The UK Biobank resource with deep phenotyping and genomic data

Genes for Good: Engaging the Public in Genetics Research via Social Media

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

A commonly occurring genetic variant within the NPLOC4-TSPAN10-PDE6G gene cluster is associated with the risk of strabismus

Strabismus refers to an abnormal alignment of the eyes leading to the loss of central binocular vision. Concomitant strabismus occurs when the angle of deviation is constant in all positions of gaze and often manifests in early childhood when it is considered to be a neurodevelopmental disorder of the visual system. As such, it is inherited as a complex genetic trait, affecting 2-4% of the population. A genome-wide association study (GWAS) for self-reported strabismus (1345 cases and 65,349 controls from UK Biobank) revealed a single genome-wide significant locus on chromosome 17q25. Approximately 20 variants across the NPLOC4-TSPAN10-PDE6G gene cluster and in almost perfect linkage disequilibrium (LD) were most strongly associated (lead variant: rs75078292, OR = 1.26, p = 2.24E-08). A recessive model provided a better fit to the data than an additive model. Association with strabismus was independent of refractive error, and the degree of association with strabismus was minimally attenuated after adjustment for amblyopia. The association with strabismus was replicated in an independent cohort of clinician-diagnosed children aged 7 years old (116 cases and 5084 controls; OR = 1.85, p = 0.009). The associated variants included 2 strong candidate causal variants predicted to have functional effects: rs6420484, which substitutes tyrosine for a conserved cysteine (C177Y) in the TSPAN10 gene, and a 4-bp deletion variant, rs397693108, predicted to cause a frameshift in TSPAN10. The population-attributable risk for the locus was approximately 8.4%, indicating an important role in conferring susceptibility to strabismus.

Correction for multiple testing in candidate-gene methylation studies

Aim: We compared the performance of multiple testing corrections for candidate gene methylation studies, namely Sidak (accurate Bonferroni), false-discovery rate and three adjustments that incorporate the correlation between CpGs: extreme tail theory (ETT), Gao et al. (GEA), and Li and Ji methods. Materials & methods: The experiment-wide type 1 error rate was examined in simulations based on Illumina EPIC and 450K data. Results: For high-correlation genes, Sidak and false-discovery rate corrections were conservative while the Li and Ji method was liberal. The GEA method tended to be conservative unless a threshold parameter was adjusted. The ETT yielded an appropriate type 1 error rate. Conclusion: For genes with substantial correlation across measured CpGs, GEA and ETT can appropriately correct for multiple testing in candidate gene methylation studies.

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.

A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank

BACKGROUND

Mendelian randomisation allows for the testing of causal effects in situations where clinical trials are challenging to do. In this hypothesis-free, data-driven phenome-wide association study (PheWAS), we sought to assess possible associations of high body-mass index (BMI) with multiple disease outcomes.

METHODS

For this registry-based case-control PheWAS, we used genome-wide data available from the UK Biobank to construct a genetic risk score of 76 variants related to BMI. Eligible UK Biobank participants were aged 37-73 years during recruitment, were white British, were unrelated to each other, and had available genetic information. Disease outcomes from these participants were mapped to a phenotype code (phecode). Participants with a phecode of interest were recoded as cases, whereas participants without a phecode of interest or any codes under a parent phecode were classified as controls. We did a PheWAS to analyse possible associations between the BMI genetic risk score and a range of disease outcomes. Disease associations passing stringent correction for multiple testing (Bonferroni corrected threshold p<5·4 × 10^-5, false discovery rate corrected p<0·0074) were assessed for causal association with use of inverse-variance weighted mendelian randomisation. We did sensitivity analyses to assess pleiotropy and stability of estimation with use of weighted median, weighted mode, Egger regression, and mendelian randomisation pleiotropy residual sum and outlier methods.

