Schaid, Daniel J.

Abstract In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Introduction: Polygenic risk scores (PRS) for coronary heart disease (CHD) are associated with incident CHD events. We assessed whether the strength of this association differs over the life course. Methods: We studied 7,958 genotyped participants of European-ancestry without CHD enrolled in the Atherosclerosis Risk in Communities Study (ARIC). Participants were 45-64 years old at the baseline visit occurring 1987-89 with follow up available through 2015. A genome-wide PRS for CHD composed of 1.7 million variants was computed for each participant. Incident CHD was defined as clinically adjudicated myocardial infarction including silent infarction, coronary revascularization, or fatal coronary event. An extended Cox model was constructed with age as the model time scale. The association of PRS with incident CHD was assumed to be log-linear and further modeled as a function of age assuming differing piecewise constant effect sizes across the intervals ≤55, 55-65, 65-80, and ≥80 years. The regression model also included the following covariates: sex, systolic blood pressure, total and HDL cholesterol, history of diabetes, antihypertensive medication use, and smoking status. Results: In the ARIC cohort (median enrollment age 54, interquartile range [IQR] 49-59, female 52%), over a median of 24.5 years (IQR 14.4-28.1), there were 1,872 incident CHD events. In comparison to average polygenic risk for CHD (50th percentile of PRS distribution), harboring high polygenic risk (95th percentile) was associated with a 2.9-fold increased risk of incident CHD (95% CI 2.12-3.95) up to age 55 years, independent of conventional risk factors. The strength of association was attenuated later in life (Hazard ratio, age 55-65, 2.5 [2.14-2.84]; age 65-80, 1.87 [1.68-2.07]; age ≥80, 1.78 [1.38-2.31]) Conclusions: A genome-wide PRS for CHD is more strongly associated with incident coronary events at younger age. The strength of association declined later in life while remaining significant. The age-dependent association of PRS with CHD may need to be accounted for in risk prediction models.

Introduction. We assessed the effects of a polygenic risk score (PRS), monogenic variants (Mono + ), and family history (FamHx) on the risk of coronary heart disease (CHD) and whether incorporating a PRS into the Pooled Cohort Equations (PCE) improves risk prediction. Method. In 199,997 UK Biobank participants, we calculated a PRS CHD (metaGRS, based on ~1.6 million single nucleotide variants), identified individuals with monogenic etiology (presence of a pathogenic variant in LDLR , APOB , or PCSK9 ), and ascertained family history of CHD (CHD in a first-degree relative). Odds of CHD (myocardial infarction/ coronary revascularization/ cardiovascular death) associated with PRS CHD , Mono + , and FamHx at age 55 were computed using logistic regression analyses adjusted for age, sex, and first 4 principal components of ancestry. We combined PRS CHD and PCE into an integrated score (IS) and assessed net reclassification using an actionable risk threshold of 7.5%. Results. The odds ratio (OR) for CHD in the top 5 th percentile of PRS CHD (PRS P95 ), Mono + , and FamHx were 3.37 (2.98 - 3.81), 2.68 (1.67 - 4.07), and 1.61 (1.47 - 1.76), respectively (all p<0.001). These effects were independent and similar in different subgroups. ( Figure 1-A ) Compared to PRS Q2-4 Mono - (3 middle quintiles of PRS CHD and no monogenic variants), the OR for CHD ranged from 1.73 (0.28 - 5.48, p = 0.446) in PRS Q1 Mono + to 5.10 (2.14 - 10.24, p <0.001) in PRS Q5 Mono + . A similar pattern was seen for other combinations, indicating additive effects ( Figure 1-B ). Incorporating PRS CHD into PCE resulted in a net reclassification improvement of 4.56%. Compared to those with high PCE and low IS, the hazard ratio for CHD in those with low PCE and high IS was 1.48 (1.34 - 1.64, p <0.001). Conclusion. The CHD risk associated with PRS CHD , monogenic variants, and family history is independent and additive. Combining PRS CHD and PCE improved risk prediction, that could improve further by adding monogenic variants and family history.

Abstract Purpose: To identify molecular predictors of grade 3/4 neutropenic or leukopenic events (NLE) after chemotherapy using a genome-wide association study (GWAS). Experimental Design: A GWAS was performed on patients in the phase III chemotherapy study SUCCESS-A (n = 3,322). Genotyping was done using the Illumina HumanOmniExpress-12v1 array. Findings were functionally validated with cell culture models and the genotypes and gene expression of possible causative genes were correlated with clinical treatment response and prognostic outcomes. Results: One locus on chromosome 16 (rs4784750; NLRC5; P = 1.56E-8) and another locus on chromosome 13 (rs16972207; TNFSF13B; P = 3.42E-8) were identified at a genome-wide significance level. Functional validation revealed that expression of these two genes is altered by genotype-dependent and chemotherapy-dependent activity of two transcription factors. Genotypes also showed an association with disease-free survival in patients with an NLE. Conclusions: Two loci in NLRC5 and TNFSF13B are associated with NLEs. The involvement of the MHC I regulator NLRC5 implies the possible involvement of immuno-oncological pathways.

Background: Sequencing Mendelian arrhythmia genes in individuals without an indication for arrhythmia genetic testing can identify carriers of pathogenic or likely pathogenic (P/LP) variants. However, the extent to which these variants are associated with clinically meaningful phenotypes before or after return of variant results is unclear. In addition, the majority of discovered variants are currently classified as variants of uncertain significance, limiting clinical actionability. Methods: The eMERGE-III study (Electronic Medical Records and Genomics Phase III) is a multicenter prospective cohort that included 21 846 participants without previous indication for cardiac genetic testing. Participants were sequenced for 109 Mendelian disease genes, including 10 linked to arrhythmia syndromes. Variant carriers were assessed with electronic health record–derived phenotypes and follow-up clinical examination. Selected variants of uncertain significance (n=50) were characterized in vitro with automated electrophysiology experiments in HEK293 cells. Results: As previously reported, 3.0% of participants had P/LP variants in the 109 genes. Herein, we report 120 participants (0.6%) with P/LP arrhythmia variants. Compared with noncarriers, arrhythmia P/LP carriers had a significantly higher burden of arrhythmia phenotypes in their electronic health records. Fifty-four participants had variant results returned. Nineteen of these 54 participants had inherited arrhythmia syndrome diagnoses (primarily long-QT syndrome), and 12 of these 19 diagnoses were made only after variant results were returned (0.05%). After in vitro functional evaluation of 50 variants of uncertain significance, we reclassified 11 variants: 3 to likely benign and 8 to P/LP. Conclusions: Genome sequencing in a large population without indication for arrhythmia genetic testing identified phenotype-positive carriers of variants in congenital arrhythmia syndrome disease genes. As the genomes of large numbers of people are sequenced, the disease risk from rare variants in arrhythmia genes can be assessed by integrating genomic screening, electronic health record phenotypes, and in vitro functional studies. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier; NCT03394859.

AbstractStatistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high‐dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single‐nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.

Background: Genome-wide association studies (GWAS) have identified several common variants associated with abdominal aortic aneurysm (AAA). We investigated whether a multi-locus genetic risk score (GRS) for AAA was associated with presence and progression of AAA in a case-control study. Methods: The study comprised of 1098 patients with AAA (74±8 years, 83% men) and 6538 controls (67±10 years, 58% men) enrolled in the Mayo Vascular Disease Biorepository. AAA was defined as a transverse diameter of abdominal aorta ≥ 3.0 cm or history of AAA repair. Controls were participants without known AAA. We found five single nucleotide polymorphisms (SNPs) previously shown to be associated with AAA at GWAS significance (P≤10^-8) in NHGRI GWAS catalog and PubMed. A GRS for AAA for each individual was calculated from four SNPs (rs2383107, rs7025486, rs599839, rs1466535) that were replicated in our cohort, by summing the number of risk alleles for each SNP weighted by their estimated effect sizes in GWAS catalog or published largest meta-analysis. Results: GRS was associated with presence of AAA: odds ratio (OR) (95% confidence interval): 1.06 (1.03-1.08). The association remained significant after adjustment for age, sex, cardiovascular risk factors, and atherosclerotic cardiovascular diseases: adjusted OR: 1.05 (1.03-1.08). Further adjustment for each SNP did not attenuate association of GRS with presence of AAA (All P<0.001). GRS was not associated with family history of aortic aneurysm (P=0.4). Adding GRS to conventional risk factors improved net reclassification index by 16% (P<0.001).In a subset of patients with AAA who had sequential imaging studies (n=628), GRS was associated with AAA growth rate ≥ 1.75 mm/year (median of the cohort) after adjustment for baseline AAA size: adjusted OR: 1.07 (1.00-1.14). No conventional risk factors were associated with AAA growth. Patients with GRS > 5.24 (median of the cohort) had 1.31 times higher odds of having AAA and 1.64 times higher odds of having AAA growth rate ≥1.75 mm/year compared to those with GRS ≤ 5.24 (both P≤0.005). Conclusions: A multi-locus GRS was associated with presence of AAA and aneurysm growth, suggesting genetic predisposition to disease initiation and progression.

Thiopurine drugs such as 6‐mercaptopurine (6‐MP) and 6‐thioguanine (6‐TG) are effective immunosuppressants and antineoplastic agents. We used a genome‐wide approach to identify genes that might contribute to individual variation in thiopurine response. Basal gene expression profiles, genome‐wide SNP data, and 6‐MP and 6‐TG Cell growth inhibition (GI50) values were generated for 200 Coriell Institute “Human Variation Panel” lymphoblastoid cell lines. Gene expression and genome‐wide SNPs were correlated with GI50 values to identify candidate genes that might contribute to thiopurine response. We identified 104 expression probes and 161 SNPs, as well as 315 gene probes and 579 SNPs that correlated significantly with GI50 values for 6‐MP and 6‐TG, respectively (P<10 −5 and 10 −6 , respectively). Three genes, CD83 , TXNDC13 and SNAP23 , contained SNPs that contributed significantly to both 6‐MP and 6‐TG cytotoxicity and were also significantly associated with expression levels (P<10 −4 ), suggesting a “three‐way model” involving genotype, gene expression and cytotoxicity. GBP5 , a gene with an expression level that correlated negatively with 6‐MP and 6‐TG GI50 values, sensitized leukemia cells when overexpressed. Functional studies of the remainder of the candidate genes are being performed. These results may enhance our ability to predict thiopurine response phenotypes. NIH GM28157.