A Robust Example of Collider Bias in a Genetic Association Study

Phenotypic and Genetic Associations Between Preschool Fine Motor Skills and Later Neurodevelopment, Psychopathology, and Educational Achievement

BACKGROUND

Fine motor skills are heritable and comprise important milestones in development, and some evidence suggests that impairments in fine motor skills are associated with neurodevelopmental conditions, psychiatric disorders, and poor educational outcomes.

METHODS

In a preregistered study of 9625 preschool children from TEDS (Twins Early Development Study), fine motor assessments (drawing, block building, folding, and questionnaires) were conducted at 2, 3, and 4 years of age. A cross-age fine motor score was derived using principal component analysis. Multivariate regression analysis was used to examine the relationships between the fine motor score and neurodevelopmental traits, psychopathology, and educational outcomes at 3 later ages (7-8, 12, and 16 years) and cross-age psychopathology composite scores. Polygenic scores (PGSs) were created for attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, anxiety, major depressive disorder, obsessive-compulsive disorder, and years of education. We ran single-PGS models and a multi-PGS model.

RESULTS

Fine motor skills were negatively associated with neurodevelopmental traits and psychopathology across childhood and adolescence and positively associated with educational achievement in adolescence (β = 0.25, p < .001). Superior fine motor skills were associated with a higher years-of-education PGS (β = 0.07, p < .001), a lower ADHD PGS (β = -0.04, p = .011), and a higher anxiety PGS (β = 0.03, p = .040). Similarly, the multi-PGS model retained the PGSs for years of education (β = 0.07), ADHD (β = -0.03), and anxiety (β = 0.01). A non-preregistered analysis in an independent preschool sample replicated the ADHD PGS association, but not the years of education or anxiety PGS associations.

CONCLUSIONS

Fine motor skills are linked genetically and phenotypically to later neurodevelopment, psychopathology, and educational outcomes. Future work should investigate the mechanisms that underlie the role of fine motor development in later outcomes.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Relationships between obesity and functional outcome after ischemic stroke: a Mendelian randomization study

BACKGROUND AND OBJECTIVES

Most previous studies suggested obesity deteriorates the functional outcome after ischemic stroke. But there are researches claiming that obesity is associated with lower mortality, recurrence, and readmission rates, which is known as the obesity paradox. Our current research aimed to investigate the correlation between genetically obesity and the post-stroke outcome with the Mendelian randomization (MR) method.

METHODS

The UK Biobank and the GIANT consortium provided instrumental variables for body mass index (BMI, 806,834 individuals) and waist-to-hip ratio (WHR, 697,734 individuals). Data of functional outcome after ischemic stroke were obtained from the Genetics of Ischemic Stroke Functional Outcome network (6012 individuals). Inverse-variance weighted approach was utilized as the primary analyses. Sensitivity analyses involved the utilization of different MR methods. The heterogeneity among genetic variants was assessed by I2 and Q value statistics.

RESULTS

In univariable analysis, there was a significant connection between genetic susceptibility to WHR and worse functional outcome (modified Rankin Scale 3) after ischemic stroke (OR [95%CI] = 1.47 [1.07, 2.02], P = 0.016). Genetic liability to BMI and was not associated with post-stroke functional outcome (all P > 0.05). The overall patterns between genetic liability to WHR and functional outcome post-ischemic outcome no longer existed in the multivariable MR analysis after adjusting for BMI (OR [95%CI] = 1.26[0.76,1.67], P = 0.56).

CONCLUSION

The current MR study provided evidence that WHR was correlated to unfavorable outcome post-ischemic stroke. Exploring interventions against obesity may potentially improve recovery after ischemic stroke.

Genetic correlations of alcohol consumption and alcohol use disorder with sex hormone levels in females and males

Background

Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins.

Methods

To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin.

Results

For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations.

Conclusions

Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.

Identification of Clinical Drivers of Left Atrial Enlargement Through Genomics of Left Atrial Size

BACKGROUND

Greater left atrial size is associated with a higher incidence of cardiovascular disease and mortality, but the full spectrum of diagnoses associated with left atrial enlargement in sex-stratified clinical populations is not well known. Our study sought to identify genetic risk mechanisms affecting left atrial diameter (LAD) in a clinical cohort.

METHODS

Using Vanderbilt deidentified electronic health record, we studied 6163 females and 5993 males of European ancestry who had at least 1 LAD measure and available genotyping. A sex-stratified polygenic score was constructed for LAD variation and tested for association against 1680 International Classification of Diseases code-based phenotypes. Two-sample univariable and multivariable Mendelian randomization approaches were used to assess etiologic relationships between candidate associations and LAD.

RESULTS

A phenome-wide association study identified 25 International Classification of Diseases code-based diagnoses in females and 11 in males associated with a polygenic score of LAD (false discovery rate q<0.01), 5 of which were further evaluated by Mendelian randomization (waist circumference [WC], atrial fibrillation, heart failure, systolic blood pressure, and coronary artery disease). Sex-stratified differences in the genetic associations between risk factors and a polygenic score for LAD were observed (WC for females; heart failure, systolic blood pressure, atrial fibrillation, and WC for males). By multivariable Mendelian randomization, higher WC remained significantly associated with larger LAD in females, whereas coronary artery disease, WC, and atrial fibrillation remained significantly associated with larger LAD in males.