FINDINGS

Our study population comprised 337 536 UK Biobank participants, and analyses were done for 925 unique phecodes from 17 different disease categories. After Bonferroni correction, PheWAS identified that BMI genetic risk score was associated with hospital-diagnosed obesity and 58 other outcomes; 30 distinct disease associations were supported by the mendelian randomisation analyses. 30 distinct disease associations were supported by the mendelian randomisation analyses. In inverse-variance weighted mendelian randomisation, genetically determined BMI was associated with endocrine disorders (odds ratio per one SD or 4·1 kg/m² higher BMI 2·72, 95% CI 2·33-3·29 for type 2 diabetes; 2·11, 1·62-2·76 for type 1 diabetes; and 1·46, 1·25-1·70 for hypothyroidism), circulatory diseases (1·96, 1·53-2·51 for phlebitis and thrombophlebitis; 1·89, 1·39-2·57 for cardiomegaly; 1·68, 1·35-2·09 for congestive heart failure; 1·55, 1·37-1·76 for hypertension; 1·31, 1·13-1·52 for ischaemic heart disease; and 1·25, 1·14-1·37 for cardiac dysrhythmias), and inflammatory or dermatological conditions (2·00, 1·72-2·23 for superficial cellulitis and abscess; 3·37, 2·17-5·25 for chronic ulcers of leg and foot; 4·99, 2·54-9·82 for gangrene; and 2·24, 1·53-3·28 for atopy). Mendelian randomisation analyses provided further support for a causal effect of BMI on renal failure, osteoarthrosis, neurological (insomnia and peripheral nerve disorders) and respiratory diseases (asthma and chronic bronchitis), structural problems (hernias and knee derangement), and chemotherapy treatment. Mendelian randomisation with Egger regression produced consistently wider CIs compared with those of other methods. 26 of 72 distinct diseases detected under false discovery rate correction produced consistent estimates across at least four mendelian randomisation methods, and consistent evidence across all five approaches was obtained for 14 diseases.

INTERPRETATION

Our data-driven approach identified a range of diseases as possibly affected by high BMI. This population-level screening approximated the accumulated consequences of high BMI, whereas the true effects might be more complex and vary by life stage. Our results highlight the importance of obesity prevention and effective management of obesity-related comorbidities.

FUNDING

National Health and Medical Research Council of Australia.

Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study

OBJECTIVE

To examine whether sleep traits have a causal effect on risk of breast cancer.

DESIGN

Mendelian randomisation study.

SETTING

UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study.

PARTICIPANTS

156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR analysis.

EXPOSURES

Self reported chronotype (morning or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these sleep traits.

MAIN OUTCOME MEASURE

Breast cancer diagnosis.

RESULTS

In multivariable regression analysis using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increase), whereas there was little evidence for an association between sleep duration and insomnia symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with sleep duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective effect of morning preference on breast cancer risk (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. Two sample MR using data from BCAC supported findings for a protective effect of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, 1.02 to 1.39 per hour increase) on breast cancer risk (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy.

CONCLUSIONS

Findings showed consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.

Genome-wide association study identifies loci for arterial stiffness index in 127,121 UK Biobank participants

Arterial stiffness index (ASI) is a non-invasive measure of arterial stiffness using infra-red finger sensors (photoplethysmography). It is a well-suited measure for large populations as it is relatively inexpensive to perform, and data can be acquired within seconds. These features raise interest in using ASI as a tool to estimate cardiovascular disease risk as prior work demonstrates increased arterial stiffness is associated with elevated systolic blood pressure, and ASI is predictive of cardiovascular disease and mortality. We conducted genome-wide association studies (GWASs) for ASI in 127,121 UK Biobank participants of European-ancestry. Our primary analyses identified variants at four loci reaching genome-wide significance (P < 5 × 10^-8): TEX41 (rs1006923; P = 5.3 × 10^-12), FOXO1 (rs7331212; P = 2.2 × 10^-11), C1orf21 (rs1930290, P = 1.1 × 10^-8) and MRVI1 (rs10840457, P = 3.4 × 10^-8). Gene-based testing revealed three significant genes, the most significant gene was COL4A2 (P = 1.41 × 10^-8) encoding type IV collagen. Other candidate genes at associated loci were also involved in smooth muscle tone regulation. Our findings provide new information for understanding the development of arterial stiffness.

Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data

Dental caries and periodontitis account for a vast burden of morbidity and healthcare spending, yet their genetic basis remains largely uncharacterized. Here, we identify self-reported dental disease proxies which have similar underlying genetic contributions to clinical disease measures and then combine these in a genome-wide association study meta-analysis, identifying 47 novel and conditionally-independent risk loci for dental caries. We show that the heritability of dental caries is enriched for conserved genomic regions and partially overlapping with a range of complex traits including smoking, education, personality traits and metabolic measures. Using cardio-metabolic traits as an example in Mendelian randomization analysis, we estimate causal relationships and provide evidence suggesting that the processes contributing to dental caries may have undesirable downstream effects on health.

Unilateral L4-dorsal root ganglion stimulation evokes pain relief in chronic neuropathic postsurgical knee pain and changes of inflammatory markers: part II whole transcriptome profiling

Background

In our recent clinical trial, increased peripheral concentrations of pro-inflammatory molecular mediators were determined in complex regional pain syndrome (CRPS) patients. After 3 months adjunctive unilateral, selective L4 dorsal root ganglion stimulation (L4-DRG_STIM), significantly decreased serum IL-10 and increased saliva oxytocin levels were assessed along with an improved pain and functional state. The current study extended molecular profiling towards gene expression analysis of genes known to be involved in the gonadotropin releasing hormone receptor and neuroinflammatory (cytokines/chemokines) signaling pathways.

