Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data.

OBJECTIVE: This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data. METHODS: We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction. RESULTS: We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues. CONCLUSION: This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Information-incorporated sparse convex clustering for disease subtyping.

MOTIVATION: Heterogeneity in human diseases presents clinical challenges in accurate disease characterization and treatment. Recently available high throughput multi-omics data may offer a great opportunity to explore the underlying mechanisms of diseases and improve disease heterogeneity assessment throughout the treatment course. In addition, increasingly accumulated data from existing literature may be informative about disease subtyping. However, the existing clustering procedures, such as Sparse Convex Clustering (SCC), cannot directly utilize the prior information even though SCC produces stable clusters. RESULTS: We develop a clustering procedure, information-incorporated Sparse Convex Clustering, to respond to the need for disease subtyping in precision medicine. Utilizing the text mining approach, the proposed method leverages the existing information from previously published studies through a group lasso penalty to improve disease subtyping and biomarker identification. The proposed method allows taking heterogeneous information, such as multi-omics data. We conduct simulation studies under several scenarios with various accuracy of the prior information to evaluate the performance of our method. The proposed method outperforms other clustering methods, such as SCC, K-means, Sparse K-means, iCluster+, and Bayesian Consensus Clustering. In addition, the proposed method generates more accurate disease subtypes and identifies important biomarkers for future studies in real data analysis of breast and lung cancer-related omics data. In conclusion, we present an information-incorporated clustering procedure that allows coherent pattern discovery and feature selection. AVAILABILITY AND IMPLEMENTATION: The code is available upon request.

Association of bone fracture with 30-year body mass index (BMI) trajectories: findings from the Framingham Heart Study : Bone fracture and 30-year BMI trajectories.

A knowledge gap exists in associating later life's osteoporotic fracture and middle adulthood's BMI trajectories. We observed an association showing those transitioning from overweight to normal weight face a higher fracture risk in late adulthood, emphasizing the potential benefits of maintaining a stable BMI to reduce late-life fractures. PURPOSE: Numerous studies on the relationship between obesity and fractures have relied on body mass index (BMI) at a single time point, yielding inconclusive results. This study investigated the association of BMI trajectories over middle adulthood with fracture risk in late adulthood. METHODS: This prospective cohort study analyzed 1772 qualified participants from the Framingham Original Cohort Study, with 292 (16.5%) incident fractures during an average of 17.1-year follow-up. We constructed BMI trajectories of age 35-64 years based on latent class mixed modeling and explored their association with the risk of fracture after 65 years using the Cox regression. RESULTS: The result showed that compared to the BMI trajectory Group 4 (normal to slightly overweight; see "Methods" for detailed description), Group 1 (overweight declined to normal weight) had a higher all-fracture risk after age 65 (hazard ratio [HR], 2.22, 95% CI, 1.13-4.39). The secondary analysis focusing on lower extremity fractures (pelvis, hip, leg, and foot) showed a similar association pattern. CONCLUSIONS: This study suggested that people whose BMI slightly increased from normal weight to low-level overweight during 30 years of middle adulthood confer a significantly lower risk of fracture in later life than those whose BMI declined from overweight to normal weight. This result implies the potentially beneficial effects of avoiding weight loss to normal weight during middle adulthood for overweight persons, with reduced fracture risk in late life.

Examining the Association between Medical Marijuana Legalization and Criminal Behaviors: Evidence from a U.S. Representative Sample.

Background: Medical marijuana legalization (MML) has been widely implemented in the past decade. However, the debates regarding the consequences of MML persist, especially criminal behaviors. Objectives: We examined the association between MML and criminal behaviors among adults in the United States. The criminal behaviors measured three past-year offenses: whether the adult (1) have sold illegal drugs, (2) have stolen anything worth > $50 USD, or (3) have attacked someone. Methods: Using the 2015-2020 National Survey of Drug Use and Health, we included 214,505 adults in our primary analysis for 2015-2019 and 27,170 adults in 2020 for supplemental analysis (age > = 18). Weighted multivariable logistic regression models were used to examine the association between MML and three criminal behaviors. Results: In our primary analysis, we observed no statistically significant association between MML and the three outcomes of criminal behavior. Nevertheless, our supplemental analysis of the 2020 data showed MML was associated with increasing odds of the three criminal behaviors (have sold illegal drugs: AOR [adjusted odds ratio] = 1.7; have stolen anything worth > $50 USD: AOR = 1.9; have attacked someone: AOR = 1.8; all p < 0.05). Conclusion: Surveys from 2015 to 2019 did not suggest MML as a risk factor for higher incidence of criminal behaviors. However, 2020 data showed statistically significant association between MML and selected criminal behaviors. Issues related to the COVID-19 pandemic, such as the U.S. economic downturn, could potentially explain this discrepancy. Further research efforts may be warranted.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