CONCLUSIONS

In a clinical population, we identified, by genomic approaches, potential etiologic risk factors for larger LAD. Further studies are needed to confirm the extent to which these risk factors may be modified to prevent or reverse adverse left atrial remodeling and the extent to which sex modifies these risk factors.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Assessing the Casual Association between Sex Hormone Levels and Fracture Risk: A Two-Sample Mendelian Randomization Study

OBJECTIVE

Prior observational studies have reported that levels of sex hormones constitute a risk factor for the fracture. The aim of this study was to ascertain whether there is a causal relationship between the levels of sex hormones and the risk of fracture through Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms (SNPs) associated with two indicators of sex hormone levels, circulating sex hormone-binding globulin (SHBG) and bioavailable testosterone levels, as exposures were selected from a large genome-wide association study (GWAS) from UK Biobank. The summary statistics for 11 different types of fracture as outcomes from the FinnGen consortium. This study employed the two-sample MR approach. For the main analysis, the inverse-variance-weighted (IVW) method was utilized. To assess the heterogeneity of MR results, the IVW method and MR-Egger method were utilized. To evaluate potential pleiotropy, MR-Egger regression was conducted. Additionally, a leave-one-SNP-out test was performed to assess the robustness of MR results to the exclusion of any individual SNP.

RESULTS

The MR analyses demonstrated a conspicuous impact of SHBG on the risk of pathological fracture with osteoporosis (OP). We found that an increase of one standard deviation (SD) in SHBG correspondingly increased the risk of pathological fracture with OP [odds ratio (OR) 2.42, 95% confidence interval (CI), 1.52-3.85; p = 1.93 × 10-4 ]. The bioavailable testosterone showed the negative casual genetic associations with fractures of foot and forearm. An increase of one SD in the genetically predetermined bioavailable testosterone was associated with a reduction of 37% in the risk of fracture of foot (OR 0.63, 95% Cl 0.49 to 0.81; p = 3.37 × 10-4 ), as well as a 39% decrease in the risk of fracture of forearm (OR 0.61, 95% Cl 0.50 to 0.76; p = 5.40 × 10-6 ).

CONCLUSIONS

Our study confirms that individuals experiencing elevated SHBG concentrations showed a major causal effect on pathological fracture with OP. High bioavailable testosterone levels play an important role in preventing the fractures of foot and forearm. Although increasing bioavailable testosterone and decreasing SHBG levels had no casual effect on most fractures in the general population, they are likely to have the most clinically relevant effect on certain fracture risk reduction.

ADuLT: An efficient and robust time-to-event GWAS

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

Phenotypic and genetic factors associated with donation of DNA and consent to record linkage for prescription history in the Australian Genetics of Depression Study

Samples can be prone to ascertainment and attrition biases. The Australian Genetics of Depression Study is a large publicly recruited cohort (n = 20,689) established to increase the understanding of depression and antidepressant treatment response. This study investigates differences between participants who donated a saliva sample or agreed to linkage of their records compared to those who did not. We observed that older, male participants with higher education were more likely to donate a saliva sample. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder, and social anxiety disorder were associated with lower odds of donating a saliva sample, whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia. Overall, our results show moderate differences among these subsamples. Most current epidemiological studies do not search for attrition biases at the genetic level. The possibility to do so is a strength of samples such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.

The genetic architecture and evolution of the human skeletal form

The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions. Polygenic score analysis identified specific associations between osteoarthritis and hip and knee SPs. In contrast to other traits, SP loci were enriched in human accelerated regions and in regulatory elements of genes that are differentially expressed between humans and great apes. Combined, our work identifies specific genetic variants that affect the skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

Functional genomic effects of indels using Bayesian genome-phenome wide association studies in sorghum

High-throughput genomic and phenomic data have enhanced the ability to detect genotype-to-phenotype associations that can resolve broad pleiotropic effects of mutations on plant phenotypes. As the scale of genotyping and phenotyping has advanced, rigorous methodologies have been developed to accommodate larger datasets and maintain statistical precision. However, determining the functional effects of associated genes/loci is expensive and limited due to the complexity associated with cloning and subsequent characterization. Here, we utilized phenomic imputation of a multi-year, multi-environment dataset using PHENIX which imputes missing data using kinship and correlated traits, and we screened insertions and deletions (InDels) from the recently whole-genome sequenced Sorghum Association Panel for putative loss-of-function effects. Candidate loci from genome-wide association results were screened for potential loss of function using a Bayesian Genome-Phenome Wide Association Study (BGPWAS) model across both functionally characterized and uncharacterized loci. Our approach is designed to facilitate in silico validation of associations beyond traditional candidate gene and literature-search approaches and to facilitate the identification of putative variants for functional analysis and reduce the incidence of false-positive candidates in current functional validation methods. Using this Bayesian GPWAS model, we identified associations for previously characterized genes with known loss-of-function alleles, specific genes falling within known quantitative trait loci, and genes without any previous genome-wide associations while additionally detecting putative pleiotropic effects. In particular, we were able to identify the major tannin haplotypes at the Tan1 locus and effects of InDels on the protein folding. Depending on the haplotype present, heterodimer formation with Tan2 was significantly affected. We also identified major effect InDels in Dw2 and Ma1, where proteins were truncated due to frameshift mutations that resulted in early stop codons. These truncated proteins also lost most of their functional domains, suggesting that these indels likely result in loss of function. Here, we show that the Bayesian GPWAS model is able to identify loss-of-function alleles that can have significant effects upon protein structure and folding as well as multimer formation. Our approach to characterize loss-of-function mutations and their functional repercussions will facilitate precision genomics and breeding by identifying key targets for gene editing and trait integration.

