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Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4 – 23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Atrial fibrillation (AF) affects more than 33 million individuals worldwide 1 and has a complex heritability 2 . We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF. This large, multi-ethnic genome-wide association study identifies 97 loci significantly associated with atrial fibrillation. These loci are enriched for genes involved in cardiac development, electrophysiology, structure and contractile function.

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury 1 – 4 . These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries 5 . Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction. A multi-ancestry meta-regression study analyses diverse genome-wide association studies and genome loci associated with tobacco and alcohol use.

Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. Integration of GWAS and single-cell transcriptomic data from the entire nervous system systematically identifies cell types underlying brain complex traits and provides insights into the etiology of Parkinson’s disease.

A number of epidemiological and genetic studies have attempted to determine whether levels of circulating lipids are associated with risks of various cancers, including breast cancer (BC). However, it remains unclear whether a causal relationship exists between lipids and BC. If alteration of lipid levels also reduced risk of BC, this could present a target for disease prevention. This study aimed to assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein, HDL; low-density lipoprotein, LDL; triglycerides, TGs) with risk for BC using Mendelian randomization (MR). Data from genome-wide association studies in up to 215,551 participants from the Million Veteran Program (MVP) were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on BC risk was evaluated using genetic data from the BCAC (Breast Cancer Association Consortium) based on 122,977 BC cases and 105,974 controls. Using MR, we observed that a 1-standard-deviation genetically determined increase in HDL levels is associated with an increased risk for all BCs (HDL: OR [odds ratio] = 1.08, 95% confidence interval [CI] = 1.04-1.13, P < 0.001). Multivariable MR analysis, which adjusted for the effects of LDL, TGs, body mass index (BMI), and age at menarche, corroborated this observation for HDL (OR = 1.06, 95% CI = 1.03-1.10, P = 4.9 × 10-4) and also found a relationship between LDL and BC risk (OR = 1.03, 95% CI = 1.01-1.07, P = 0.02). We did not observe a difference in these relationships when stratified by breast tumor estrogen receptor (ER) status. We repeated this analysis using genetic variants independent of the leading association at core HDL pathway genes and found that these variants were also associated with risk for BCs (OR = 1.11, 95% CI = 1.06-1.16, P = 1.5 × 10-6), including locus-specific associations at ABCA1 (ATP Binding Cassette Subfamily A Member 1), APOE-APOC1-APOC4-APOC2 (Apolipoproteins E, C1, C4, and C2), and CETP (Cholesteryl Ester Transfer Protein). In addition, we found evidence that genetic variation at the ABO locus is associated with both lipid levels and BC. Through multiple statistical approaches, we minimized and tested for the confounding effects of pleiotropy and population stratification on our analysis; however, the possible existence of residual pleiotropy and stratification remains a limitation of this study. We observed that genetically elevated plasma HDL and LDL levels appear to be associated with increased BC risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal of developing potential therapeutic strategies aimed at altering the cholesterol-mediated effect on BC risk.

The genetic etiologies of more than half of rare diseases remain unknown. Standardized genome sequencing and phenotyping of large patient cohorts provide an opportunity for discovering the unknown etiologies, but this depends on efficient and powerful analytical methods. We built a compact database, the ‘Rareservoir’, containing the rare variant genotypes and phenotypes of 77,539 participants sequenced by the 100,000 Genomes Project. We then used the Bayesian genetic association method BeviMed to infer associations between genes and each of 269 rare disease classes assigned by clinicians to the participants. We identified 241 known and 19 previously unidentified associations. We validated associations with ERG , PMEPA1 and GPR156 by searching for pedigrees in other cohorts and using bioinformatic and experimental approaches. We provide evidence that (1) loss-of-function variants in the Erythroblast Transformation Specific (ETS)-family transcription factor encoding gene ERG lead to primary lymphoedema, (2) truncating variants in the last exon of transforming growth factor-β regulator PMEPA1 result in Loeys–Dietz syndrome and (3) loss-of-function variants in GPR156 give rise to recessive congenital hearing impairment. The Rareservoir provides a lightweight, flexible and portable system for synthesizing the genetic and phenotypic data required to study rare disease cohorts with tens of thousands of participants. A flexible and compact database containing rare variant genotypes and phenotypes of 77,539 participants sequenced by the 100,000 Genomes Project enables the identification of new disease-causing genes.