ANNORE: genetic fine-mapping with functional annotation.

Genome-wide association studies (GWASs) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes and functional annotation from external databases to prioritize the most plausible causal single-nucleotide polymorphisms (SNPs) in each trait-associated locus. Our proposed method improves upon previous fine-mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine-mapping studies helps to distinguish between variants in high LD and to identify promising targets for follow-up studies.

The impact of obesity on lung function measurements and respiratory disease: A Mendelian randomization study.

INTRODUCTION: Observational studies have shown that body mass index (BMI) and waist-to-hip ratio (WHR) are both inversely associated with lung function, as assessed by forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). However, observational data are susceptible to confounding and reverse causation. METHODS: We selected genetic instruments based on their relevant large-scale genome-wide association studies. Summary statistics of lung function and asthma came from the UK Biobank and SpiroMeta Consortium meta-analysis (n = 400,102). After examining pleiotropy and removing outliers, we applied inverse-variance weighting to estimate the causal association of BMI and BMI-adjusted WHR (WHRadjBMI) with FVC, FEV1, FEV1/FVC, and asthma. Sensitivity analyses were performed using weighted median, MR-Egger, and MRlap methods. RESULTS: We found that BMI was inversely associated with FVC (effect estimate, -0.167; 95% confidence interval (CI), -0.203 to -0.130) and FEV1 (effect estimate, -0.111; 95%CI, -0.149 to -0.074). Higher BMI was associated with higher FEV1/FVC (effect estimate, 0.079; 95%CI, 0.049 to 0.110) but was not significantly associated with asthma. WHRadjBMI was inversely associated with FVC (effect estimate, -0.132; 95%CI, -0.180 to -0.084) but has no significant association with FEV1. Higher WHR was associated with higher FEV1/FVC (effect estimate, 0.181; 95%CI, 0.130 to 0.232) and with increased risk of asthma (effect estimate, 0.027; 95%CI, 0.001 to 0.053). CONCLUSION: We found significant evidence that increased BMI is suggested to be causally related to decreased FVC and FEV1, and increased BMI-adjusted WHR could lead to lower FVC value and higher risk of asthma. Higher BMI and BMI-adjusted WHR were suggested to be causally associated with higher FEV1/FVC.

Identifying important gene signatures of BMI using network structure-aided nonparametric quantile regression.

We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B-spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network-constrained penalty by the total ℓ 2 $$ {\ell}_2 $$ norm of the difference between the effect functions of any two linked genes in the known network. We further propose an efficient computation procedure to solve the optimization problem that arises in our model. Simulation studies show that our proposed method performs well in identifying more truly associated genes and less falsely associated genes than alternative approaches. We apply the proposed method to analyze the microarray gene-expression dataset in the Framingham Heart Study and identify several 75 percentile BMI associated genes. In conclusion, our proposed approach efficiently identifies the outcome-associated variables in a nonparametric additive quantile regression framework by leveraging known network information.

Instrumental variable model average with applications in Mendelian randomization.

Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.

Exome sequence association study of levels and longitudinal change of cardiovascular risk factor phenotypes in European Americans and African Americans from the Atherosclerosis Risk in Communities Study.