Methods

Blood samples were collected from 12 CRPS patients for whole-transcriptome profiling in order to assay 18,845 inflammation-associated genes from frozen blood at baseline and after 3 months L4-DRG_STIM using PANTHER™ pathway enrichment analysis tool.

Results

Pathway enrichment analyses tools (GOrilla™ and PANTHER™) showed predominant involvement of inflammation mediated by chemokines/cytokines and gonadotropin releasing hormone receptor pathways. Further, screening of differentially regulated genes showed changes in innate immune response related genes. Transcriptomic analysis showed that 21 genes (predominantly immunoinflammatory) were significantly changed after L4-DRG_STIM. Seven genes including TLR1, FFAR2, IL1RAP, ILRN, C5, PKB and IL18 were down regulated and fourteen genes including CXCL2, CCL11, IL36G, CRP, SCGB1A1, IL-17F, TNFRSF4, PLA2G2A, CREB3L3, ADAMTS12, IL1F10, NOX1, CHIA and BDKRB1 were upregulated.

Conclusions

In our sub-group analysis of L4-DRG_STIM treated CRPS patients, we found either upregulated or downregulated genes involved in immunoinflammatory circuits relevant for the pathophysiology of CRPS indicating a possible relation. However, large biobank-based approaches are recommended to establish genetic phenotyping as a quantitative outcome measure in CRPS patients.

Trial registration The study protocol was registered at the 15.11.2016 on German Register for Clinical Trials (DRKS ID 00011267). https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011267

Advancements and Obstacles of CRISPR-Cas9 Technology in Translational Research

The expanding CRISPR-Cas9 technology is an easily accessible, programmable, and precise gene-editing tool with numerous applications, most notably in biomedical research. Together with advancements in genome and transcriptome sequencing in the era of metadata, genomic engineering with CRISPR-Cas9 meets the developmental requirements of precision medicine, and clinical tests using CRISPR-Cas9 are now possible. This review summarizes developments and established preclinical applications of CRISPR-Cas9 technology, along with its current challenges, and highlights future applications in translational research.

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

CCR5-∆32 is deleterious in the homozygous state in humans

We use the genotyping and death register information of 409,693 individuals of British ancestry to investigate fitness effects of the CCR5-∆32 mutation. We estimate a 21% increase in the all-cause mortality rate in individuals who are homozygous for the ∆32 allele. A deleterious effect of the ∆32/∆32 mutation is also independently supported by a significant deviation from the Hardy-Weinberg equilibrium (HWE) due to a deficiency of ∆32/∆32 individuals at the time of recruitment.

New insight into human sweet taste: a genome-wide association study of the perception and intake of sweet substances

BACKGROUND

Individual differences in human perception of sweetness are partly due to genetics; however, which genes are associated with the perception and the consumption of sweet substances remains unclear.

OBJECTIVE

The aim of this study was to verify previous reported associations within genes involved in the peripheral receptor systems (i.e., TAS1R2, TAS1R3, and GNAT3) and reveal novel loci.

METHODS

We performed genome-wide association scans (GWASs) of the perceived intensity of 2 sugars (glucose and fructose) and 2 high-potency sweeteners (neohesperidin dihydrochalcone and aspartame) in an Australian adolescent twin sample (n = 1757), and the perceived intensity and sweetness and the liking of sucrose in a US adult twin sample (n = 686). We further performed GWASs of the intake of total sugars (i.e., total grams of all dietary mono- and disaccharides per day) and sweets (i.e., handfuls of candies per day) in the UK Biobank sample (n = ≤174,424 white-British individuals). All participants from the 3 independent samples were of European ancestry.

RESULTS

We found a strong association between the intake of total sugars and the single nucleotide polymorphism rs11642841 within the FTO gene on chromosome 16 (P = 3.8 × 10-8) and many suggestive associations (P < 1.0 × 10-5) for each of the sweet perception and intake phenotypes. We showed genetic evidence for the involvement of the brain in both sweet taste perception and sugar intake. There was limited support for the associations with TAS1R2, TAS1R3, and GNAT3 in all 3 European samples.

CONCLUSIONS

Our findings indicate that genes additional to those involved in the peripheral receptor system are also associated with the sweet taste perception and intake of sweet-tasting foods. The functional potency of the genetic variants within TAS1R2, TAS1R3, and GNAT3 may be different between ethnic groups and this warrants further investigations.

