An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates.

DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t-test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.

DNA methylation (DNAm)-based deconvolution provides highly accurate estimates of the proportion of each cell type in a mixed-cell type biological sample (e.g., whole-blood). These estimates can be used for examining the association between cell type proportions and biological or clinical end points; for example, comparing the estimated neutrophil proportion in whole blood between smokers and non-smokers. Cell proportion data has unique features which present challenges for traditional and widely used statistical methods. In response to this issue, our work presents two simulation studies and a real-world analysis that benchmark the performance of current standard statistical methods against an alternative method called analysis composition of microbes (ANCOM), which was originally developed for the analysis of microbiome data. In our real-world analysis we used DNAm data collected from Women's Health Initiative Long Life Study I and compared the results of each method against a gold-standard that is typically not available for these analyses. In each of our simulation studies, ANCOM was able to detect true differences in cell proportions between the groups being compared but had a much lower rate of false discovery compared with the standard statistical methods. Our real-world analysis demonstrated similar findings. Overall, our study highlights the potential of ANCOM as a powerful and robust method for analyzing DNAm-derived deconvolution estimates when the interest is comparisons of cell type proportions and biological or clinical end points. ANCOM's ability to minimize false discovery while maintaining robust statistical power positions it as a valuable addition to the epigenomic analysis toolkit.

Association of clonal hematopoiesis and mosaic chromosomal alterations with solid malignancy incidence and mortality.

BACKGROUND: Understanding the impact of clonal hematopoiesis of indeterminate potential (CHIP) and mosaic chromosomal alterations (mCAs) on solid tumor risk and mortality can shed light on novel cancer pathways. METHODS: The authors analyzed whole genome sequencing data from the Trans-Omics for Precision Medicine Women's Health Initiative study (n = 10,866). They investigated the presence of CHIP and mCA and their association with the development and mortality of breast, lung, and colorectal cancers. RESULTS: CHIP was associated with higher risk of breast (hazard ratio [HR], 1.30; 95% confidence interval [CI], 1.03-1.64; p = .02) but not colorectal (p = .77) or lung cancer (p = .32). CHIP carriers who developed colorectal cancer also had a greater risk for advanced-stage (p = .01), but this was not seen in breast or lung cancer. CHIP was associated with increased colorectal cancer mortality both with (HR, 3.99; 95% CI, 2.41-6.62; p < .001) and without adjustment (HR, 2.50; 95% CI, 1.32-4.72; p = .004) for advanced-stage and a borderline higher breast cancer mortality (HR, 1.53; 95% CI, 0.98-2.41; p = .06). Conversely, mCA (cell fraction [CF] >3%) did not correlate with cancer risk. With higher CFs (mCA >5%), autosomal mCA was associated with increased breast cancer risk (HR, 1.39; 95% CI, 1.06-1.83; p = .01). There was no association of mCA (>3%) with breast, colorectal, or lung mortality except higher colon cancer mortality (HR, 2.19; 95% CI, 1.11-4.3; p = .02) with mCA >5%. CONCLUSIONS: CHIP and mCA (CF >5%) were associated with higher breast cancer risk and colorectal cancer mortality individually. These data could inform on novel pathways that impact cancer risk and lead to better risk stratification.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints.

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

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.

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.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes.

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Enhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing.

Multiple myeloma (MM) is a heterogenous plasma cell malignancy, for which the established prognostic models exhibit limitations in capturing the full spectrum of outcome variability. Leveraging single-cell RNA-sequencing data, we developed a novel plasma cell gene signature. We evaluated and validated the associations of the resulting plasma cell malignancy (PBM) score with disease state, progression and clinical outcomes using data from five independent myeloma studies consisting of 2115 samples (1978 MM, 65 monoclonal gammopathy of undetermined significance, 35 smoldering MM, and 37 healthy controls). Overall, a higher PBM score was significantly associated with a more advanced stage within the spectrum of plasma cell dyscrasias (all p < 0.05) and a shorter overall survival in MM (hazard ratio, HR = 1.72; p < 0.001). Notably, the prognostic effect of the PBM score was independent of the International Staging System (ISS) and Revised ISS (R-ISS). The downstream analysis further linked higher PBM scores with the presence of cytogenetic abnormalities, TP53 mutations, and compositional changes in the myeloma tumor immune microenvironment. Our integrated analyses suggest the PBM score may provide an opportunity for refining risk stratification and guide decisions on therapeutic approaches to MM.

Donor germ-line variants associate with outcomes of allogeneic hematopoietic stem cell transplantation in patients with myelodysplastic syndromes.

Overall survival probability for MDS patients who underwent allo-HCT and were matched to donors that are wild-type (red) and heterozygous (blue) for the rs111224634 SNP.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Proteomics to predict relapse in patients with myelodysplastic neoplasms undergoing allogeneic hematopoietic cell transplantation.