Cardiovascular disease (CVD) is responsible for 31% of all deaths worldwide. Among CVD risk factors are age, race, increased systolic blood pressure (BP), and dyslipidemia. Both BP and blood lipids levels change with age, with a dose-dependent relationship between the cumulative exposure to hyperlipidemia and the risk of CVD. We performed an exome sequence association study using longitudinal data with up to 7805 European Americans (EAs) and 3171 African Americans (AAs) from the Atherosclerosis Risk in Communities (ARIC) study. We assessed associations of common (minor allele frequency > 5%) nonsynonymous and splice-site variants and gene-based sets of rare variants with levels and with longitudinal change of seven CVD risk factor phenotypes (BP traits: systolic BP, diastolic BP, pulse pressure; lipids traits: triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]). Furthermore, we investigated the relationship of the identified variants and genes with select CVD endpoints. We identified two novel genes: DCLK3 associated with the change of HDL-C levels in AAs and RAB7L1 associated with the change of LDL-C levels in EAs. RAB7L1 is further associated with an increased risk of heart failure in ARIC EAs. Investigation of the contribution of genetic factors to the longitudinal change of CVD risk factor phenotypes promotes our understanding of the etiology of CVD outcomes, stressing the importance of incorporating the longitudinal structure of the cohort data in future analyses.

Leveraging family history in genetic association analyses of binary traits.

BACKGROUND: Considering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS. RESULTS: In our simulation scenario of trait with modest T2D heritability (h2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10-8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits. CONCLUSIONS: Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

Rare coding variants in RCN3 are associated with blood pressure.

BACKGROUND: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. RESULTS: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7). CONCLUSIONS: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

Hepatic Fibrosis Associates With Multiple Cardiometabolic Disease Risk Factors: The Framingham Heart Study.

BACKGROUND AND AIMS: NAFLD is increasing in prevalence and will soon be the most common chronic liver disease. Liver stiffness, as assessed by vibration-controlled transient elastography (VCTE), correlates with hepatic fibrosis, an important predictor of liver-related and all-cause mortality. Although liver fat is associated with cardiovascular risk factors, the association between hepatic fibrosis and cardiovascular risk factors is less clear. APPROACH AND RESULTS: We performed VCTE, assessing controlled attenuation parameter (CAP; measure of steatosis) and liver stiffness measurement (LSM) in 3,276 Framingham Heart Study adult participants (53.9% women, mean age 54.3 ± 9.1 years) presenting for a routine study visit. We performed multivariable-adjusted logistic regression models to determine the association between LSM and obesity-related, vascular-related, glucose-related, and cholesterol-related cardiovascular risk factors. The prevalence of hepatic steatosis (CAP ≥ 290 dB/m) was 28.8%, and 8.8% had hepatic fibrosis (LSM ≥ 8.2 kPa). Hepatic fibrosis was associated with multiple cardiovascular risk factors, including increased odds of obesity (OR, 1.82; 95% CI, 1.35-2.47), metabolic syndrome (OR, 1.49; 95% CI 1.10-2.01), diabetes (OR, 2.67; 95% CI, 1.21-3.75), hypertension (OR, 1.52; 95% CI, 1.15-1.99), and low high-density lipoprotein cholesterol (OR, 1.47; 95% CI, 1.09-1.98), after adjustment for age, sex, smoking status, alcohol drinks/week, physical activity index, aminotransferases, and CAP. CONCLUSIONS: In our community-based cohort, VCTE-defined hepatic fibrosis was associated with multiple cardiovascular risk factors, including obesity, metabolic syndrome, diabetes, hypertension, and high-density lipoprotein cholesterol, even after accounting for covariates and CAP. Additional longitudinal studies are needed to determine if hepatic fibrosis contributes to incident cardiovascular disease risk factors or events.

Use of likelihood estimates for variances for the design and evaluation of multiregional clinical trials with heterogeneous variances.

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

Evaluation of population stratification adjustment using genome-wide or exonic variants.

Population stratification may cause an inflated type-I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed-effects models and assess the utility of genome-wide (GW) and exonic variants, by simulation and using a data set from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices computed by GW and exonic markers are different, the type-I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of confounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC-based corrections and mixed-effects models. We find that while these two methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed-effects model can reach a higher power for SNVs highly confounded by population stratification.

Efficient gene-environment interaction tests for large biobank-scale sequencing studies.

Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects.

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course.