To improve the 'personalized-medicine' approach to the treatment of depression, we need to identify biomarkers that, assessed before starting treatment, predict future response to antidepressants ('predictors'), as well as biomarkers that are targeted by antidepressants and change longitudinally during the treatment ('targets'). In this study, we tested the leukocyte mRNA expression levels of genes belonging to glucocorticoid receptor (GR) function (FKBP-4, FKBP-5, and GR), inflammation (interleukin (IL)-1α, IL-1β, IL-4, IL-6, IL-7, IL-8, IL-10, macrophage inhibiting factor (MIF), and tumor necrosis factor (TNF)-α), and neuroplasticity (brain-derived neurotrophic factor (BDNF), p11 and VGF), in healthy controls (n=34) and depressed patients (n=74), before and after 8 weeks of treatment with escitalopram or nortriptyline, as part of the Genome-based Therapeutic Drugs for Depression study. Non-responders had higher baseline mRNA levels of IL-1β (+33%), MIF (+48%), and TNF-α (+39%). Antidepressants reduced the levels of IL-1β (-6%) and MIF (-24%), and increased the levels of GR (+5%) and p11 (+8%), but these changes were not associated with treatment response. In contrast, successful antidepressant response was associated with a reduction in the levels of IL-6 (-9%) and of FKBP5 (-11%), and with an increase in the levels of BDNF (+48%) and VGF (+20%)-that is, response was associated with changes in genes that did not predict, at the baseline, the response. Our findings indicate a dissociation between 'predictors' and 'targets' of antidepressant responders. Indeed, while higher levels of proinflammatory cytokines predict lack of future response to antidepressants, changes in inflammation associated with antidepressant response are not reflected by all cytokines at the same time. In contrast, modulation of the GR complex and of neuroplasticity is needed to observe a therapeutic antidepressant effect.

Dipender Gill and Stephen Burgess discuss the accompanying study by James Yarmolinsky and colleagues investigating the associations between genetically-proxied inhibition of antihypertensive drug targets and risk of common cancer subtypes using Mendelian randomization.

Many maternal traits are associated with a neonate's gestational duration, birth weight, and birth length. These birth outcomes are subsequently associated with late-onset health conditions. The causal mechanisms and the relative contributions of maternal and fetal genetic effects behind these observed associations are unresolved. Based on 10,734 mother-infant duos of European ancestry from the UK, Northern Europe, Australia, and North America, we constructed haplotype genetic scores using single-nucleotide polymorphisms (SNPs) known to be associated with adult height, body mass index (BMI), blood pressure (BP), fasting plasma glucose (FPG), and type 2 diabetes (T2D). Using these scores as genetic instruments, we estimated the maternal and fetal genetic effects underlying the observed associations between maternal phenotypes and pregnancy outcomes. We also used infant-specific birth weight genetic scores as instrument and examined the effects of fetal growth on pregnancy outcomes, maternal BP, and glucose levels during pregnancy. The maternal nontransmitted haplotype score for height was significantly associated with gestational duration (p = 2.2 × 10-4). Both maternal and paternal transmitted height haplotype scores were highly significantly associated with birth weight and length (p < 1 × 10-17). The maternal transmitted BMI scores were associated with birth weight with a significant maternal effect (p = 1.6 × 10-4). Both maternal and paternal transmitted BP scores were negatively associated with birth weight with a significant fetal effect (p = 9.4 × 10-3), whereas BP alleles were significantly associated with gestational duration and preterm birth through maternal effects (p = 3.3 × 10-2 and p = 4.5 × 10-3, respectively). The nontransmitted haplotype score for FPG was strongly associated with birth weight (p = 4.7 × 10-6); however, the glucose-increasing alleles in the fetus were associated with reduced birth weight through a fetal effect (p = 2.2 × 10-3). The haplotype scores for T2D were associated with birth weight in a similar way but with a weaker maternal effect (p = 6.4 × 10-3) and a stronger fetal effect (p = 1.3 × 10-5). The paternal transmitted birth weight score was significantly associated with reduced gestational duration (p = 1.8 × 10-4) and increased maternal systolic BP during pregnancy (p = 2.2 × 10-2). The major limitations of the study include missing and heterogenous phenotype data in some data sets and different instrumental strength of genetic scores for different phenotypic traits. We found that both maternal height and fetal growth are important factors in shaping the duration of gestation: genetically elevated maternal height is associated with longer gestational duration, whereas alleles that increase fetal growth are associated with shorter gestational duration. Fetal growth is influenced by both maternal and fetal effects and can reciprocally influence maternal phenotypes: taller maternal stature, higher maternal BMI, and higher maternal blood glucose are associated with larger birth size through maternal effects; in the fetus, the height- and metabolic-risk-increasing alleles are associated with increased and decreased birth size, respectively; alleles raising birth weight in the fetus are associated with shorter gestational duration and higher maternal BP. These maternal and fetal genetic effects may explain the observed associations between the studied maternal phenotypes and birth outcomes, as well as the life-course associations between these birth outcomes and adult phenotypes.

In recent years, epidemiologists have increasingly sought to employ genetic data to identify ‘causal’ relationships between exposures of interest and various endpoints – an instrumental variable approach sometimes termed Mendelian randomization. However, this approach is subject to all of the limitations of instrumental variable analysis and to several limitations specific to its genetic underpinnings, including confounding, weak instrument bias, pleiotropy, adaptation, and failure of replication. Although the approach enjoys some utility in testing the etiological role of discrete biochemical pathways, like folate metabolism, examples like that of alcohol consumption and cardiovascular disease demonstrate that it must be treated with all of the circumspection that should accompany all forms of observational epidemiology. Going forward, we urge the elimination of randomization or causality in reports of its use and suggest that Mendelian randomization instead be termed exactly what it is – genetic instrumental variable analysis.