Obesity and risk of gestational diabetes mellitus: A two-sample Mendelian randomization study

AIMS

To estimate genetically predicted causal associations of general and central obesity with GDM, and to determine the mediating role of circulating lipids.

METHODS

Summary-level data was obtained from the largest available genome-wide association studies of five obesity traits, five lipid traits and GDM. Two-sample univariate Mendelian randomization (MR), multivariate MR, and MR-based mediation analysis was applied to determine the total effect, direct effect and the mediating effect, respectively.

RESULTS

Univariate MR showed that the odds of GDM increased per 1-SD increase in body mass index (BMI) (OR = 1.64, P = 5.05 × 10^-17), waist-to-hip ratio (WHR) (OR = 1.57, P = 2.27 × 10^-14) and WHR adjusted for BMI (OR = 1.42, P = 6.11 × 10^-15). The heterogeneous associations of waist circumference (OR = 1.64, P = 5.57 × 10^-14) and hip circumference (OR = 1.20, P = 0.002) on GDM further reflected that body fat distribution could influence GDM risk. Mediation analysis suggested that triglycerides, high-density lipoprotein-cholesterol and apolipoprotein A-I each mediated between 5% and 10% of the association between obesity and GDM.

CONCLUSION

Our findings supported a deleterious causal effect of obesity on GDM risk, where lipid metabolism acted as potential drivers of the relationships between both general and central obesity and GDM.

The genetic architecture of the human skeletal form

The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood. Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height. All skeletal proportions are highly heritable (∼40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions. We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes. Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score

BACKGROUND

Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes.

OBJECTIVE

The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies.

METHODS

We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions.

RESULTS

Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks.

CONCLUSIONS

Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

Genetic liability to obesity and peptic ulcer disease: a Mendelian randomization study

BACKGROUND

Epidemiological evidence relating obesity to peptic ulcer disease (PUD) has been mixed. Here we sought to determine the causality in the association of obesity with PUD risk using the Mendelian randomization (MR) approach.

METHODS

This study was based on summary-level data for body mass index (BMI), waist-to-hip ratio (WHR), and PUD derived from large genome-wide association studies (GWASs). Single nucleotide polymorphisms significantly associated with BMI and WHR (P < 5 × 10^-8) were leveraged as instrumental variables. Causal estimates were pooled using several meta-analysis methods. In addition, multivariable MR was employed to account for covariation between BMI and WHR, as well as to explore potential mediators.

RESULTS

Genetically predicted higher BMI has a causal effect on PUD, with an OR of 1.34 per SD increase in BMI (~ 4.8 kg/m²) (P = 9.72 × 10^-16). Likewise, there was a 35% higher risk of PUD (P = 2.35 × 10^-10) for each SD increase in WHR (0.09 ratio). Complementary analyses returned consistent results. Multivariable MR demonstrated that adjustment for WHR largely attenuated the BMI-PUD association. However, the causal association of WHR with PUD risk survived adjustment for BMI. Both the associations remained robust upon adjustment for several traditional risk factors. Replication analyses using different instrumental variants further strengthened the causal inference. Besides, we found no evidence for the causal association in the reverse analyses from PUD to BMI/WHR.

CONCLUSIONS

This MR study revealed that obesity (notably abdominal obesity) is causally associated with higher PUD risk. Programs aimed at weight loss may represent therapeutic opportunities for PUD.

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank-Brief Report

BACKGROUND

The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date.

METHODS

We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively.

RESULTS

Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings.

CONCLUSIONS

Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY.

Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes

Body fat distribution is a major, heritable risk factor for cardiometabolic disease, independent of overall adiposity. Using exome-sequencing in 618,375 individuals (including 160,058 non-Europeans) from the UK, Sweden and Mexico, we identify 16 genes associated with fat distribution at exome-wide significance. We show 6-fold larger effect for fat-distribution associated rare coding variants compared with fine-mapped common alleles, enrichment for genes expressed in adipose tissue and causal genes for partial lipodystrophies, and evidence of sex-dimorphism. We describe an association with favorable fat distribution (p = 1.8 × 10^-09), favorable metabolic profile and protection from type 2 diabetes (~28% lower odds; p = 0.004) for heterozygous protein-truncating mutations in INHBE, which encodes a circulating growth factor of the activin family, highly and specifically expressed in hepatocytes. Our results suggest that inhibin βE is a liver-expressed negative regulator of adipose storage whose blockade may be beneficial in fat distribution-associated metabolic disease.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

A theory-based practical solution to correct for sex-differential participation bias

Most genomic cohorts are retrospective where the exposures and outcomes are predetermined prior to sample collection. Therefore, a spurious association between an exposure and an outcome can arise if both variables affect study participation. Such concerns were raised in previous studies questioning the representativeness of the UK Biobank. Recently, a genome-wide association study (GWAS) on biological sex found many autosomal hits and non-negligible autosomal heritability which the authors attribute to selection bias. In this study, we propose a simple and a practical method that can overcome sex-driven selection bias based on theoretical analysis and simulations.

