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.

Placental IGFBP1 levels during early pregnancy and the risk of insulin resistance and gestational diabetes.

Reduced insulin sensitivity (insulin resistance) is a hallmark of normal physiology in late pregnancy and also underlies gestational diabetes mellitus (GDM). We conducted transcriptomic profiling of 434 human placentas and identified a positive association between insulin-like growth factor binding protein 1 gene (IGFBP1) expression in the placenta and insulin sensitivity at ~26 weeks gestation. Circulating IGFBP1 protein levels rose over the course of pregnancy and declined postpartum, which, together with high gene expression levels in our placenta samples, suggests a placental or decidual source. Higher circulating IGFBP1 levels were associated with greater insulin sensitivity (lesser insulin resistance) at ~26 weeks gestation in the same cohort and in two additional pregnancy cohorts. In addition, low circulating IGFBP1 levels in early pregnancy predicted subsequent GDM diagnosis in two cohorts of pregnant women. These results implicate IGFBP1 in the glycemic physiology of pregnancy and suggest a role for placental IGFBP1 deficiency in GDM pathogenesis.

Novel syndromic neurodevelopmental disorder caused by de novo deletion of CHASERR, a long noncoding RNA.

Genes encoding long non-coding RNAs (lncRNAs) comprise a large fraction of the human genome, yet haploinsufficiency of a lncRNA has not been shown to cause a Mendelian disease. CHASERR is a highly conserved human lncRNA adjacent to CHD2-a coding gene in which de novo loss-of-function variants cause developmental and epileptic encephalopathy. Here we report three unrelated individuals each harboring an ultra-rare heterozygous de novo deletion in the CHASERR locus. We report similarities in severe developmental delay, facial dysmorphisms, and cerebral dysmyelination in these individuals, distinguishing them from the phenotypic spectrum of CHD2 haploinsufficiency. We demonstrate reduced CHASERR mRNA expression and corresponding increased CHD2 mRNA and protein in whole blood and patient-derived cell lines-specifically increased expression of the CHD2 allele in cis with the CHASERR deletion, as predicted from a prior mouse model of Chaserr haploinsufficiency. We show for the first time that de novo structural variants facilitated by Alu-mediated non-allelic homologous recombination led to deletion of a non-coding element (the lncRNA CHASERR) to cause a rare syndromic neurodevelopmental disorder. We also demonstrate that CHD2 has bidirectional dosage sensitivity in human disease. This work highlights the need to carefully evaluate other lncRNAs, particularly those upstream of genes associated with Mendelian disorders.

Peripheral Blood Mononuclear Cell Gene Expression Associated with Pulmonary Microvascular Perfusion: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease Study.

Rationale Chronic obstructive pulmonary disease (COPD) and emphysema are associated with endothelial damage and altered pulmonary microvascular perfusion. Molecular mechanisms underlying these changes are poorly understood in patients due, in part, to the inaccessibility of the pulmonary vasculature. Peripheral blood mononuclear cells (PBMC) interact with the pulmonary endothelium. Objective To test the association between gene expression in PBMCs and pulmonary microvascular perfusion in COPD. Methods The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study recruited two independent samples of COPD cases and controls with 10 or more pack-years. In both samples, pulmonary microvascular blood flow, pulmonary microvascular blood volume (PMBV), and mean transit time were assessed on contrast-enhanced MRI, and PBMC gene expression was assessed by microarray. Additional replication was performed in a third sample with PMBV measures on contrast-enhanced, dual-energy CT. Differential expression analyses were adjusted for age, gender, race-ethnicity, educational attainment, height, weight, smoking status, and pack-years. Results The 79 participants in the discovery sample had mean age of 69±6 years, 44% were female, 25% were non-white, 34% were current smokers and 66% had COPD. There were large PBMC gene expression signatures associated with pulmonary microvascular perfusion traits, with several replicated in the replication sets with MRI (n=47) or dual-energy CT scan (n=157) measures. Many of the identified genes are involved in inflammatory processes, including NF-κB and chemokine signaling pathways. Conclusions PBMC gene expression in NF-κB, inflammatory and chemokine signaling pathways was associated pulmonary microvascular perfusion in COPD, potentially offering new targetable candidates for novel therapies.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects.

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas.

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.

Identification of constrained sequence elements across 239 primate genomes.