Insulin resistance: Genetic associations with depression and cognition in population based cohorts

Insulin resistance, broadly defined as the reduced ability of insulin to exert its biological action, has been associated with depression and cognitive dysfunction in observational studies. However, it is unclear whether these associations are causal and whether they might be underpinned by other shared factors. To address this knowledge gap, we capitalized on the stability of genetic biomarkers through the lifetime, and on their unidirectional relationship with depression and cognition. Specifically, we determined the association between quantitative measures of cognitive function and depression and genetic instruments of insulin resistance traits in two large-scale population samples, the Generation Scotland: Scottish Family Health Study (GS: SFHS; N = 19,994) and in the UK Biobank (N = 331,374). In the GS:SFHS, the polygenic risk score (PRS) for fasting insulin was associated with verbal intelligence and depression while the PRS for the homeostasis model assessment of insulin resistance was associated with verbal intelligence. Despite this overlap in genetic architecture, Mendelian randomization analyses in the GS:SFHS and in the UK Biobank samples did not yield evidence for causal associations from insulin resistance traits to either depression or cognition. These findings may be due to weak genetic instruments, limited cognitive measures and insufficient power but they may also indicate the need to identify other biological mechanisms that may mediate the relationship from insulin resistance to depression and cognition.

Association of maternal circulating 25(OH)D and calcium with birth weight: A mendelian randomisation analysis

BACKGROUND

Systematic reviews of randomised controlled trials (RCTs) have suggested that maternal vitamin D (25[OH]D) and calcium supplementation increase birth weight. However, limitations of many trials were highlighted in the reviews. Our aim was to combine genetic and RCT data to estimate causal effects of these two maternal traits on offspring birth weight.

METHODS AND FINDINGS

We performed two-sample mendelian randomisation (MR) using genetic instrumental variables associated with 25(OH)D and calcium that had been identified in genome-wide association studies (GWAS; sample 1; N = 122,123 for 25[OH]D and N = 61,275 for calcium). Associations between these maternal genetic variants and offspring birth weight were calculated in the UK Biobank (UKB) (sample 2; N = 190,406). We used data on mother-child pairs from two United Kingdom birth cohorts (combined N = 5,223) in sensitivity analyses to check whether results were influenced by fetal genotype, which is correlated with the maternal genotype (r ≈ 0.5). Further sensitivity analyses to test the reliability of the results included MR-Egger, weighted-median estimator, 'leave-one-out', and multivariable MR analyses. We triangulated MR results with those from RCTs, in which we used randomisation to supplementation with vitamin D (24 RCTs, combined N = 5,276) and calcium (6 RCTs, combined N = 543) as an instrumental variable to determine the effects of 25(OH)D and calcium on birth weight. In the main MR analysis, there was no strong evidence of an effect of maternal 25(OH)D on birth weight (difference in mean birth weight -0.03 g [95% CI -2.48 to 2.42 g, p = 0.981] per 10% higher maternal 25[OH]D). The effect estimate was consistent across our MR sensitivity analyses. Instrumental variable analyses applied to RCTs suggested a weak positive causal effect (5.94 g [95% CI 2.15-9.73, p = 0.002] per 10% higher maternal 25[OH]D), but this result may be exaggerated because of risk of bias in the included RCTs. The main MR analysis for maternal calcium also suggested no strong evidence of an effect on birth weight (-20 g [95% CI -44 to 5 g, p = 0.116] per 1 SD higher maternal calcium level). Some sensitivity analyses suggested that the genetic instrument for calcium was associated with birth weight via exposures that are independent of calcium levels (horizontal pleiotropy). Application of instrumental variable analyses to RCTs suggested that calcium has a substantial effect on birth weight (178 g [95% CI 121-236 g, p = 1.43 × 10-9] per 1 SD higher maternal calcium level) that was not consistent with any of the MR results. However, the RCT instrumental variable estimate may have been exaggerated because of risk of bias in the included RCTs. Other study limitations include the low response rate of UK Biobank, which may bias MR estimates, and the lack of suitable data to test whether the effects of genetic instruments on maternal calcium levels during pregnancy were the same as those outside of pregnancy.

CONCLUSIONS

Our results suggest that maternal circulating 25(OH)D does not influence birth weight in otherwise healthy newborns. However, the effect of maternal circulating calcium on birth weight is unclear and requires further exploration with more research including RCT and/or MR analyses with more valid instruments.

