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.

Maternal stressful life events during pregnancy and childhood asthma and wheeze.

BACKGROUND: Studies have linked prenatal maternal psychosocial stress to childhood wheeze/asthma but have rarely investigated factors that may mitigate risks. OBJECTIVE: To investigate associations between prenatal stress and childhood wheeze/asthma, evaluating factors that may modify stress effects. METHODS: Participants included 2056 mother-child dyads from Environmental influences on Child Health Outcomes (ECHO)-PATHWAYS, a consortium of 3 prospective pregnancy cohorts (the Conditions Affecting Neurocognitive Development and Learning in Early Childhood study, The Infant Development and Environment Study, and a subset of the Global Alliance to Prevent Prematurity and Stillbirth study) from 6 cities. Maternal stressful life events experienced during pregnancy (PSLEs) were reported using the Pregnancy Risk Assessment Monitoring System Stressful Life Events questionnaire. Parents reported child wheeze/asthma outcomes at age 4 to 6 years using standardized questionnaires. We defined outcomes as ever asthma, current wheeze, current asthma, and strict asthma. We used modified Poisson regression with robust standard errors (SEs) to estimate risk ratios (RRs) and 95% CI per 1-unit increase in PSLE, adjusting for confounders. We evaluated effect modification by child sex, maternal history of asthma, maternal childhood traumatic life events, neighborhood-level resources, and breastfeeding. RESULTS: Overall, we observed significantly elevated risk for current wheeze with increasing PSLE (RR, 1.09 [95% CI, 1.03-1.14]), but not for other outcomes. We observed significant effect modification by child sex for strict asthma (P interaction = .03), in which risks were elevated in boys (RR, 1.10 [95% CI, 1.02-1.19]) but not in girls. For all other outcomes, risks were significantly elevated in boys and not in girls, although there was no statistically significant evidence of effect modification. We observed no evidence of effect modification by other factors (P interactions > .05). CONCLUSION: Risk of adverse childhood respiratory outcomes is higher with increasing maternal PSLEs, particularly in boys.

WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

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.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Prenatal exposure to polycyclic aromatic hydrocarbons is not associated with behavior problems in preschool and early school-aged children: A prospective multi-cohort study.

BACKGROUND: Epidemiological study findings are inconsistent regarding associations between prenatal polycyclic aromatic hydrocarbon (PAH) exposures and childhood behavior. This study examined associations of prenatal PAH exposure with behavior at age 4-6 years in a large, diverse, multi-region prospective cohort. Secondary aims included examination of PAH mixtures and effect modification by child sex, breastfeeding, and child neighborhood opportunity. METHODS: The ECHO PATHWAYS Consortium pooled 1118 mother-child dyads from three prospective pregnancy cohorts in six U.S. cities. Seven PAH metabolites were measured in prenatal urine. Child behavior was assessed at age 4-6 using the Total Problems score from the Child Behavior Checklist (CBCL). Neighborhood opportunity was assessed using the socioeconomic and educational scales of the Child Opportunity Index. Multivariable linear regression was used to estimate associations per 2-fold increase in each PAH metabolite, adjusted for demographic, prenatal, and maternal factors and using interaction terms for effect modifiers. Associations with PAH mixtures were estimated using Weighted Quantile Sum Regression (WQSR). RESULTS: The sample was racially and sociodemographically diverse (38% Black, 49% White, 7% Other; household-adjusted income range $2651-$221,102). In fully adjusted models, each 2-fold increase in 2-hydroxynaphthalene was associated with a lower Total Problems score, contrary to hypotheses (b = -0.80, 95% CI = -1.51, -0.08). Associations were notable in boys (b = -1.10, 95% CI = -2.11, -0.08) and among children breastfed 6+ months (b = -1.31, 95% CI = -2.25, -0.37), although there was no statistically significant evidence for interaction by child sex, breastfeeding, or neighborhood child opportunity. Associations were null for other PAH metabolites; there was no evidence of associations with PAH mixtures from WQSR. CONCLUSION: In this large, well-characterized, prospective study of mother-child pairs, prenatal PAH exposure was not associated with child behavior problems. Future studies characterizing the magnitude of prenatal PAH exposure and studies in older childhood are needed.

Placental transcriptomic signatures of spontaneous preterm birth.

BACKGROUND: Spontaneous preterm birth accounts for most preterm births and leads to significant morbidity in the newborn and childhood period. This subtype of preterm birth represents an increasing proportion of all preterm births when compared with medically indicated preterm birth, yet it is understudied in omics analyses. The placenta is a key regulator of fetal and newborn health, and the placental transcriptome can provide insight into pathologic changes that lead to spontaneous preterm birth. OBJECTIVE: This analysis aimed to identify genes for which placental expression was associated with spontaneous preterm birth (including early preterm and late preterm birth). STUDY DESIGN: The ECHO PATHWAYS consortium extracted RNA from placental samples collected from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood and the Global Alliance to Prevent Prematurity and Stillbirth studies. Placental transcriptomic data were obtained by RNA sequencing. Linear models were fit to estimate differences in placental gene expression between term birth and spontaneous preterm birth (including gestational age subgroups defined by the American College of Obstetricians and Gynecologists). Models were adjusted for numerous confounding variables, including labor status, cohort, and RNA sequencing batch. This analysis excluded patients with induced labor, chorioamnionitis, multifetal gestations, or medical indications for preterm birth. Our combined cohort contained gene expression data for 14,023 genes in 48 preterm and 540 term samples. Genes and pathways were considered statistically significantly different at false discovery rate-adjusted P value of <.05. RESULTS: In total, we identified 1728 genes for which placental expression was associated with spontaneous preterm birth with more differences in expression in early preterm samples than late preterm samples when compared with full-term samples. Of those, 9 genes were significantly decreased in both early and late spontaneous preterm birth, and the strongest associations involved placental expression of IL1B, ALPL, and CRLF1. In early and late preterm samples, we observed decreased expression of genes involved in immune signaling, signal transduction, and endocrine function. CONCLUSION: This study provides a comprehensive assessment of the differences in the placental transcriptome associated with spontaneous preterm birth with robust adjustment for confounding. Results of this study are in alignment with the known etiology of spontaneous preterm birth, because we identified multiple genes and pathways for which the placental and chorioamniotic membrane expression was previously associated with prematurity, including IL1B. We identified decreased expression in key signaling pathways that are essential for placental growth and function, which may be related to the etiology of spontaneous preterm birth. We identified increased expression of genes within metabolic pathways associated exclusively with early preterm birth. These signaling and metabolic pathways may provide clinically targetable pathways and biomarkers. The findings presented here can be used to understand underlying pathologic changes in premature placentas, which can inform and improve clinical obstetrics practice.

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.