Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations.

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP).

OBJECTIVE: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS: T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition.

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.

Exploiting meta-analysis of genome-wide interaction with serum 25-hydroxyvitamin D to identify novel genetic loci associated with pulmonary function.

BACKGROUND: Higher 25-hydroxyvitamin D (25(OH)D) concentrations in serum has a positive association with pulmonary function. Investigating genome-wide interactions with 25(OH)D may reveal new biological insights into pulmonary function. OBJECTIVES: We aimed to identify novel genetic variants associated with pulmonary function by accounting for 25(OH)D interactions. METHODS: We included 211,264 participants from the observational United Kingdom Biobank study with pulmonary function tests (PFTs), genome-wide genotypes, and 25(OH)D concentrations from 4 ancestral backgrounds-European, African, East Asian, and South Asian. Among PFTs, we focused on forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) because both were previously associated with 25(OH)D. We performed genome-wide association study (GWAS) analyses that accounted for variant×25(OH)D interaction using the joint 2 degree-of-freedom (2df) method, stratified by participants' smoking history and ancestry, and meta-analyzed results. We evaluated interaction effects to determine how variant-PFT associations were modified by 25(OH)D concentrations and conducted pathway enrichment analysis to examine the biological relevance of our findings. RESULTS: Our GWAS meta-analyses, accounting for interaction with 25(OH)D, revealed 30 genetic variants significantly associated with FEV1 or FVC (P2df <5.00×10-8) that were not previously reported for PFT-related traits. These novel variant signals were enriched in lung function-relevant pathways, including the p38 MAPK pathway. Among variants with genome-wide-significant 2df results, smoking-stratified meta-analyses identified 5 variants with 25(OH)D interactions that influenced FEV1 in both smoking groups (never smokers P1df interaction<2.65×10-4; ever smokers P1df interaction<1.71×10-5); rs3130553, rs2894186, rs79277477, and rs3130929 associations were only evident in never smokers, and the rs4678408 association was only found in ever smokers. CONCLUSION: Genetic variant associations with lung function can be modified by 25(OH)D, and smoking history can further modify variant×25(OH)D interactions. These results expand the known genetic architecture of pulmonary function and add evidence that gene-environment interactions, including with 25(OH)D and smoking, influence lung function.

Genetic and transcriptomic landscape of colonic diverticulosis.

OBJECTIVE: Colonic diverticulosis is a prevalent condition among older adults, marked by the presence of thin-walled pockets in the colon wall that can become inflamed, infected, haemorrhage or rupture. We present a case-control genetic and transcriptomic study aimed at identifying the genetic and cellular determinants underlying this condition and the relationship with other gastrointestinal disorders. DESIGN: We conducted DNA and RNA sequencing on colonic tissue from 404 patients with (N=172) and without (N=232) diverticulosis. We investigated variation in the transcriptome associated with diverticulosis and further integrated this variation with single-cell RNA-seq data from the human intestine. We also integrated our expression quantitative trait loci with genome-wide association study using Mendelian randomisation (MR). Furthermore, a Polygenic Risk Score analysis gauged associations between diverticulosis severity and other gastrointestinal disorders. RESULTS: We discerned 38 genes with differential expression and 17 with varied transcript usage linked to diverticulosis, indicating tissue remodelling as a primary diverticula formation mechanism. Diverticula formation was primarily linked to stromal and epithelial cells in the colon including endothelial cells, myofibroblasts, fibroblasts, goblet, tuft, enterocytes, neurons and glia. MR highlighted five genes including CCN3, CRISPLD2, ENTPD7, PHGR1 and TNFSF13, with potential causal effects on diverticulosis. Notably, ENTPD7 upregulation was confirmed in diverticulosis cases. Additionally, diverticulosis severity was positively correlated with genetic predisposition to diverticulitis. CONCLUSION: Our results suggest that tissue remodelling is a primary mechanism for diverticula formation. Individuals with an increased genetic proclivity to diverticulitis exhibit a larger numbers of diverticula on colonoscopy.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

Shared genetic risk between anorexia nervosa and cardiovascular disease events: Evidence from genome-wide association studies.

OBJECTIVE: Cardiovascular complications occur in up to 80% of patients with anorexia nervosa (AN), yet the underlying mechanisms warrant further investigation. We assessed the genetic correlation (rg ) between AN and cardiovascular disease (CVD) events to inform whether elevated cardiovascular risk among individuals with AN is due to shared genetic effects. METHOD: We used genome-wide association study summary statistics for AN (N = 72,517), AN with binge eating (N = 12,630), AN without binge eating (N = 12,516), and six CVD events (N = 390,142 to 977,323). We calculated the rg s via linkage disequilibrium score regression and corrected for multiple testing using false discovery rate. RESULTS: Significant rg s emerged between AN with heart failure (rg  = -0.11, SE = 0.05, q = .04) and myocardial infarction (rg  = -0.10, SE = 0.03, q = .01). AN with binge eating had a significant rg with myocardial infarction (rg  = -0.15, SE = 0.06, q = .02). No significant rg emerged between AN without binge eating and any CVD event. DISCUSSION: Some loci affect the liability to AN and CVD in opposite directions and the shared genetic effects may not be consistent across all CVD events. Our results provide further evidence suggesting that the elevated cardiovascular risk in AN may not be due to shared genetic underpinnings, but more likely a downstream consequence of the disease.

Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos.

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.

Branched chain amino acids harbor distinct and often opposing effects on health and disease.

