A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin.

Previous genome-wide association studies (GWASs) for adiponectin, a complex trait linked to type 2 diabetes and obesity, identified >20 associated loci. However, most loci were identified in populations of European ancestry, and many of the target genes underlying the associations remain unknown. We conducted a cross-ancestry adiponectin GWAS meta-analysis in ≤46,434 individuals from the Metabolic Syndrome in Men (METSIM) cohort and the ADIPOGen and AGEN consortiums. We combined study-specific association summary statistics using a fixed-effects, inverse variance-weighted approach. We identified 22 loci associated with adiponectin (p < 5×10-8), including 15 known and seven previously unreported loci. Among individuals of European ancestry, Genome-wide Complex Traits Analysis joint conditional analysis (GCTA-COJO) identified 14 additional distinct signals at the ADIPOQ, CDH13, HCAR1, and ZNF664 loci. Leveraging the cross-ancestry data, FINEMAP + SuSiE identified 45 causal variants (PP > 0.9), which also exhibited potential pleiotropy for cardiometabolic traits. To prioritize target genes at associated loci, we propose a combinatorial likelihood scoring formalism (Gene Priority Score [GPScore]) based on measures derived from 11 gene prioritization strategies and the physical distance to the transcription start site. With GPScore, we prioritize the 30 most probable target genes underlying the adiponectin-associated variants in the cross-ancestry analysis, including well-known causal genes (e.g., ADIPOQ, CDH13) and additional genes (e.g., CSF1, RGS17). Functional association networks revealed complex interactions of prioritized genes, their functionally connected genes, and their underlying pathways centered around insulin and adiponectin signaling, indicating an essential role in regulating energy balance in the body, inflammation, coagulation, fibrinolysis, insulin resistance, and diabetes. Overall, our analyses identify and characterize adiponectin association signals and inform experimental interrogation of target genes for adiponectin.

Preterm birth, birthweight, and subsequent risk for depression.

An individual's birthweight, a marker of in utero exposures, was recently associated with certain psychiatric conditions. However, studies investigating the relationship between an individual's preterm birth status and/or birthweight and risk for depression during adulthood are sparse; we used data from the Women's Health Initiative (WHI) to investigate these potential associations. At study entry, 86,925 postmenopausal women reported their birthweight by category (<6 lbs., 6-7 lbs. 15 oz., 8-9 lbs. 15 oz., or ≥10 lbs.) and their preterm birth status (full-term or ≥4 weeks premature). Women also completed the Burnham screen for depression and were asked to self-report if: (a) they had ever been diagnosed with depression, or (b) if they were taking antidepressant medications. Linear and logistic regression models were used to estimate unadjusted and adjusted effect estimates. Compared to those born weighing between 6 and 7 lbs. 15 oz., individuals born weighing <6 lbs. (ßadj = 0.007, P < 0.0001) and ≥10 lbs. (ßadj = 0.006, P = 0.02) had significantly higher Burnam scores. Individuals born weighing <6 lbs. were also more likely to have depression (adjOR 1.21, 95% CI 1.11-1.31). Individuals born preterm were also more likely to have depression (adjOR 1.18, 95% CI 1.02-1.35); while attenuated, this association remained in analyses limited to only those reportedly born weighing <6 lbs. Our research supports the role of early life exposures on health risks across the life course. Individuals born at low or high birthweights and those born preterm may benefit from early evaluation and long-term follow-up for the prevention and treatment of mental health outcomes.

Association of maternal birth weight and maternal preterm birth with subsequent risk for adverse reproductive outcomes: The Women's Health Initiative.

