Faculty Bibliography

Genome-wide association studies (GWASs) have identified numerous variants associated with polygenic traits and diseases. However, with few exceptions, a mechanistic understanding of which variants affect which genes in which tissues to modulate trait variation is lacking. Here, we present genomic analyses to explain trait heritability of blood pressure (BP) through the genetics of transcriptional regulation using GWASs, multiomics data from different tissues, and machine learning approaches. Approximately 500,000 predicted regulatory variants across four tissues explain 33.4% of variant heritability: 2.5%, 5.3%, 7.7%, and 11.8% for kidney-, adrenal-, heart-, and artery-specific variants, respectively. Variation in the enhancers involved shows greater tissue specificity than in the genes they regulate, suggesting that gene regulatory networks perturbed by enhancer variants in a tissue relevant to a phenotype are the major source of interindividual variation in BP. Thus, our study provides an approach to scan human tissue and cell types for their physiological contribution to any trait.

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify "high-quality" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.

Massively parallel reporter assays (MPRAs) are a high-throughput method for evaluating in vitro activities of thousands of candidate cis-regulatory elements (CREs). In these assays, candidate sequences are cloned upstream or downstream from a reporter gene tagged by unique DNA sequences. However, tag sequences may themselves affect reporter gene expression and lead to major potential biases in the measured cis-regulatory activity. Here, we present a sequence-based method for correcting tag-sequence-specific effects and show that our method can significantly reduce this source of variation and improve the identification of functional regulatory variants by MPRAs. We also show that our model captures sequence features associated with post-transcriptional regulation of mRNA. Thus, this new method helps not only to improve detection of regulatory signals in MPRA experiments but also to design better MPRA protocols.

Hundreds of loci have been associated with blood pressure traits from many genome-wide association studies. We identified an enrichment of these loci in aorta and tibial artery expression quantitative trait loci in our previous work in ~ 100 000 Genetic Epidemiology Research on Aging (GERA) study participants. In the present study, we sought to fine-map known loci and identify novel genes by determining putative regulatory regions for these and other tissues relevant to blood pressure. We constructed maps of putative cis-regulatory elements using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Variants within these regions may be evaluated quantitatively for their tissue- or cell-type-specific regulatory impact using deltaSVM functional scores, as described in our previous work. We aggregate variants within these putative cis-regulatory elements within 50Kb of the start or end of 'expressed' genes in these tissues or cell types using public expression data, and use deltaSVM scores as weights in the group-wise sequence kernel association test (SKAT) to identify candidates. We test for association with both blood pressure traits and expression within these tissues or cell types of interest, and identify the candidates MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B, and PPCDC. Additionally, we examined two known QT interval genes, SCN5A and NOS1AP, in the Atherosclerosis Risk in Communities Study (ARIC), as a positive control, and observed the expected heart-specific effect. Thus, our method identifies variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.

The rationale for genome-wide association study (GWAS) results is sequence variation in cis-regulatory elements (CREs) modulating a target gene's expression as the major cause of trait variation. To understand the complete molecular landscape of one of these GWAS loci, we performed in vitro reporter screens in cardiomyocyte cell lines for CREs overlapping nearly all common variants associated with any of five independent QT interval (QTi)-associated GWAS hits at the SCN5A-SCN10A locus. We identified 13 causal CRE variants using allelic reporter activity, cardiomyocyte nuclear extract-based binding assays, overlap with human cardiac tissue DNaseI hypersensitive regions, and predicted impact of sequence variants on DNaseI sensitivity. Our analyses identified at least one high-confidence causal CRE variant for each of the five sentinel hits that could collectively predict SCN5A cardiac gene expression and QTi association. Although all 13 variants could explain SCN5A gene expression, the highest statistical significance was obtained with seven variants (inclusive of the five above). Thus, multiple, causal, mutually associated CRE variants can underlie GWAS signals.

cis-regulatory elements (CRE), short DNA sequences through which transcription factors (TF) exert regulatory control on gene expression, are postulated to be the major sites of causal sequence variation underlying the genetics of complex traits and diseases. We present integrative analyses, combining high-throughput genomic and epigenomic data with sequence-based computations, to identify the causal transcriptional components in a given tissue. We use data on adult human hearts to demonstrate that a) sequence-based predictions detect numerous, active, tissue-specific CREs missed by experimental observations, b) learned sequence features identify the cognate TFs, c) CRE variants are specifically associated with cardiac gene expression, and, d) a significant fraction of the heritability of exemplar cardiac traits (QT interval, blood pressure, pulse rate) is attributable to these variants. This general systems approach can thus identify candidate causal variants and the components of gene regulatory networks (GRN) to enable understanding of the mechanisms of complex disorders at a tissue or cell-type basis.