BACKGROUND: The branched chain amino acids (BCAA) leucine, isoleucine, and valine are essential nutrients that have been associated with diabetes, cancers, and cardiovascular diseases. Observational studies suggest that BCAAs exert homogeneous phenotypic effects, but these findings are inconsistent with results from experimental human and animal studies. METHODS: Hypothesizing that inconsistencies between observational and experimental BCAA studies reflect bias from shared lifestyle and genetic factors in observational studies, we used data from the UK Biobank and applied multivariable Mendelian randomization causal inference methods designed to address these biases. RESULTS: In n = 97,469 participants of European ancestry (mean age = 56.7 years; 54.1% female), we estimate distinct and often opposing total causal effects for each BCAA. For example, of the 117 phenotypes with evidence of a statistically significant total causal effect for at least one BCAA, almost half (44%, n = 52) are associated with only one BCAA. These 52 associations include total causal effects of valine on diabetic eye disease [odds ratio = 1.51, 95% confidence interval (CI) = 1.31, 1.76], valine on albuminuria (odds ratio = 1.14, 95% CI = 1.08, 1.20), and isoleucine on angina (odds ratio = 1.17, 95% CI = 1.31, 1.76). CONCLUSIONS: Our results suggest that the observational literature provides a flawed picture of BCAA phenotypic effects that is inconsistent with experimental studies and could mislead efforts developing novel therapeutics. More broadly, these findings motivate the development and application of causal inference approaches that enable 'omics studies conducted in observational settings to account for the biasing effects of shared genetic and lifestyle factors.

The three branched chain amino acids (BCAAs) leucine, isoleucine, and valine are important building blocks of muscle proteins that are obtained from the diet. Many studies in human populations have examined whether BCAAs affect health and disease. These human studies report results that are inconsistent with results from highly controlled animal studies. Because interest in the therapeutic targeting of BCAAs is growing, we wanted to better understand these discrepancies. Briefly, we used data from a large database that captured many diseases (e.g., cardiovascular disease, cancers, and respiratory disease) and new statistical methods. Our results showed that discrepancies between human studies and animal studies may reflect errors in the ways human studies were designed and conducted. As a result, these human studies may provide a flawed picture of BCAA effects that could mislead efforts developing novel therapeutics.

Epigenome-wide methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2+): a prospective cohort study in the United States.

BACKGROUND: Methylation levels may be associated with and serve as markers to predict risk of progression of precancerous cervical lesions. We conducted an epigenome-wide association study (EWAS) of CpG methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2 +) following an abnormal screening test. METHODS: A prospective US cohort of 289 colposcopy patients with normal or CIN1 enrollment histology was assessed. Baseline cervical sample DNA was analyzed using Illumina HumanMethylation 450K (n = 76) or EPIC 850K (n = 213) arrays. Participants returned at provider-recommended intervals and were followed up to 5 years via medical records. We assessed continuous CpG M values for 9 cervical cancer-associated genes and time-to-progression to CIN2+. We estimated CpG-specific time-to-event ratios (TTER) and hazard ratios using adjusted, interval-censored Weibull accelerated failure time models. We also conducted an exploratory EWAS to identify novel CpGs with false discovery rate (FDR) < 0.05. RESULTS: At enrollment, median age was 29.2 years; 64.0% were high-risk HPV-positive, and 54.3% were non-white. During follow-up (median 24.4 months), 15 participants progressed to CIN2+. Greater methylation levels were associated with a shorter time-to-CIN2+ for CADM1 cg03505501 (TTER = 0.28; 95%CI 0.12, 0.63; FDR = 0.03) and RARB Cluster 1 (TTER = 0.46; 95% CI 0.29, 0.71; FDR = 0.01). There was evidence of similar trends for DAPK1 cg14286732, PAX1 cg07213060, and PAX1 Cluster 1. The EWAS detected 336 novel progression-associated CpGs, including those located in CpG islands associated with genes FGF22, TOX, COL18A1, GPM6A, XAB2, TIMP2, GSPT1, NR4A2, and APBB1IP. CONCLUSIONS: Using prospective time-to-event data, we detected associations between CADM1-, DAPK1-, PAX1-, and RARB-related CpGs and cervical disease progression, and we identified novel progression-associated CpGs. IMPACT: Methylation levels at novel CpG sites may help identify individuals with ≤CIN1 histology at higher risk of progression to CIN2+ and inform risk-based cervical cancer screening guidelines.

Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R 2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified TCL1A , NRIP1 , and TERT locus variants as modulators of mCA clonal expansion rate.

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics.

The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆγ2 = 0.012 ± 9.2 × 10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts.

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.

Challenges and strategies for recruitment of minorities to clinical research and trials.

Minority populations are largely absent from clinical research trials. The neglect of these populations has become increasingly apparent, with escalating cancer burdens and chronic disease. The challenges to recruitment of minorities in the United States are multiple including trust or lack thereof. Keys to successful recruitment are responding to community issues, its history, beliefs, and its social and economic pressures. The strategy we have used in many low-income, sometimes remote, communities is to recruit staff from the same community and train them in the required basic research methods. They are the first line of communication. After our arrival in the Texas Rio Grande Valley in 2001, we applied these principles learned over years of global research, to studies of chronic diseases. Beginning in 2004, we recruited and trained a team of local women who enrolled in a cohort of over five thousand Mexican Americans from randomly selected households. This cohort is being followed, and the team has remained, acquiring not only advanced skills (ultrasound, FibroScan, retinal photos, measures of cognition, etc.) but capacity to derive key health information. Currently, we are participating in multiple funded studies, including an NIH clinical trial, liver disease, obesity, and diabetes using multiomics aimed at developing precision medicine approaches to chronic disease prevention and treatment.