Disease relapse remains a major barrier to success after allogeneic hematopoietic cell transplantation (allo-HCT) in myelodysplastic neoplasms (MDS). While certain high risk genomic alterations are associated with increased risk of relapse, there is a lack of clinically applicable tools to analyze the downstream cellular events that are associated with relapse. We hypothesized that unique proteomic signatures in MDS patients undergoing allo-HCT could serve as a tool to understand this aspect and predict relapse. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 52 MDS patients who underwent allo-HCT and analyzed their proteomic profile from pretransplant blood samples in a matched case-control design. Twenty-six patients without disease relapse after allo-HCT (controls) were matched with 26 patients who experienced relapse (cases). Proteomics assessment was conducted using the Slow Off-rate Modified Aptamers (SOMAmer) based assay. In gene set enrichment analysis, we noted that expression in the hallmark complement, and hallmark allograft rejection pathways were statistically enriched among patients who had disease relapse post-transplant. In addition, correlation analyses showed that methylation array probes in cis- and transcription regulatory elements of immune pathway genes were modulated and differentially sensitize the immune response. These findings suggest that proteomic analysis could serve as a novel tool for prediction of relapse after allo-HCT in MDS.

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.

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.

A General Approach to Adjusting Genetic Studies for Assortative Mating.

The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM.

Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality.

Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia.

Differences in the Circulating Proteome in Individuals with versus without Sickle Cell Trait.

BACKGROUND: Sickle cell trait affects approximately 8% of Black individuals in the United States, along with many other individuals with ancestry from malaria-endemic regions worldwide. While traditionally considered a benign condition, recent evidence suggests that sickle cell trait is associated with lower eGFR and higher risk of kidney diseases, including kidney failure. The mechanisms underlying these associations remain poorly understood. We used proteomic profiling to gain insight into the pathobiology of sickle cell trait. METHODS: We measured proteomics ( N =1285 proteins assayed by Olink Explore) using baseline plasma samples from 592 Black participants with sickle cell trait and 1:1 age-matched Black participants without sickle cell trait from the prospective Women's Health Initiative cohort. Age-adjusted linear regression was used to assess the association between protein levels and sickle cell trait. RESULTS: In age-adjusted models, 35 proteins were significantly associated with sickle cell trait after correction for multiple testing. Several of the sickle cell trait-protein associations were replicated in Black participants from two independent cohorts (Atherosclerosis Risk in Communities study and Jackson Heart Study) assayed using an orthogonal aptamer-based proteomic platform (SomaScan). Many of the validated sickle cell trait-associated proteins are known biomarkers of kidney function or injury ( e.g. , hepatitis A virus cellular receptor 1 [HAVCR1]/kidney injury molecule-1 [KIM-1], uromodulin [UMOD], ephrins), related to red cell physiology or hemolysis (erythropoietin [EPO], heme oxygenase 1 [HMOX1], and α -hemoglobin stabilizing protein) and/or inflammation (fractalkine, C-C motif chemokine ligand 2/monocyte chemoattractant protein-1 [MCP-1], and urokinase plasminogen activator surface receptor [PLAUR]). A protein risk score constructed from the top sickle cell trait-associated biomarkers was associated with incident kidney failure among those with sickle cell trait during Women's Health Initiative follow-up (odds ratio, 1.32; 95% confidence interval, 1.10 to 1.58). CONCLUSIONS: We identified and replicated the association of sickle cell trait with a number of plasma proteins related to hemolysis, kidney injury, and inflammation.

The Relationship of Duffy Gene Polymorphism, High Sensitivity C-Reactive Protein, and Long-term Outcomes.

Background: Black adults have higher incidence of all-cause death and worse cardiovascular outcomes when compared to other populations. The Duffy chemokine receptor is not expressed in a large majority of Black adults and the clinical implications of this are unclear. Methods: Here, we investigated the relationship of Duffy receptor status, high-sensitivity C-reactive protein (hs-CRP), and long-term cardiovascular outcomes in Black members of two contemporary, longitudinal cohort studies (the Jackson Heart Study and Multi-Ethnic Study of Atherosclerosis). Data on 4,307 Black participants (2,942 Duffy null and 1,365 Duffy receptor positive, as defined using Single Nucleotide Polymorphism (SNP) rs2814778) were included in this analysis. Results: Duffy null was not independently associated with elevated levels of serum hs-CRP levels once conditioning for known CRP locus alleles in linkage disequilibrium with the Duffy gene. Duffy null status was not found to be independently associated with higher incidence of all-cause mortality or secondary outcomes after adjusting for possible confounders in Black participants. Conclusions: These findings suggest that increased levels of hs-CRP found in Duffy null individuals is due to co-inheritance of CRP alleles known to influence circulating levels hs-CRP and that Duffy null status was not associated with worse adverse outcomes over the follow-up period in this cohort of well-balanced Black participants.

Genetic examination of hematological parameters in SARS-CoV-2 infection and COVID-19.

People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 related outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry.

BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.