Skeletal myocytes have well-established fast and slow twitch fibers with unique gene and protein specific expression patterns. By immunohistochemical staining, these show a mosaic pattern across myocytes. We hypothesized cardiac myocytes may behave similarly where some proteins are differentially expressed between mature cardiomyocytes. We utilized the tool HPASubC on over 52,000 cardiac images of the Human Protein Atlas to identify differential protein expression patterns by immunohistochemistry across the cardiomyocytes. We matched identified proteins to open chromatin and gene expression data. We identified 143 putative proteins with mosaic patterns of expression across the cardiomyocytes. We validated four of these proteins (MYL3, MYL4, PAM, and MYOM1) and demonstrated unique atrial or ventricular patterns of expression for each. Acetylation of histone H3K27 at the promoters of these four genes were consistent with the atrial/ventricular expression patterns. Despite the generally accepted homogeneity of cardiomyocytes, a small subset of proteins varies between cardiomyocytes in a mosaic pattern. This fundamental process has been previously uncharacterized. These changes may inform on different functional and disease-related activities of proteins in individual cardiomyocytes.

OBJECTIVES: Hypertension is a major risk factor for all cardiovascular diseases, especially among African Americans. This study focuses on identifying specific blood pressure (BP) genes using 15 914 individuals of African ancestry from eight cohorts (Africa America Diabetes Mellitus, Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in young Adults, Genetics Network, Genetic Epidemiology Network of Arteriopathy, Howard University Family Study, Hypertension Genetic Epidemiology Network, and Loyola University Chicago Cohort) to further genetic findings in this population which has generally been underrepresented in BP studies. METHODS: We genotyped and performed various single variant and gene-based exome-wide analyses on 15 914 individuals on the Illumina HumanExome Beadchip v1.0 or v1.1 to test association with SBP and DBP long-term average residuals that were adjusted for age, age-squared, sex, and BMI. RESULTS: We identified rare variants affecting SBP and DBP in 10 genes: AFF1, GAPDHS, SLC28A3, COL6A1, CRYBA2, KRBA1, SEL1L3, YOD1, CCDC13, and QSOX1. Prior experimental evidence for six of these 10 candidate genes supports their involvement in cardiovascular mechanisms, corroborating their potential roles in BP regulation. CONCLUSION: Although our results require replication or validation due to their low numbers of carriers, and an ethnicity-specific genotyping array may be more informative, this study, which has identified several candidate genes in this population most susceptible to hypertension, presents one of the largest African-ancestry BP studies to date and the largest including analysis of rare variants.

For most multigenic disorders, clinical manifestation (penetrance) and presentation (expressivity) are likely to be an outcome of genetic interaction between multiple susceptibility genes. Here, using gene knockouts in mice, we evaluated genetic interaction between loss of Ret and loss of Sema3d, two Hirschsprung disease susceptibility genes. We intercrossed Ret and Sema3d double null heterozygotes to generate mice with the nine possible genotypes and assessed survival by counting various genotypes, myenteric plexus presence by acetylcholinesterase staining and embryonic day 12.5 (E12.5) intestine transcriptome by RNA-sequencing. Survival rates of Ret wild-type, null heterozygote and null homozygote mice at E12.5, birth and weaning were not influenced by the genotypes at Sema3d locus and vice versa. Loss of myenteric plexus was observed only in all Ret null homozygotes, irrespective of the genotypes at Sema3d locus, and Sema3d null heterozygote and homozygote mice had normal intestinal innervation. As compared with wild-type mice intestinal gene expression, loss of Ret in null homozygotes led to differential expression of approximately 300 genes, whereas loss of Sema3d in null homozygotes had no major consequence and there was no evidence supporting major interaction between the two genes influencing intestine transcriptome. Overall, given the null alleles and phenotypic assays used, we did not find evidence for genetic interaction between Ret and Sema3d affecting survival, presence of myenteric plexus or intestine transcriptome.