Biology subjects

23andMe Research Team,

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

10 recordsLinked to original sources

bioRxiv2018

Genome-wide meta-analysis of depression in 807,553 individuals identifies 102 independent variants with replication in a further 1,507,153 individuals

Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

genetics↗

bioRxiv2018

Evidence of causal effect of major depression on alcohol dependence: Findings from the Psychiatric Genomics Consortium

BackgroundDespite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood.\n\nMethodsThis study was conducted using genome-wide data from the Psychiatric Genomics Consortium (MD: 135,458 cases and 344,901 controls; AD: 10,206 cases and 28,480 controls) and UK Biobank (AC-Frequency: from \"daily or almost daily\" to \"never\", 438,308 individuals; AC-Quantity: total units of alcohol per week, 307,098 individuals). Linkage disequilibrium score regression and Mendelian Randomization (MR) analyses were applied to investigate shared genetic mechanisms (horizontal pleiotropy) and causal relationships (mediated pleiotropy) among these traits.\n\nOutcomesPositive genetic correlation was observed between MD and AD (rgMD-AD=+0.47, P=6.6x10-10). AC-Quantity showed positive genetic correlation with both AD (rgAD-AC-Quantity=+0.75, P=1.8x10-14) and MD (rgMD-AC-Quantity=+0.14, P=2.9x10-7), while there was negative correlation of AC-Frequency with MD (rgMD-AC-Frequency=-0.17, P=1.5x10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e., causal relationship) with a causal role of MD on AD (beta=0.28, P=1.29x10-6) that does not appear to be biased by confounding such as horizontal pleiotropy. No evidence of reverse causation was observed as the AD genetic instrument did not show a causal effect on MD.\n\nInterpretationResults support a causal role for MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity not only addresses important public health concerns but also has the potential to facilitate prevention and intervention efforts.\n\nFundingNational Institute of Mental Health and National Institute on Drug Abuse.\n\nPutting data into contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to August 24, 2018, for research studies that investigated causality among alcohol-and depression related phenotypes using Mendelian randomization approaches. We used the search terms \"alcohol\" AND \"depression\" AND \"Mendelian Randomization\". No restrictions were applied to language, date, or article type. Ten articles were retrieved, but only two were focused on alcohol consumption and depression-related traits. The studies were based on genetic variants in alcohol dehydrogenase (ADH) genes only, did not find evidence for a causal effect of alcohol consumption on depression phenotypes, with one study finding a causal effect of alcohol consumption on alcoholism. Both studies noted that future studies are needed with increased sample sizes and clinically derived phenotypes. To our knowledge, no previous study has applied two-sample Mendelian randomization to investigate causal relationships between alcohol dependence and major depression.\n\nTwin studies show genetic factors influence susceptibility to MD, AD, and alcohol consumption. Differently from observational approaches where several studies have investigated the relationship between alcohol-and depression-related phenotypes, very limited use of molecular genetic data has been applied to investigate this issue. Additionally, the use of genetic information has been shown to be less biased by confounders and reverse causation than observation data. However, genetic approaches, like Mendelian randomization, require large sample sizes to be informative.\n\nAdded value of this studyIn this study, we used genome-wide data from the Psychiatric Genomic Consortium and UK Biobank, which include information regarding hundred thousands of individuals, to test the presence of shared genetic mechanisms and causal relationships among major depression, alcohol dependence, and alcohol consumption. The results support a causal influence of MD on AD, while alcohol consumption showed shared genetic mechanisms with respect to both major depression and alcohol dependence.\n\nImplications of all the available evidenceGiven the significant morbidity and mortality associated with MD, AD, and the comorbid condition, understanding mechanisms underlying these associations not only address important public health concerns but also has the potential to facilitate prevention and intervention efforts.

genetics↗

bioRxiv2018

Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, which includes coding, conserved, regulatory and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods in simulations using real genotypes. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank. We used association statistics from British-ancestry samples as training data (avg N=373K) and samples of other European ancestries as validation data (avg N=22K), to minimize confounding. LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2=0.144; highest R2=0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total N=1107K; higher heritability in UK Biobank cohort) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

genetics↗

bioRxiv2018

Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease

Using genome-wide data from 697,828 research participants from 23andMe and UK Biobank, we increase the number of identified loci associated with being a morning person, a behavioural indicator of a persons underlying circadian rhythm, from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we show that the chronotype loci influence sleep timing: the mean sleep timing of the 5% of individuals carrying the most "morningness" alleles was 25 minutes earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. We provide evidence that being a morning person is causally associated with better mental health but does not appear to affect BMI or Type 2 diabetes. This study offers new insights into the biology of circadian rhythms and links to disease in humans.

genetics↗

bioRxiv2018

Association of whole-genome and NETRIN1 signaling pathway-derived polygenic risk scores for Major Depressive Disorder and thalamic radiation white matter microstructure in UK Biobank

BackgroundMajor Depressive Disorder (MDD) is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome, and growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide an important route for identification of disease mechanisms by focusing on a specific process excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether MDD polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRS) and the whole genome excluding NETRIN1 pathway genes (genomic-PRS) were associated with white matter integrity.\n\nMethodsWe used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: N = 6,401; MD: N = 6,390).\n\nResultsWe found significantly lower FA in the superior longitudinal fasciculus ({beta} = -0.035, pcorrected = 0.029) and significantly higher MD in a global measure of thalamic radiations ({beta} = 0.029, pcorrected = 0.021), as well as higher MD in the superior ({beta} = 0.034, pcorrected = 0.039) and inferior ({beta} = 0.029, pcorrected = 0.043) longitudinal fasciculus and in the anterior ({beta} = 0.025, pcorrected = 0.046) and superior ({beta} = 0.027, pcorrected = 0.043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.\n\nConclusionsOur findings indicate that variation in the NETRIN1 signaling pathway may confer risk for MDD through effects on thalamic radiation white matter microstructure.