BACKGROUND: Advancements in medical technology and pharmacologic interventions have drastically improved survival of infants born preterm and low birth weight, but knowledge regarding the long-term health impacts of these individuals is limited and inconsistent. AIM: To investigate whether an individual's birthweight or history of being born preterm increases the risk of an adverse reproductive outcome. STUDY DESIGN: Nested case-control study within the Women's Health Initiative. SUBJECTS: 79,934 individuals who self-reported their personal birthweight category and/or preterm birth status. OUTCOMES MEASURES: Self-reported pregnancy outcomes: subfertility, miscarriage, stillbirth, preeclampsia, gestational diabetes, gestational hypertension, preterm birth, low birthweight infant, high birthweight infant. Logistic regression models were used to estimate unadjusted and adjusted odds ratios (OR). RESULTS: After adjustments, individuals reporting their birthweight <6lbs. were 20 % more likely to have a stillbirth or 70 % more likely to have a low birthweight infant and were less likely to have a full-term birth or high birthweight infant during their pregnancy. Individuals reporting a birthweight ≥10 lbs. were more likely to have a high birthweight infant (OR 3.49, 95 % CI 2.73-4.39) and less likely to have a low birthweight infant (OR 0.64, 95 % CI 0.47-0.82). Individuals born preterm were at increased risk for infertility, miscarriage, preeclampsia, gestational diabetes, and delivering a preterm or low birthweight infant. CONCLUSIONS: As more individuals born preterm and/or low birthweight survive to adulthood, the incidence and prevalence of poor reproductive outcomes may increase. Women born at extremes of birthweight and prematurity may need to be monitored more closely during their own pregnancies.

Associations of maternal preterm birth with subsequent risk for type 2 diabetes in women from the women's health initiative.

Preterm birth has been associated with insulin resistance and beta-cell dysfunction, a hallmark characteristic of type 2 diabetes. However, studies investigating the relationship between a personal history of being born preterm and type 2 diabetes are sparse. We sought to investigate the potential association between a personal history of being born preterm and risk for type 2 diabetes in a racially and ethnically diverse population. Baseline and incident data (>16 years of follow-up) from the Women's Health Initiative (n = 85,356) were used to examine the association between personal history of being born preterm (born 1910-1940s) and prevalent (baseline enrollment; cross-sectional) or incident (prospective cohort) cases of type 2 diabetes. Logistic and Cox proportional hazards regression models were used to estimate odds and hazards ratios. Being born preterm was significantly, positively associated with odds for prevalent type 2 diabetes at enrollment (adjOR = 1.79, 95% CI 1.43-2.24; P < 0.0001). Stratified regression models suggested the positive associations at baseline were consistent across race and ethnicity groups. However, being born preterm was not significantly associated with risk for incident type 2 diabetes. Regression models stratified by age at enrollment suggest the relationship between being born preterm and type 2 diabetes persists only among younger age groups. Preterm birth was associated with higher risk of type 2 diabetes but only in those diagnosed with type 2 diabetes prior to study enrollment, suggesting the association between preterm birth and type 2 diabetes may exist at earlier age of diagnosis but wane over time.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Identification of genetic effects underlying type 2 diabetes in South Asian and European populations.

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.

Validation of birth certificate and maternal recall of events in labor and delivery with medical records in the Iowa health in pregnancy study.

BACKGROUND: Epidemiological research of events related to labor and delivery frequently uses maternal interview or birth certificates as a primary method of data collection; however, the validity of these data are rarely confirmed. This study aimed to examine the validity of birth certificate data and maternal interview of maternal demographics and events related to labor and delivery with data abstracted from medical records in a US setting. METHODS: Birth certificate and maternal recall data from the Iowa Health in Pregnancy Study (IHIPS), a population-based case-control study of risk factors for preterm and small-for-gestational age births, were linked to medical record data to assess the validity of events that occurred during labor and delivery along with reported maternal demographics. Sensitivity, specificity, positive and negative predictive values, and kappa scores were calculated. RESULTS: Postpartum maternal recall and birth certificate data were excellent for infant characteristics (birth weight, gestational age, infant sex) and variables related to labor and delivery (mode of delivery) when compared with medical records. Birth certificate data for labor induction had low sensitivity (46.3%) and positive predictive value (18.3%) compared to medical records. Compared to maternal interview, birth certificate data also had poor agreement for smoking and alcohol use during pregnancy. Agreement between all three methods of data collection was very low for pregnancy weight gain (kappa = 0.07-0.08). CONCLUSIONS: Maternal interview and birth certificate data can be a valid source for collecting data on infant characteristics and events that occurred during labor and delivery. However, caution should be used if solely using birth certificate data to gather data on maternal demographic and/or lifestyle factors.