Genetically Determined Reproductive Aging and Coronary Heart Disease: A Bidirectional 2-sample Mendelian Randomization

BACKGROUND

Accelerated reproductive aging, in women indicated by early natural menopause, is associated with increased coronary heart disease (CHD) risk in observational studies. Conversely, an adverse CHD risk profile has been suggested to accelerate menopause.

OBJECTIVES

To study the direction and evidence for causality of the relationship between reproductive aging and (non-)fatal CHD and CHD risk factors in a bidirectional Mendelian randomization (MR) approach, using age at natural menopause (ANM) genetic variants as a measure for genetically determined reproductive aging in women. We also studied the association of these variants with CHD risk (factors) in men.

DESIGN

Two-sample MR, using both cohort data as well as summary statistics, with 4 methods: simple and weighted median-based, standard inverse-variance weighted (IVW) regression, and MR-Egger regression.

PARTICIPANTS

Data from EPIC-CVD and summary statistics from UK Biobank and publicly available genome-wide association studies were pooled for the different analyses.

MAIN OUTCOME MEASURES

CHD, CHD risk factors, and ANM.

RESULTS

Across different methods of MR, no association was found between genetically determined reproductive aging and CHD risk in women (relative risk estimateIVW = 0.99; 95% confidence interval (CI), 0.97-1.01), or any of the CHD risk factors. Similarly, no associations were found in men. Neither did the reversed analyses show evidence for an association between CHD (risk factors) and reproductive aging.

CONCLUSION

Genetically determined reproductive aging is not causally associated with CHD risk (factors) in women, nor were the genetic variants associated in men. We found no evidence for a reverse association in a combined sample of women and men.

Genetic analysis of over half a million people characterises C-reactive protein loci

Chronic low-grade inflammation is linked to a multitude of chronic diseases. We report the largest genome-wide association study (GWAS) on C-reactive protein (CRP), a marker of systemic inflammation, in UK Biobank participants (N = 427,367, European descent) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (total N = 575,531 European descent). We identify 266 independent loci, of which 211 are not previously reported. Gene-set analysis highlighted 42 gene sets associated with CRP levels (p ≤ 3.2 ×10-6) and tissue expression analysis indicated a strong association of CRP related genes with liver and whole blood gene expression. Phenome-wide association study identified 27 clinical outcomes associated with genetically determined CRP and subsequent Mendelian randomisation analyses supported a causal association with schizophrenia, chronic airway obstruction and prostate cancer. Our findings identified genetic loci and functional properties of chronic low-grade inflammation and provided evidence for causal associations with a range of diseases.

Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer's disease CSF profile of neuronal injury and inflammation

Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development.

Circulating inflammatory cytokines and risk of five cancers: a Mendelian randomization analysis

BACKGROUND

Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis.

METHODS

Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer).

RESULTS

There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses.

CONCLUSIONS

Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.

Genome-wide association studies for Alzheimer's disease: bigger is not always better

As the size of genome-wide association studies increase, the number of associated trait loci identified inevitably increase. One welcomes this if it allows the better delineation of the pathways to disease and increases the accuracy of genetic prediction of disease risk through polygenic risk score analysis. However, there are several problems in the continuing increase in the genome-wide analysis of 'Alzheimer's disease'. In this review, we have systematically assessed the history of Alzheimer's disease genome-wide association studies, including their sample sizes, age and selection/assessment criteria of cases and controls and heritability explained by these disease genome-wide association studies. We observe that nearly all earlier disease genome-wide association studies are now part of all current disease genome-wide association studies. In addition, the latest disease genome-wide association studies include (i) only a small fraction (∼10%) of clinically screened controls, substituting for them population-based samples which are systematically younger than cases, and (ii) around 50% of Alzheimer's disease cases are in fact 'proxy dementia cases'. As a consequence, the more genes the field finds, the less the heritability they explain. We highlight potential caveats this situation creates and discuss some of the consequences occurring when translating the newest Alzheimer's disease genome-wide association study results into basic research and/or clinical practice.

Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour

Age at first sexual intercourse and age at first birth have implications for health and evolutionary fitness. In this genome-wide association study (age at first sexual intercourse, N = 387,338; age at first birth, N = 542,901), we identify 371 single-nucleotide polymorphisms, 11 sex-specific, with a 5-6% polygenic score prediction. Heritability of age at first birth shifted from 9% [CI = 4-14%] for women born in 1940 to 22% [CI = 19-25%] for those born in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility and spermatid differentiation. Our findings suggest that polycystic ovarian syndrome may lead to later age at first birth, linking with infertility. Late age at first birth is associated with parental longevity and reduced incidence of type 2 diabetes and cardiovascular disease. Higher childhood socioeconomic circumstances and those in the highest polygenic score decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, understanding longevity and guiding experimentation into mechanisms of infertility.