genetics↗

bioRxiv2018

Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,958)

Alcohol use disorders (AUD) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test (AUDIT) from two population-based cohorts of European ancestry: UK Biobank (UKB; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption (AUDIT-C), and for items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (rg=0.22, p=3.0x10-10), major depressive disorder (rg=0.26, p=5.6x10-3), and attention-deficit/hyperactivity disorder (ADHD; rg=0.23, p=1.1x10-5), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (rg=-0.24, p=3.7x10-3) and ADHD (rg=-0.10, p=1.8x10-2). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of [≤]4 as controls and [≥]12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (rg=0.82, p=3.2x10-6) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.

genetics↗

bioRxiv2018

Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ([Formula] to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

genetics↗

bioRxiv2017

Common variant burden contributes significantly to the familial aggregation of migraine in 1,589 families

It has long been observed that complex traits, including migraine, often aggregate in families, but the underlying genetic architecture behind this is not well understood. Two competing hypotheses exist, emphasizing either rare or common genetic variation. More specifically, familial aggregation could be predominantly explained by rare, penetrant variants that segregate according to Mendelian inheritance or rather by the sufficient polygenic accumulation of many common variants, each with an individually small effect. Some combination of both common and rare variation could also contribute towards a spectrum of disease risk.\n\nWe investigated this in a collection of 8,319 individuals across 1,589 migraine families from Finland. Family members were individually diagnosed by a migraine-specific questionnaire with either migraine without aura (MO, ICHD-3 code 1.1, n=2,357), migraine with typical aura (ICHD- 3 code 1.2.1, n=2,420), hemiplegic migraine (HM, ICHD-3 code 1.2.3, n=540), or no migraine (n=3,002). For comparison, we used population-based migraine cases (n=1,101) and controls (n=13,369) from the FINRISK study. The disease status of FINRISK individuals was assigned based on health registry data from outpatient clinics and/or prescription medication. All individuals were genotyped on the Illumina(R) CoreExome or PsychArray chip platforms and imputed to a Finnish reference panel of 6,962 haplotypes. Polygenic risk scores (PRS), representing the common variant burden in each individual, were calculated using weights from the most recent large-scale genome-wide association study of migraine. To account for family structure in our analyses, we used a mixed-model approach, adjusting for the genetic relationship matrix as a random effect.\n\nWe found a significantly higher common variant burden in familial cases of migraine (for all subtypes, measured by the odds ratio [OR] per standard deviation [SD] increase in PRS; OR = 1.76, 95% CI = 1.71-1.81, P = 1.7x10-109) compared to cases from a population cohort (OR = 1.32, 95% CI = 1.25-1.38, P = 7.2x10-17) when using the population controls as a reference group. The highest enrichment was observed for HM (OR = 1.96, 95% CI = 1.86-2.07, P = 8.7x10-36) and migraine with typical aura (OR = 1.85, 95% CI = 1.79-1.91, P = 1.4x10-86) but enrichment was also present for MO (OR = 1.57, 95% CI = 1.51-1.63, P = 1.1x10-48). Comparing within cases, there was no significant difference in common variant burden between the migraine with aura subtypes, HM and migraine with typical aura (OR = 1.09, 95% CI = 0.99-1.19, P = 0.09), but both showed significantly higher enrichment compared to MO (OR = 1.28, 95% CI = 1.17-1.38, P = 7.3x10-7, and OR = 1.17, 95% CI = 1.11-1.23, P = 4.62x10-5, respectively). Additionally, we found that higher common variant burden corresponded to earlier age of headache onset (OR per SD increase in PRS for 3,631 cases with onset before 20 years old compared to 1,686 cases with onset later than 20 years old; OR = 1.11, 95% CI = 1.05-1.18, P = 8.3x10-4). FINRISK population cases identified from national health registry data were found to have lower common variant burden in comparison to the familial migraine cases (OR = 1.32, 95% CI = 1.25-1.38, P = 6.8x10-17), unless the individuals had attended both a specialist clinic and also received prophylactic migraine treatment (OR = 1.70, 95% CI = 1.53-1.88, P = 3.9x10-9). Finally, although rare variants have been suggested as the primary cause for familial hemiplegic migraine (FHM), we found only four out of 45 sequenced FHM families (8.9%) with a pathogenic mutation in one of the known risk genes.\n\nIn summary, our results demonstrate a substantial contribution of common polygenic variation to familial aggregation in migraine, comparable to both controls and that observed in migraine cases from a population cohort. The findings also suggest that individuals with migraine aura symptoms (either typical aura, which is mostly visual, or rare motor aura) tend to have higher common variant burden on average supporting the polygenic model also in these migraine subtypes.

genetics↗

bioRxiv2017

GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4-6. Previous genome-wide association studies7-12 (GWAS) reported 16 genomic loci10-12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3x10-8), medium spiny neurons (P=4x10-8) and serotonergic neurons (P=1x10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4x10-9), behavioural response to cocaine processes (P=1.84x10-7), and axon part (P=5.26x10-8). We show that neuroticisms genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

genetics↗

bioRxiv2017

MTAG: Multi-Trait Analysis of GWAS

We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

genomics↗