Birthweight and subsequent risk for thyroid and autoimmune conditions in postmenopausal women.

The objective of this study was to determine the association between birthweight and risk of thyroid and autoimmune conditions in a large sample of postmenopausal women. Baseline data from the Women's Health Initiative (n = 80,806) were used to examine the associations between birthweight category (<6 lbs., 6-7 lbs. 15 oz, 8-9 lbs. 15 oz, and ≥10 lbs.) and prevalent thyroid (underactive and overactive thyroid and goiter) and autoimmune (lupus, rheumatoid arthritis (RA), multiple sclerosis, ulcerative colitis/Crohn's disease) conditions. Follow-up questionnaire data were used to examine the associations between birthweight and incident underactive and overactive thyroid, lupus, and RA. Logistic and Cox proportional hazards regression models were used to estimate crude and adjusted odds (OR) and hazards ratios (HR), respectively. Overall, women born weighing ≥10 lbs. had an increased risk for underactive thyroid [OR 1.14 (95% CI 1.02, 1.28)] and incident lupus [HR 1.51 (95% CI 1.12, 2.03)] and a decreased risk for overactive thyroid [OR 0.67 (95% CI 0.50, 0.92)] compared to women born weighing 6-7.99 lbs., after adjustment for adult BMI, demographic variables, and lifestyle factors. Further, women born weighing <6 lbs. were at increased risk for underactive thyroid [OR 1.13 (95% CI 1.04, 1.22)]. Birthweight was not associated with other thyroid or autoimmune disorders. High birthweight was associated with later-life thyroid and autoimmune conditions while low birthweight was associated with underactive thyroid. Preconception and prenatal interventions aimed at reducing the risk of both high and low birthweights may reduce the burden of later-life thyroid and autoimmune conditions.

Genetic discovery and risk characterization in type 2 diabetes across diverse populations.

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10-8), which replicated in independent studies of African ancestry (p = 6.26 × 10-23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10-9), which also replicated in independent studies (p = 3.45 × 10-4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10-20). Comparing individuals in the top GRS risk category (40%-60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.

The trans-ancestral genomic architecture of glycemic traits.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry.

PURPOSE OF REVIEW: Prevalence of type 2 diabetes (T2D) and progression of complications differ between worldwide populations. While obesity is a major contributing risk factor, variations in physiological manifestations, e.g., developing T2D at lower body mass index in some populations, suggest other contributing factors. Early T2D genetic associations were mostly discovered in European ancestry populations. This review describes the progression of genetic discoveries associated with T2D in individuals of East Asian ancestry in the last 10 years and highlights the shared genetic susceptibility between the population groups and additional insights into genetic contributions to T2D. RECENT FINDINGS: Through increased sample size and power, new genetic associations with T2D were discovered in East Asian ancestry populations, often with higher allele frequencies than European ancestry populations. As we continue to generate maps of T2D-associated variants across diverse populations, there will be a critical need to expand and diversify other omics resources to enable integration for clinical translation.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences.

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity.

Identification of type 2 diabetes loci in 433,540 East Asian individuals.

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs.

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an L 1 regularization in mDAG to ensure it can recover a large sparse DAG with limited sample size. We demonstrate through extensive simulations that mDAG outperforms two well-known methods in recovering the true underlying DAG. We apply mDAG to a cross-sectional immunological study of Chlamydia trachomatis infection and successfully infer the regularity network of cytokines. We also apply mDAG to a large cohort study, generating sensible mechanistic hypotheses underlying plasma adiponectin level. The R package mDAG is publicly available from CRAN at https://CRAN.R-project.org/package=mDAG.

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.