Body mass index and subfertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study

STUDY QUESTION

What is the association between BMI and subfertility?

SUMMARY ANSWER

We observed a J-shaped relationship between BMI and subfertility in both sexes, when using both a standard multivariable regression and Mendelian randomization (MR) analysis.

WHAT IS KNOWN ALREADY

High BMI in both women and men is associated with subfertility in observational studies and this relationship is further substantiated by a few small randomized controlled trials of weight reduction and success of assisted reproduction. Women with low BMI also have lower conception rates with assisted reproduction technologies.

STUDY DESIGN, SIZE, DURATION

Cohort study (the Norwegian Mother, Father and Child Cohort Study), 28 341 women and 26 252 men, recruited from all over Norway between 1999 and 2008.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Women (average age 30, average BMI 23.1 kg/m2) and men (average age 33, average BMI 25.5 kg/m2) had available genotype data and provided self-reported information on time-to-pregnancy and BMI. A total of 10% of couples were subfertile (time-to-pregnancy ≥12 months).

MAIN RESULTS AND THE ROLE OF CHANCE

Our findings support a J-shaped association between BMI and subfertility in both sexes using multivariable logistic regression models. Non-linear MR validated this relationship. A 1 kg/m2 greater genetically predicted BMI was linked to 18% greater odds of subfertility (95% CI 5% to 31%) in obese women (≥30.0 kg/m2) and 15% lower odds of subfertility (-24% to -2%) in women with BMI <20.0 kg/m2. A 1 kg/m2 higher genetically predicted BMI was linked to 26% greater odds of subfertility (8-48%) among obese men. Low genetically predicted BMI values were also related to greater subfertility risk in men at the lower end of the BMI distribution. A genetically predicted BMI of 23 and 25 kg/m2 was linked to the lowest subfertility risk in women and men, respectively.

LIMITATIONS, REASONS FOR CAUTION

The main limitations of our study were that we did not know whether the subfertility was driven by the women, men or both; the exclusive consideration of individuals of northern European ancestry; and the limited amount of participants with obesity or BMI values <20.0 kg/m2.

WIDER IMPLICATIONS OF THE FINDINGS

Our results support a causal effect of obesity on subfertility in women and men. Our findings also expand the current evidence by indicating that individuals with BMI values <20 kg/m2 may have an increased risk of subfertility. These results suggest that BMI values between 20 and 25 kg/m2 are optimal for a minimal risk of subfertility.

STUDY FUNDING/COMPETING INTEREST(S)

The MoBa Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Norwegian Ministry of Education and Research. This project received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No 947684). It was also partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. Open Access funding was provided by the Folkehelseinstituttet/Norwegian Institute of Public Health. D.A.L. is a UK National Institute for Health Research Senior Investigator (NF-SI-0611-10196) and is supported by the US National Institutes of Health (R01 DK10324) and a European Research Council Advanced Grant (DevelopObese; 669545). The funders had no role in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. D.A.L. receives (or has received in the last 10 years) research support from National and International government and charitable bodies, Roche Diagnostics and Medtronic for research unrelated to the current work. The rest of the authors declare that no competing interests exist.

TRIAL REGISTRATION NUMBER

N/A.

Testosterone, sex hormone-binding globulin, insulin-like growth factor-1 and endometrial cancer risk: observational and Mendelian randomization analyses

BACKGROUND

Dysregulation of endocrine pathways related to steroid and growth hormones may modify endometrial cancer risk; however, prospective data on testosterone, sex hormone-binding globulin (SHBG) and insulin-like growth factor (IGF)-1 are limited. To elucidate the role of these hormones in endometrial cancer risk we conducted complementary observational and Mendelian randomization (MR) analyses.

METHODS

The observational analyses included 159,702 women (80% postmenopausal) enrolled in the UK Biobank. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models. For MR analyses, genetic variants associated with hormone levels were identified and their association with endometrial cancer (12,906 cases/108,979 controls) was examined using two-sample MR.

RESULTS

In the observational analysis, higher circulating concentrations of total (HR per unit inverse normal scale = 1.38, 95% CI = 1.22-1.57) and free testosterone (HR per unit log scale = 2.07, 95% CI = 1.66-2.58) were associated with higher endometrial cancer risk. An inverse association was found for SHBG (HR per unit inverse normal scale = 0.76, 95% CI = 0.67-0.86). Results for testosterone and SHBG were supported by the MR analyses. No association was found between genetically predicted IGF-1 concentration and endometrial cancer risk.

CONCLUSIONS

Our results support probable causal associations between circulating concentrations of testosterone and SHBG with endometrial cancer risk.

Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank

OBJECTIVE

Socioeconomic factors have been suggested to influence the effect of education- and intelligence-associated genetic variants. However, results from previous studies on the interaction between socioeconomic status and education or intelligence have been inconsistent. The authors sought to assess these interactions in the UK Biobank cohort of 500,000 participants.