Genome sequencing for rightward hemispheric language dominance

Most people have left-hemisphere dominance for various aspects of language processing, but only roughly 1% of the adult population has atypically reversed, rightward hemispheric language dominance (RHLD). The genetic-developmental program that underlies leftward language laterality is unknown, as are the causes of atypical variation. We performed an exploratory whole-genome-sequencing study, with the hypothesis that strongly penetrant, rare genetic mutations might sometimes be involved in RHLD. This was by analogy with situs inversus of the visceral organs (left-right mirror reversal of the heart, lungs and so on), which is sometimes due to monogenic mutations. The genomes of 33 subjects with RHLD were sequenced and analyzed with reference to large population-genetic data sets, as well as 34 subjects (14 left-handed) with typical language laterality. The sample was powered to detect rare, highly penetrant, monogenic effects if they would be present in at least 10 of the 33 RHLD cases and no controls, but no individual genes had mutations in more than five RHLD cases while being un-mutated in controls. A hypothesis derived from invertebrate mechanisms of left-right axis formation led to the detection of an increased mutation load, in RHLD subjects, within genes involved with the actin cytoskeleton. The latter finding offers a first, tentative insight into molecular genetic influences on hemispheric language dominance.

Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans

Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the genotype and dietary saturated fat intake on body mass index (BMI) in the UK Biobank. We discover 14 novel GxE variants in 12 lipid-responsive promoters, including well-known lipid genes (LIPE, CARM1, and PLIN2) and novel genes, such as LDB3, for which we provide further functional and integrative genomics evidence. We further identify 24 GxE variants in enhancers, totaling 38 new GxE variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can discover new functional GxE mechanisms in humans.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND

Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

METHODS

We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

RESULTS

Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

CONCLUSIONS

Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

ukbtools: An R package to manage and query UK Biobank data

INTRODUCTION

The UK Biobank (UKB) is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. However, several obstacles limit immediate analysis of the data: data files vary in format, may be very large, and have numerical codes for column names.

RESULTS

ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis. All associated data files are merged into a single dataset with descriptive column names. The package also provides tools to assist in quality control by exploring the primary demographics of subsets of participants; query of disease diagnoses for one or more individuals, and estimating disease frequency relative to a reference variable; and to retrieve genetic metadata.

CONCLUSION

Having a dataset with meaningful variable names, a set of UKB-specific exploratory data analysis tools, disease query functions, and a set of helper functions to explore and write genetic metadata to file, will rapidly enable UKB users to undertake their research.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Societal Implications of the Internet of Pathogens

The growth of pathogen genomics shows no signs of abating. Whole-genome sequencing of clinical viral and bacterial isolates continues to grow in nearly exponential bounds. Reductions in cost driven by new technology have created a seamless environment for generating, sharing, and analyzing pathogen genomes. The high-resolution view of infectious disease transmission dynamics offered by analyzing whole genomes from pathogens, coupled with the genomicist ethic of widespread data sharing, has created a veritable Internet of pathogens, which inadvertently produces new threats to patient privacy and protected heath information. The health care system, and society more generally, have yet to explore the far-reaching privacy concerns raised by readily accessible pathogen genomic data. The recent use of human genomic databases, the existence of freely available alternative data and metadata sources, and lax regulation of collecting publicly available genomes to identify individuals in a criminal context raise concerning parallels about what is possible with pathogen genomics. The growing ability to ascertain culpability for infectious disease transmission at a nearly individual level could change our perspective on disease outbreaks from one based on public health to one based on individual liability. These technological breakthroughs in the absence of an understanding of potential privacy and liability issues lead to questions about the dominant paradigm of better living through pathogen genomics.

Homozygosity for TYK2 P1104A underlies tuberculosis in about 1% of patients in a cohort of European ancestry

The human genetic basis of tuberculosis (TB) has long remained elusive. We recently reported a high level of enrichment in homozygosity for the common TYK2 P1104A variant in a heterogeneous cohort of patients with TB from non-European countries in which TB is endemic. This variant is homozygous in ∼1/600 Europeans and ∼1/5,000 people from other countries outside East Asia and sub-Saharan Africa. We report a study of this variant in the UK Biobank cohort. The frequency of P1104A homozygotes was much higher in patients with TB (6/620, 1%) than in controls (228/114,473, 0.2%), with an odds ratio (OR) adjusted for ancestry of 5.0 [95% confidence interval (CI): 1.96-10.31, P = 2 × 10-3]. Conversely, we did not observe enrichment for P1104A heterozygosity, or for TYK2 I684S or V362F homozygosity or heterozygosity. Moreover, it is unlikely that more than 10% of controls were infected with Mycobacterium tuberculosis, as 97% were of European genetic ancestry, born between 1939 and 1970, and resided in the United Kingdom. Had all of them been infected, the OR for developing TB upon infection would be higher. These findings suggest that homozygosity for TYK2 P1104A may account for ∼1% of TB cases in Europeans.

