to measure at scale in large populations. Although pain sensitivity is known to be moderately heritable, only a small number of genetic studies have been published and they have had limited success in identifying the genetic architecture of pain sensitivity. In this study, we deployed an online pain sensitivity questionnaire (PSQ) and an at-home version of the cold pressor test (CPT) in a large genotyped cohort. We performed genome-wide association studies (GWAS) on the PSQ score (n = 25,321) and CPT duration (n = 6,853). Despite a reasonably large sample size, we identified only one genome-wide significant locus associated with the PSQ score, which was located in the TSSC1 gene (rs58194899, OR = 0.950 [0.933-0.967], P-value = 1.9*10-8). This gene is responsible for intracellular cell trafficking and it could modulate pain sensitivity via changes in neuroplasticity. PSQ score is genetically correlated with chronic and acute pain, including chronic neck and shoulder pain (rg = 0.71), fracture (0.71), rheumatoid arthritis (0.68), osteoarthritis (0.38), and pneumothorax (0.82). PSQ score was also genetically correlated with known risk factors, such as the length of working week (0.65), noisy workplace (0.41), smoking (0.36), or extreme BMI (0.23). Gene-based analysis followed by pathway analysis showed that GWAS results were enriched for genes expressed in the brain, predominantly in the frontal cortex and basal ganglia, and enriched for genes involved in neuronal development and glutamatergic synapse signaling pathways. Finally, we confirmed that females with red hair were more sensitive to pain and found that genetic variation in the MC1R gene was associated with an increase in pain sensitivity as assessed by the PSQ. Overall, we detailed the genetic background of pain sensitivity using scalable at-home procedures that may serve as a blueprint for larger genetic studies. Author SummaryDespite decades of research and many advances in our understanding of the physical, emotional, and psychological aspects, the genetic contribution to pain sensitivity is still largely unknown. We administered a pain sensitivity questionnaire in parallel with an experimental cold pressor test in a large, genotyped cohort of 31,000 research participants, and found a novel genome-wide genetic association in TSSC1. This gene is responsible for intracellular cell trafficking and it could modulate pain sensitivity via changes in neuroplasticity. Overall, the pain sensitivity measurements revealed strong positive genetic correlations with chronic and acute pain conditions, as well as with unhealthy traits and behaviors, such as obesity, smoking, or difficult working conditions. The genetic association results also suggested, perhaps unsurprisingly, that only the brain tissues could be relevant for pain sensitivity, and identified enrichments for glutamatergic synapse pathways. Despite the complexity of pain sensitivity, our study demonstrated that it is now possible to deploy sophisticated pain questionnaires online and perform experimental medicine tests in at-home settings to further study the genetic architecture of pain sensitivity.

genetics↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗

bioRxiv2019

A two-stage approach to identifying and validating modifiable factors for the prevention of depression

BackgroundAlthough depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression.\n\nMethodsWe curated baseline data on more than 100 lifestyle and environmental factors in participants lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genome-wide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa.\n\nResultsA range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk.\n\nConclusionAs the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset ones genetic risk has become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.

genetics↗