METHODS

The authors assessed the effect of socioeconomic deprivation on education- and intelligence-associated genetic variants by estimating the single-nucleotide polymorphism (SNP) heritability for fluid intelligence, educational attainment, and years of education in subsets of UK Biobank participants with different degrees of social deprivation, using linkage disequilibrium score regression. They also generated polygenic scores with LDpred and tested for interactions with social deprivation.

RESULTS

SNP heritability increased with socioeconomic deprivation for fluid intelligence, educational attainment, and years of education. Polygenic scores were also found to interact with socioeconomic deprivation, where the effects of the scores increased with increasing deprivation for all traits.

CONCLUSIONS

These results indicate that genetics have a larger influence on educational and cognitive outcomes in more socioeconomically deprived U.K. citizens, which has serious implications for equality of opportunity.

Circulating Levels of Testosterone, Sex Hormone Binding Globulin and Colorectal Cancer Risk: Observational and Mendelian Randomization Analyses

BACKGROUND

Epidemiologic studies evaluating associations between sex steroid hormones and colorectal cancer risk have yielded inconsistent results. To elucidate the role of circulating levels of testosterone, and sex hormone-binding globulin (SHBG) in colorectal cancer risk, we conducted observational and Mendelian randomization (MR) analyses.

METHODS

The observational analyses included 333,530 participants enrolled in the UK Biobank with testosterone and SHBG measured. HRs and 95% confidence intervals (CI) were estimated using multivariable Cox proportional hazards models. For MR analyses, genetic variants robustly associated with hormone levels were identified and their association with colorectal cancer (42,866 cases/42,752 controls) was examined using two-sample MR.

RESULTS

In the observational analysis, there was little evidence that circulating levels of total testosterone were associated with colorectal cancer risk; the MR analyses showed a greater risk for women (OR per 1-SD = 1.09; 95% CI, 1.01-1.17), although pleiotropy may have biased this result. Higher SHBG concentrations were associated with greater colorectal cancer risk for women (HR per 1-SD = 1.16; 95% CI, 1.05-1.29), but was unsupported by the MR analysis. There was little evidence of associations between free testosterone and colorectal cancer in observational and MR analyses.

CONCLUSIONS

Circulating concentrations of sex hormones are unlikely to be causally associated with colorectal cancer. Additional experimental studies are required to better understand the possible role of androgens in colorectal cancer development.

IMPACT

Our results from large-scale analyses provide little evidence for sex hormone pathways playing a causal role in colorectal cancer development.See related commentary by Hang and Shen, p. 1302.

Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data

Omics studies frequently use samples collected during cohort studies. Conditioning on sample availability can cause selection bias if sample availability is nonrandom. Inverse probability weighting (IPW) is purported to reduce this bias. We evaluated IPW in an epigenome-wide analysis testing the association between DNA methylation (261,435 probes) and age in healthy adolescent subjects (n = 114). We simulated age and sex to be correlated with sample selection and then evaluated four conditions: complete population/no selection bias (all subjects), naïve selection bias (no adjustment), and IPW selection bias (selection bias with IPW adjustment). Assuming the complete population condition represented the "truth," we compared each condition to the complete population condition. Bias or difference in associations between age and methylation was reduced in the IPW condition versus the naïve condition. However, genomic inflation and type 1 error were higher in the IPW condition relative to the naïve condition. Postadjustment using bacon, type 1 error and inflation were similar across all conditions. Power was higher under the IPW condition compared with the naïve condition before and after inflation adjustment. IPW methods can reduce bias in genome-wide analyses. Genomic inflation is a potential concern that can be minimized using methods that adjust for inflation.

Gene-environment dependencies lead to collider bias in models with polygenic scores

The application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.

Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

Genetically predicted circulating concentrations of micronutrients and risk of breast cancer: A Mendelian randomization study

The epidemiological literature reports inconsistent associations between consumption or circulating concentrations of micronutrients and breast cancer risk. We investigated associations between genetically predicted concentrations of 11 micronutrients (beta-carotene, calcium, copper, folate, iron, magnesium, phosphorus, selenium, vitamin B6 , vitamin B12 and zinc) and breast cancer risk using Mendelian randomization (MR). A two-sample MR study was conducted using 122 977 women with breast cancer and 105 974 controls from the Breast Cancer Association Consortium. MR analyses were conducted using the inverse variance-weighted approach, and sensitivity analyses were conducted to assess the impact of potential violations of MR assumptions. A value of 1 SD (SD: 0.08 mmol/L) higher genetically predicted concentration of magnesium was associated with a 17% (odds ratio [OR]: 1.17, 95% confidence interval [CI]: 1.10-1.25, P value = 9.1 × 10-7 ) and 20% (OR: 1.20, 95% CI: 1.08-1.34, P value = 3.2 × 10-6 ) higher risk of overall and ER+ve breast cancer, respectively. An inverse association was observed for a SD (0.5 mg/dL) higher genetically predicted phosphorus concentration and ER-ve breast cancer (OR: 0.84, 95% CI: 0.72-0.98, P value = .03). There was little evidence that any other nutrient was associated with breast cancer. The results for magnesium were robust under all sensitivity analyses and survived correction for multiple comparisons. Higher circulating concentrations of magnesium and potentially phosphorus may affect breast cancer risk. Further work is required to replicate these findings and investigate underlying mechanisms.