A test for deviations from expected genotype frequencies on the X chromosome for sex-biased admixed populations

Genome-wide scans for deviations from expected genotype frequencies, as determined by the Hardy-Weinberg equilibrium (HWE), are commonly applied to detect genotyping errors and deviations from random mating. In contrast to the autosomes, genotype frequencies on the X chromosome do not reach HWE within a single generation. Instead, if allele frequencies in males and females initially differ, they oscillate for a few generations toward equilibrium. Allele frequency differences between the sexes are expected in populations that have experienced recent sex-biased admixture, namely, their male and female founders differed in ancestry. Sex-biased admixture does not allow testing for HWE on X, because deviations are naturally expected, even under random mating (post admixture) and error-free genotyping. In this paper, we develop a likelihood ratio test and a χ² test to detect deviations from expected genotype frequencies on X, beyond natural deviations due to sex-biased admixture. We demonstrate by simulations that our tests are powerful for detecting deviations due to non-random mating, while at the same time they do not reject the null under historical sex-biased admixture and random mating thereafter. We also demonstrate that when applied to 1000 Genomes project populations, our likelihood ratio test rejects fewer SNPs than other tests, but we describe limitations in the interpretation of the results.

The Genetics of Human Skin and Hair Pigmentation

Human skin and hair color are visible traits that can vary dramatically within and across ethnic populations. The genetic makeup of these traits-including polymorphisms in the enzymes and signaling proteins involved in melanogenesis, and the vital role of ion transport mechanisms operating during the maturation and distribution of the melanosome-has provided new insights into the regulation of pigmentation. A large number of novel loci involved in the process have been recently discovered through four large-scale genome-wide association studies in Europeans, two large genetic studies of skin color in Africans, one study in Latin Americans, and functional testing in animal models. The responsible polymorphisms within these pigmentation genes appear at different population frequencies, can be used as ancestry-informative markers, and provide insight into the evolutionary selective forces that have acted to create this human diversity.

Association between birth weight and refractive error in adulthood: a Mendelian randomisation study

BACKGROUND

Pathological myopia is one of the leading causes of blindness globally. Lower birth weight (BW) within the normal range has been reported to increase the risk of myopia, although findings conflict. We sought to estimate the causal effect of BW on refractive error using Mendelian randomisation (MR), under the assumption of a linear relationship.

METHODS

Genetic variants associated with BW were identified from meta-analysis of a genome-wide association study (GWAS) for self-reported BW in 162 039 UK Biobank participants and a published Early Growth Genetics (EGG) consortium GWAS (n=26 836). We performed a one-sample MR analysis in 39 658 unrelated, adult UK Biobank participants (independent of the GWAS sample) using an allele score for BW as instrumental variable. A two-sample MR sensitivity analysis and conventional ordinary least squares (OLS) regression analyses were also undertaken.

RESULTS

In OLS analysis, BW showed a small, positive association with refractive error: +0.04 D per SD increase in BW (95% CI 0.02 to 0.07; p=0.002). The one-sample MR-estimated causal effect of BW on refractive error was higher, at +0.28 D per SD increase in BW (95% CI 0.05 to 0.52, p=0.02). A two-sample MR analysis provided similar causal effect estimates, with minimal evidence of directional pleiotropy.

CONCLUSIONS

Our study suggests lower BW within the normal range is causally associated with a more myopic refractive error. However, the impact of the causal effect was modest (range 1.00 D covering approximately 95% of the population).

Association analyses identify 31 new risk loci for colorectal cancer susceptibility

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention.

Integrative analysis of Dupuytren's disease identifies novel risk locus and reveals a shared genetic etiology with BMI

Dupuytren's disease is a common inherited tissue‐specific fibrotic disorder, characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand. Although genome‐wide association study (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (3,871 cases and 4,686 controls) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome‐wide association study. We identify 43 tissue‐specific gene associations with Dupuytren's risk, including one in a novel risk region. We also estimate the genome‐wide genetic correlation between Dupuytren's disease and 45 complex traits and find significant genetic correlations between Dupuytren's disease and body mass index (BMI), type II diabetes, triglycerides, and high‐density lipoprotein (HDL), suggesting a shared genetic etiology between these traits. We further examine local genetic correlation to identify 8 and 3 novel regions significantly correlated with BMI and HDL respectively. Our results are consistent with previous epidemiological findings showing that lower BMI increases risk for Dupuytren's disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytren's disease and serve as a starting point for functional validation.