Noncoding DNA is central to our understanding of human gene regulation and complex diseases1,2, and measuring the evolutionary sequence constraint can establish the functional relevance of putative regulatory elements in the human genome3-9. Identifying the genomic elements that have become constrained specifically in primates has been hampered by the faster evolution of noncoding DNA compared to protein-coding DNA10, the relatively short timescales separating primate species11, and the previously limited availability of whole-genome sequences12. Here we construct a whole-genome alignment of 239 species, representing nearly half of all extant species in the primate order. Using this resource, we identified human regulatory elements that are under selective constraint across primates and other mammals at a 5% false discovery rate. We detected 111,318 DNase I hypersensitivity sites and 267,410 transcription factor binding sites that are constrained specifically in primates but not across other placental mammals and validate their cis-regulatory effects on gene expression. These regulatory elements are enriched for human genetic variants that affect gene expression and complex traits and diseases. Our results highlight the important role of recent evolution in regulatory sequence elements differentiating primates, including humans, from other placental mammals.

Higher Maternal Body Mass Index Is Associated With Lower Placental Expression of EPYC: A Genome-Wide Transcriptomic Study.

CONTEXT: Elevated body mass index (BMI) in pregnancy is associated with adverse maternal and fetal outcomes. The placental transcriptome may elucidate molecular mechanisms underlying these associations. OBJECTIVE: We examined the association of first-trimester maternal BMI with the placental transcriptome in the Gen3G prospective cohort. METHODS: We enrolled participants at 5 to 16 weeks of gestation and measured height and weight. We collected placenta samples at delivery. We performed whole-genome RNA sequencing using Illumina HiSeq 4000 and aligned RNA sequences based on the GTEx v8 pipeline. We conducted differential gene expression analysis of over 15 000 genes from 450 placental samples and reported the change in normalized gene expression per 1-unit increase in log2 BMI (kg/m2) as a continuous variable using Limma Voom. We adjusted models for maternal age, fetal sex, gestational age at delivery, gravidity, and surrogate variables accounting for technical variability. We compared participants with BMI of 18.5 to 24.9 mg/kg2 (N = 257) vs those with obesity (BMI ≥30 kg/m2, N = 82) in secondary analyses. RESULTS: Participants' mean ± SD age was 28.2 ± 4.4 years and BMI was 25.4 ± 5.5 kg/m2 in early pregnancy. Higher maternal BMI was associated with lower placental expression of EPYC (slope = -1.94, false discovery rate [FDR]-adjusted P = 7.3 × 10-6 for continuous BMI; log2 fold change = -1.35, FDR-adjusted P = 3.4 × 10-3 for BMI ≥30 vs BMI 18.5-24.9 kg/m2) and with higher placental expression of IGFBP6, CHRDL1, and CXCL13 after adjustment for covariates and accounting for multiple testing (FDR < 0.05). CONCLUSION: Our genome-wide transcriptomic study revealed novel genes potentially implicated in placental biologic response to higher maternal BMI in early pregnancy.

Placental RNA sequencing implicates IGFBP1 in insulin sensitivity during pregnancy and in gestational diabetes.

Reduced insulin sensitivity (or greater insulin resistance) is a hallmark of normal physiology in late pregnancy and also underlies gestational diabetes mellitus (GDM) pathophysiology. We conducted transcriptomic profiling of 434 human placentas and identified a strong positive association between insulin-like growth factor binding protein 1 gene (IGFBP1) expression in the placenta and insulin sensitivity at ~ 26 weeks' gestation. Circulating IGFBP1 protein levels rose over the course of pregnancy and declined postpartum, which together with high placental gene expression levels, suggests a placental source. Higher circulating IGFBP1 levels were strongly associated with greater insulin sensitivity (lesser insulin resistance) at ~ 26 weeks' gestation in the same cohort and two additional pregnancy cohorts. In addition, low circulating IGFBP1 levels in early pregnancy predicted subsequent GDM diagnosis in two cohorts. These results implicate IGFBP1 in the glycemic physiology of pregnancy and suggest a role for placental IGFBP1 deficiency in GDM pathogenesis.

The functional impact of rare variation across the regulatory cascade.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease.

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE-gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP-ARE-gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

Multiset correlation and factor analysis enables exploration of multi-omics data.

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Pan-cancer proteogenomics connects oncogenic drivers to functional states.

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation.

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXL1-associated CVD risk. We show that CRISPR-induced Asxl1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVD-protective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects.

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.