Common methodological issues and suggested solutions in bone research

Bone research is a dynamic area of scientific investigation that usually encompasses multidisciplines. Virtually all basic cellular research, clinical research and epidemiologic research rely on statistical concepts and methodology for inference. This paper discusses common issues and suggested solutions concerning the application of statistical thinking in bone research, particularly in clinical and epidemiological investigations. The issues are sample size estimation, biases and confounders, analysis of longitudinal data, categorization of continuous data, selection of significant variables, over-fitting, P-values, false positive finding, confidence interval, and Bayesian inference. It is hoped that by adopting the suggested measures the scientific quality of bone research can improve.

Multi-environment gene interactions linked to the interplay between polysubstance dependence and suicidality

Substance dependence diagnoses (SDs) are important risk factors for suicidality. We investigated the associations of multiple SDs with different suicidality outcomes, testing how genetic background moderates these associations. The Yale-Penn cohort (N = 15,557) was recruited to investigate the genetics of SDs. The Army STARRS (Study to Assess Risk and Resilience in Servicemembers) cohort (N = 11,236) was recruited to evaluate mental health risk and resilience among Army personnel. We applied multivariate logistic regression to investigate the associations of SDs with suicidality and, in the Yale-Penn cohort, we used the structured linear mixed model (StructLMM) to study multivariate gene-environment interactions. In Yale-Penn, lifetime polysubstance dependence was strongly associated with lifetime suicidality: having five SDs showed an association with suicidality, from odds ratio (OR) = 6.77 (95% confidence interval, CI = 5.74-7.99) for suicidal ideation (SI) to OR = 3.61 (95% CI = 2.7-4.86) for suicide attempt (SA). In Army STARRS, having multiple substance use disorders for alcohol and/or drugs was associated with increased suicidality ranging from OR = 2.88 (95% CI = 2.6-3.19) for SI to OR = 3.92 (95% CI = 3.19-4.81) for SA. In Yale-Penn, we identified multivariate gene-environment interactions (Bayes factors, BF > 0) of SI with respect to a gene cluster on chromosome 16 (LCAT, p = 1.82 × 10^-7; TSNAXIP1, p = 2.13 × 10^-7; CENPT, p = 2.32 × 10^-7; PARD6A, p = 5.57 × 10^-7) for opioid dependence (BF = 12.2), cocaine dependence (BF = 12.1), nicotine dependence (BF = 9.2), and polysubstance dependence (BF = 2.1). Comorbidity of multiple SDs is a significant associated with suicidality and heritability of suicidality is partially moderated by multivariate gene interactions.

A genome-wide association study in Japanese identified one variant associated with a preference for a Japanese dietary pattern

BACKGROUND/OBJECTIVES

Individual eating habits may be influenced by genetic factors, in addition to environmental factors. Previous studies suggested that adherence to Japanese food patterns was associated with a decreased risk of all-cause and cardiovascular disease mortality. We conducted a genome-wide association study (GWAS) in a Japanese population to find genetic variations that affect adherence to a Japanese food pattern.

SUBJECTS/METHODS

We analyzed GWAS data using 14,079 participants from the Japan Multi-Institutional Collaborative Cohort study. We made a Japanese food score based on six food groups. Association of the imputed variants with the Japanese food score was performed by linear regression analysis with adjustments for age, sex, total energy intake, alcohol intake (g/day), and principal components 1-10 omitting variants in the major histocompatibility region.

RESULTS

We found one SNP in the 14q11.2 locus that was significantly associated with the Japanese food score with P values <5 × 10^-8. Functional annotation revealed that the expression levels of two genes (BCL2L2, SLC22A17) were significantly inversely associated with this SNP. These genes are known to be related to olfaction and obesity.

CONCLUSION

We found a new SNP that was associated with the Japanese food score in a Japanese population. This SNP is inversely associated with genes link to olfaction and obesity.

Germline variants are associated with increased primary melanoma tumor thickness at diagnosis

Germline genetic variants have been identified, which predispose individuals and families to develop melanoma.

Tumor thickness is the strongest predictor of outcome for clinically localized primary melanoma patients. We sought to determine whether there is a heritable genetic contribution to variation in tumor thickness. If confirmed, this will justify the search for specific genetic variants influencing tumor thickness.

To address this, we estimated the proportion of variation in tumor thickness attributable to genome-wide genetic variation (variant-based heritability) using unrelated patients with measured primary cutaneous melanoma thickness. As a secondary analysis, we conducted a genome-wide association study (GWAS) of tumor thickness.

The analyses utilized 10 604 individuals with primary cutaneous melanoma drawn from nine GWAS datasets from eight cohorts recruited from the general population, primary care and melanoma treatment centers. Following quality control and filtering to unrelated individuals with study phenotypes, 8125 patients were used in the primary analysis to test whether tumor thickness is heritable. An expanded set of 8505 individuals (47.6% female) were analyzed for the secondary GWAS meta-analysis. Analyses were adjusted for participant age, sex, cohort and ancestry.