Quantile regression analysis reveals widespread evidence for gene-environment or gene-gene interactions in myopia development

A genetic contribution to refractive error has been confirmed by the discovery of more than 150 associated variants in genome-wide association studies (GWAS). Environmental factors such as education and time outdoors also demonstrate strong associations. Currently however, the extent of gene-environment or gene-gene interactions in myopia is unknown. We tested the hypothesis that refractive error-associated variants exhibit effect size heterogeneity, a hallmark feature of genetic interactions. Of 146 variants tested, evidence of non-uniform, non-linear effects were observed for 66 (45%) at Bonferroni-corrected significance (P < 1.1 × 10-4) and 128 (88%) at nominal significance (P < 0.05). LAMA2 variant rs12193446, for example, had an effect size varying from -0.20 diopters (95% CI -0.18 to -0.23) to -0.89 diopters (95% CI -0.71 to -1.07) in different individuals. SNP effects were strongest at the phenotype extremes and weaker in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in myopia are pervasive.

GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures

Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 - 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10-42, β = -0.090) and confers risk of hip fracture (P = 1.0 × 10-8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density.

[Human skin pigmentation: more surprises]

Careful GWAS analysis of a group of mixed-ancestry Latino-American individuals reveals the role of a new "light" variant of the MSFD12 gene which appears to originate in some of the Asian populations that migrated into the Americas 15,000 years ago. This indicates convergent evolution of sparsely pigmented skin at high latitudes, and highlights the interest of performing GWAS studies in non-European populations.

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND

Despite 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. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.

METHODS

Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).

RESULTS

Positive genetic correlation was observed between MD and AD (rgMD-AD = + 0.47, P = 6.6 × 10-10). AC-quantity showed positive genetic correlation with both AD (rgAD-AC quantity = + 0.75, P = 1.8 × 10-14) and MD (rgMD-AC quantity = + 0.14, P = 2.9 × 10-7), while there was negative correlation of AC-frequency with MD (rgMD-AC frequency = -0.17, P = 1.5 × 10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10-6). There was no evidence for reverse causation.

CONCLUSION

This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies

BACKGROUND

Childhood-onset and adult-onset asthma differ with respect to severity and comorbidities. Whether they also differ with respect to genetic risk factors has not been previously investigated in large samples. The goals of this study were to identify shared and distinct genetic risk loci for childhood-onset and adult-onset asthma, and to identify the genes that might mediate the effects of associated variation.

METHODS

We did genome-wide and transcriptome-wide studies, using data from the UK Biobank, in individuals with asthma, including adults with childhood-onset asthma (onset before 12 years of age), adults with adult-onset asthma (onset between 26 and 65 years of age), and adults without asthma (controls; aged older than 38 years). We did genome-wide association studies (GWAS) for childhood-onset asthma and adult-onset asthma each compared with shared controls, and for age of asthma onset in all asthma cases, with a genome-wide significance threshold of p<5 × 10^-8. Enrichment studies determined the tissues in which genes at GWAS loci were most highly expressed, and PrediXcan, a transcriptome-wide gene-based test, was used to identify candidate risk genes.

FINDINGS

Of 376 358 British white individuals from the UK Biobank, we included 37 846 with self-reports of doctor-diagnosed asthma: 9433 adults with childhood-onset asthma; 21 564 adults with adult-onset asthma; and an additional 6849 young adults with asthma with onset between 12 and 25 years of age. For the first and second GWAS analyses, 318 237 individuals older than 38 years without asthma were used as controls. We detected 61 independent asthma loci: 23 were childhood-onset specific, one was adult-onset specific, and 37 were shared. 19 loci were associated with age of asthma onset. The most significant asthma-associated locus was at 17q12 (odds ratio 1·406, 95% CI 1·365-1·448; p=1·45 × 10^-111) in the childhood-onset GWAS. Genes at the childhood onset-specific loci were most highly expressed in skin, blood, and small intestine; genes at the adult onset-specific loci were most highly expressed in lung, blood, small intestine, and spleen. PrediXcan identified 113 unique candidate genes at 22 of the 61 GWAS loci. Single-nucleotide polymorphism-based heritability estimates were more than three times larger for childhood-onset asthma (0·327) than for adult-onset disease (0·098). The onset of disease in childhood was associated with additional genes with relatively large effect sizes, with the largest odds ratio observed at the FLG locus at 1q21.3 (1·970, 95% CI 1·823-2·129).

INTERPRETATION

Genetic risk factors for adult-onset asthma are largely a subset of the genetic risk for childhood-onset asthma but with overall smaller effects, suggesting a greater role for non-genetic risk factors in adult-onset asthma. Combined with gene expression and tissue enrichment patterns, we suggest that the establishment of disease in children is driven more by dysregulated allergy and epithelial barrier function genes, whereas the cause of adult-onset asthma is more lung-centred and environmentally determined, but with immune-mediated mechanisms driving disease progression in both children and adults.

