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.

A Guide to Understanding Mendelian Randomization Studies.

Epidemiology provides a powerful framework for characterizing exposure-disease relationships, but its utility for making causal inferences is limited because epidemiologic data are observational in nature and subject to biases stemming from undetected confounding variables and reverse causation. Mendelian randomization (MR) is an increasingly popular method used to circumvent these limitations. MR uses genetic variants, or instruments, as a natural experiment to proxy an exposure, thus allowing estimation of causal effects upon an outcome that are minimally affected by the usual biases present in epidemiologic studies. Notably, MR relies on three core assumptions related to the selection of the genetic instruments, and adherence to these assumptions must be carefully evaluated to assess the validity of the causal estimates. The goal of this review is to provide readers with a basic understanding of MR studies and how to read and evaluate them. Specifically, we outline the basics of how MR analysis is conducted, the assumptions underlying instrument selection, and how to assess the quality of MR studies.

A Preliminary Study of Quantitative MRI Cartilage Loss Fraction and Its Association With Future Arthroplasty Using the Osteoarthritis Initiative Database.

Background and objective  Osteoarthritis (OA) is the most common arthritis in the world. Despite the high disease burden, there is no therapy to prevent, halt, or reverse OA, and many clinical trials relied on radiographic biomarkers for therapy response. It is important to identify patients with early OA who will eventually need arthroplasty, the end-stage treatment for osteoarthritis. This pilot study evaluates a novel MRI biomarker, cartilage loss fraction, for association with future arthroplasty and evaluates its feasibility of use and effect size estimates. Materials and methods Publicly available knee MRIs from the Osteoarthritis Initiative were used. A total of 38 participants with Kellgren-Lawrence (K-L) grade >1 and 38 participants with K-L grade ≤ 1 at enrollment were matched in age, sex, race, and BMI, and assessed for the degree of full-thickness cartilage loss, or cartilage loss fraction. Univariate conditional logistic regression analysis was performed for differences in cartilage loss fractions between groups. Receiver operating characteristic (ROC) curve analysis was performed to assess the association of MRI biomarkers and knee arthroplasty during the eight-year follow-up. Results The medial femoral condyle, medial tibial plateau, total, and two-year progression cartilage loss fractions were significantly higher in participants with K-L grade >1 (p < 0.01 for all) and showed high area under the curve (AUC) values on ROC analysis (812, 0.827, 0.917, and 0.933, respectively). These results were comparable or more strongly associated with other OA grading schemes. Conclusion MRI biomarker cartilage loss fractions are significantly higher in subjects with K-L grade >1 and show a strong association with arthroplasty. After further validation, cartilage loss fracture may be used to predict future arthroplasty.

A Linear Relationship between the Number of Cancers among First-Degree Relatives and the Risk of Multiple Primary Cancers.

With advances in the early detection and treatment of cancer, the incidence of multiple primary cancers (MPC) or second primary cancers has increased over time. Characterization of etiologic risk factors, including family history of cancer, within the general population is critical for assessing MPC risk in patients. We examined the association between family history of cancer among first-degree relatives and MPC risk in a prospective study of 139,958 participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox proportional hazard models were used to calculate HRs and 95% confidence intervals (95% CI), adjusting for potential confounders. Over a median follow-up of 16 years (IQR: 11-19 years), 6,170 participants were diagnosed with MPC. Having a family history of cancer increased the risk of MPC by 18% (HR, 1.18; 95% CI, 1.12-1.24). A positive linear trend was observed between the reported number of cancers in the family history and MPC risk with HRs (95% CI) of 1.13 (1.07-1.20), 1.23 (1.14-1.33), 1.29 (1.15-1.45), and 1.42 (1.20-1.70) for one, two, three, and four or more cancers among first-degree relatives, respectively (Ptrend = 2.36 × 10-13). No significant differences were observed by cancer histology or specific cancer types reported in the family history. Our study demonstrates that the family history of cancer is an important risk factor for the development of MPCs and that a comprehensive assessment of the number of cancers reported among first-degree relatives may identify those at higher risk who may benefit from targeted cancer prevention and screening strategies. Prevention Relevance: Our study makes a substantial contribution to the understanding of risk factors for MPCs in the general population. It demonstrates that individuals with a strong family history of cancer are at higher risk for MPCs and may benefit from more targeted cancer prevention and screening interventions.

Acute pharmacodynamic responses to sitagliptin: Drug-induced increase in early insulin secretion in oral glucose tolerance test.

DPP4 inhibitors are widely prescribed as treatments for type 2 diabetes. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses. Sitagliptin (100 mg) was administered to 47 healthy volunteers. Several endpoints were measured to assess clinically relevant responses - including the effect of sitagliptin on glucose and insulin levels during an oral glucose tolerance test (OGTT). This pilot study confirmed that sitagliptin (100 mg) decreased the area under the curve for glucose during an OGTT (p = 0.0003). Furthermore, sitagliptin promoted insulin secretion during the early portion of the OGTT as reflected by an increase in the ratio of plasma insulin at 30 min divided by plasma insulin at 60 min (T30:T60) from mean ± SEM 0.87 ± 0.05 to 1.62 ± 0.36 mU/L (p = 0.04). The magnitude of sitagliptin's effect on insulin secretion (as judged by the increase in the T30:T60 ratio for insulin) was correlated with the magnitude of sitagliptin-induced increase in the area under the curve for intact plasma GLP1 levels during the first hour of the OGTT. This study confirmed previously reported sex differences in glucose and insulin levels during an OGTT. Specifically, females exhibited higher levels of glucose and insulin at the 90-180 min time points. However, we did not detect significant sex-associated differences in the magnitude of sitagliptin-induced changes in T30:T60 ratios for either glucose or insulin. In conclusion, T30:T60 ratios for insulin and glucose during an OGTT provide useful indices to assess pharmacodynamic responses to DPP4 inhibitors.

A Linear Relationship between the Number of Cancers among First-degree Relatives and the Risk of Multiple Primary Cancers.

With advances in the early detection and treatment of cancer, the incidence of multiple primary cancers (MPC), or second primary cancers, has risen over time. Characterization of etiologic risk factors, including family history of cancer, within the general population is critical for assessing MPC risk in patients. We examined the association between family history of cancer among first-degree relatives and MPC risk in a prospective study of 139,958 participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Cox proportional hazard models were used to calculate hazard ratios (HR) and 95% confidence intervals (95%CI), adjusting for potential confounders. Over a median follow-up of 16 years (interquartile range: 11-19 years), 6,170 participants were diagnosed with MPC. Having a family history of cancer increased the risk of MPC by 18% (HR=1.18, 95%CI: 1.12-1.24). A positive linear trend was observed between the reported number of cancers in the family history and MPC risk with HRs (95%CI) of 1.13 (1.07-1.20), 1.23 (1.14-1.33), 1.29 (1.15-1.45), and 1.42 (1.20-1.70) for 1, 2, 3, and 4+ cancers among first-degree relatives, respectively (Ptrend=2.36x10-13). No significant differences were observed by cancer histology or specific types of cancer reported in the family history. Our study demonstrates that family history of cancer is an important risk factor for the development of multiple primary cancers and that a comprehensive of assessment of the number of cancers reported among first-degree relatives may identify those at higher risk who may benefit from targeted cancer prevention and screening strategies.

The Impact of Conventional Stroke Risk Factors on Early and Late Onset Ischemic Stroke: a Mendelian Randomization Study.

Objective: Although stroke incidence is decreasing in older ages, it is increasing in young adults. While these divergent trends in stroke incidence are at least partially attributable to diverging prevalence trends in stoke risk factors, age-dependent differences in the impact of stroke risk factors on stroke may also contribute. To address this issue, we utilized Mendelian Randomization (MR) to assess differences in the association of stroke risk factors between early onset ischemic stroke (EOS) and late onset ischemic stroke (LOS). Methods: We employed a two-sample MR design with inverse variance weighting as the primary method of analysis. Using large publicly available genome-wide association summary results, we calculated MR estimates for conventional stroke risk factors (body mass index, total, HDL-and LDL-cholesterol, triglycerides, type 2 diabetes, systolic and diastolic blood pressure, and smoking) in EOS cases (onset 18-59 years, n = 6,728) and controls from the Early Onset Stroke Consortium and in LOS cases (onset ≥ 60 years, n = 9,272) and controls from the Stroke Genetics Network. We then compared odds ratios between EOS and LOS, stratified by TOAST subtypes, to determine if any differences observed between effect sizes could be attributed to differences in the distribution of stroke subtypes. Results: EOS was significantly associated with all risk factors except for total cholesterol levels, and LOS was associated with all risk factors except for triglyceride and total cholesterol levels. The associations of BMI, DBP, SBP, and HDL-cholesterol were significantly stronger in EOS than LOS (all p < 0.004). The differential distribution of stroke subtypes could not explain the difference in effect size observed between EOS and LOS. Conclusion: These results suggest that interventions targeted at lowering body mass index and blood pressure may be particularly important for reducing stroke risk in young adults.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

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.