We found that 26.6% (SE 11.9%, P = 0.0128) of variation in tumor thickness is attributable to genome-wide genetic variation. While requiring replication, a chromosome 11 locus was associated (P < 5 × 10⁻⁸) with tumor thickness. Our work indicates that sufficiently large datasets will enable the discovery of genetic variants associated with greater tumor thickness, and this will lead to the identification of host biological processes influencing melanoma growth and invasion.

The Use Of Genetic Correlation And Mendelian Randomization Studies To Increase Our Understanding of Relationships Between Complex Traits

PURPOSE OF THE REVIEW

Increasing access to large-scale genetic datasets in population-based studies allows for genetic association studies as a means to examine previously known and novel relationships among complex traits. In this review, we discuss two widely used approaches to leverage genetic data to study the links between traits: Genome-wide genetic correlation and Mendelian Randomization (MR) studies.

RECENT FINDINGS

Both genetic correlation and MR studies have provided important novel insights. However, although they are less sensitive to many sources of bias present in traditional, observational epidemiology, they still rely on assumptions that in practice might be difficult to assess. To overcome this, development of novel methods less sensitive to these assumptions is an active area of research.

SUMMARY

We believe that as population-based genetic datasets grow larger and novel methods allowing for weaker forms of current assumptions become available, genetic correlation and MR studies will become an integral part of genetic epidemiology studies.

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants

Cerebral small vessel disease is a major cause of stroke and dementia, but its genetic basis is incompletely understood. We perform a genetic study of three MRI markers of the disease in UK Biobank imaging data and other sources: white matter hyperintensities (N = 42,310), fractional anisotropy (N = 17,663) and mean diffusivity (N = 17,467). Our aim is to better understand the disease pathophysiology. Across the three traits, we identify 31 loci, of which 21 were previously unreported. We perform a transcriptome-wide association study to identify associations with gene expression in relevant tissues, identifying 66 associated genes across the three traits. This genetic study provides insights into the understanding of the biological mechanisms underlying small vessel disease.

Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.

Mendelian Randomization Study of Obesity and Cerebrovascular Disease

OBJECTIVE

To systematically investigate causal relationships between obesity and cerebrovascular disease and the extent to which hypertension and hyperglycemia mediate the effect of obesity on cerebrovascular disease.

METHODS

We used summary statistics from genome-wide association studies for body mass index (BMI), waist-to-hip ratio (WHR), and multiple cerebrovascular disease phenotypes. We explored causal associations with 2-sample Mendelian randomization (MR) accounting for genetic covariation between BMI and WHR, and we assessed what proportion of the association between obesity and cerebrovascular disease was mediated by systolic blood pressure (SBP) and blood glucose levels, respectively.

RESULTS

Genetic predisposition to higher BMI did not increase the risk of cerebrovascular disease. In contrast, for each 10% increase in WHR there was a 75% increase (95% confidence interval [CI] = 44-113%) in risk for large artery ischemic stroke, a 57% (95% CI = 29-91%) increase in risk for small vessel ischemic stroke, a 197% increase (95% CI = 59-457%) in risk of intracerebral hemorrhage, and an increase in white matter hyperintensity volume (β = 0.11, 95% CI = 0.01-0.21). These WHR associations persisted after adjusting for genetic determinants of BMI. Approximately one-tenth of the observed effect of WHR was mediated by SBP for ischemic stroke (proportion mediated: 12%, 95% CI = 4-20%), but no evidence of mediation was found for average blood glucose.

INTERPRETATION

Abdominal adiposity may trigger causal pathological processes, partially independent from blood pressure and totally independent from glucose levels, that lead to cerebrovascular disease. Potential targets of these pathological processes could represent novel therapeutic opportunities for stroke. ANN NEUROL 2020;87:516-524.

Sources of bias in genomics research of oral and dental traits

Evidence regarding the genomic basis of oral/dental traits and diseases is a fundamental pillar of the emerging notion of precision health. During the last decade, technological advances have improved the feasibility and affordability of conducting genome-wide association studies (GWAS) and studying the associations of emanating data with both common and rare oral conditions. Most evidence thus far emanates from GWAS of dental caries and periodontal disease that have tested the associations of several million single nucleotide polymorphisms (SNPs) with typically binary, health vs. disease phenotypes. GWAS offer advantages over the previous candidate-gene studies, mainly owing to their agnostic (i.e., unbiased, or hypothesis-free) nature. Nevertheless, GWAS are prone to virtually all sources of random and systematic error. Here, we review common sources of bias in genomics research with focus on GWAS including: type I and II errors, population stratification and heterogeneity, selection bias, adjustment for heritable covariates, appropriate reference panels for imputation, and gene annotation. We argue that valid and precise phenotype measurement is a key requirement, as GWAS sample sizes and thus statistical power increase. Finally, we stress that the lack of diversity of populations with phenotypes and genotypes is a major limitation for the generalizability and ultimate translation of the emerging genomics evidence-base into oral health promotion for all.

Using human genetics to understand the disease impacts of testosterone in men and women

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22-1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33-1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76-0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.

Predicting Polygenic Risk of Psychiatric Disorders

Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10^-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r² > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10^-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.