FUNDING

US National Institutes of Health.

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

Genome-wide association study of medication-use and associated disease in the UK Biobank

Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria (P < 10⁻⁸/23) for statistical significance are identified. We investigate the implications of these GWAS findings in relation to biological mechanism, potential drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes are 16 known therapeutic-effect target genes for medications from 9 categories. Two of the medication classes studied are for disorders that have not previously been subject to large GWAS (hypothyroidism and gastro-oesophageal reflux disease).

An understanding of the genetic variants associated with medication use may shed light on the underlying biological pathways of disease, and aid in drug development. Here, Wu and colleagues conduct a GWAS for self-reported medication-use in the UK Biobank, finding more than 500 independent variants and many promising leads for future work.

On the differences between mega- and meta-imputation and analysis exemplified on the genetics of age-related macular degeneration

While current genome‐wide association analyses often rely on meta‐analysis of study‐specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega‐imputation and mega‐analysis) or study‐specifically (meta‐imputation and meta‐analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age‐related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis.

From 27,448,454 genetic variants after 1,000‐Genomes‐based imputation, mega‐imputation yielded ~400,000 more variants with high imputation quality (mostly rare variants) compared to meta‐imputation. For AMD signal detection (P < 5 × 10⁻⁸) in mega‐imputed data, most loci were detected with mega‐analysis without adjusting for study membership (40 loci, including 34 known); we considered these loci genuine, since genetic effects and P‐values were comparable across analyses. In meta‐imputed data, we found 31 additional signals, mostly near chromosome tails or reference panel gaps, which disappeared after accounting for interaction of whole‐genome amplification (WGA) with study membership or after excluding studies with WGA‐participants.

For signal detection with multistudy IPD, we recommend mega‐imputation and mega‐analysis, with meta‐imputation followed by meta‐analysis being a computationally appealing alternative.

Body composition and atrial fibrillation: a Mendelian randomization study

AIMS

Increases in fat-free mass and fat mass have been associated with higher risk of atrial fibrillation (AF) in observational studies. It is not known whether these associations reflect independent causal processes. Our aim was to evaluate independent causal roles of fat-free mass and fat mass on AF.

METHODS AND RESULTS

We conducted a large observational study to estimate the associations between fat-free mass and fat mass on incident AF in the UK Biobank (N = 487 404, N events = 10 365). Genome-wide association analysis was performed to obtain genetic instruments for Mendelian randomization (MR). We evaluated the causal effects of fat-free mass and fat mass on AF with two-sample method by using genetic associations from AFGen consortium as outcome. Finally, we evaluated independent causal effects of fat-free mass and fat mass with multivariate MR. Both fat-free mass and fat mass had observational associations with incident AF [hazard ratio (HR) = 1.77, 95% confidence interval (CI) 1.72-1.83; HR = 1.40, 95% CI 1.37-1.43 per standard deviation increase in fat-free and fat mass, respectively]. The causal effects using the inverse-variance weighted method were 1.55 (95% CI 1.38-1.75) for fat-free mass and 1.30 (95% CI 1.17-1.45) for fat mass. Weighted median, Egger regression, and penalized methods showed similar estimates. The multivariate MR analysis suggested that the causal effects of fat-free and fat mass were independent of each other (causal risk ratios: 1.37, 95% CI 1.06-1.75; 1.28, 95% CI 1.03-1.58).

CONCLUSION

Genetically programmed increases in fat-free mass and fat mass independently cause an increased risk of AF.

Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity

The melanocortin 4 receptor (MC4R) is a G protein-coupled receptor whose disruption causes obesity. We functionally characterized 61 MC4R variants identified in 0.5 million people from UK Biobank and examined their associations with body mass index (BMI) and obesity-related cardiometabolic diseases. We found that the maximal efficacy of β-arrestin recruitment to MC4R, rather than canonical Gαs-mediated cyclic adenosine-monophosphate production, explained 88% of the variance in the association of MC4R variants with BMI. While most MC4R variants caused loss of function, a subset caused gain of function; these variants were associated with significantly lower BMI and lower odds of obesity, type 2 diabetes, and coronary artery disease. Protective associations were driven by MC4R variants exhibiting signaling bias toward β-arrestin recruitment and increased mitogen-activated protein kinase pathway activation. Harnessing β-arrestin-biased MC4R signaling may represent an effective strategy for weight loss and the treatment of obesity-related cardiometabolic diseases.

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.

Polygenic prediction via Bayesian regression and continuous shrinkage priors

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer

Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.

The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1-13%, or r = 0.1-0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure-function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas.