Bringing computation to unmet needs in Human Genomics
Based in Nashville, TN, the Below lab works to improve understanding of how genetic and other molecular factors influence human health and disease.
Overview
Dr. Below’s lab develops and applies computational methods to further understanding of the genetic and epigenetic basis of human disease. Specifically, she focuses on development of novel strategies for identifying and confirming genetic risk factors to a wide range of familial and complex traits including cardiovascular and metabolic diseases, Alzheimer’s disease and other dementias, speech and language traits, oral and tooth traits, and infectious disease (pneumonia, COVID-19) via ascertainment of dense genetic, transcriptomic, and phenotypic data. She is particularly interested in the bioinformatics methods involved in network analysis of related individuals, genomic segments shared identical by descent, large-scale meta-analyses, and genetically derived predictions of expression in large electronic health record databases linked to DNA databanks.
Recent Publications
UpSet plots of gene/protein-based findings across omics
Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.
Replication and generalization
Polygenic severe obesity (body mass index [BMI] ≥40 kg/m2) has increased, especially in Hispanic/Latino populations, yet we know little about the underlying mechanistic pathways. We analyzed whole-blood multiomics data to identify genes differentially regulated in severe obesity in Mexican Americans from the Cameron County Hispanic Cohort. Our RNA sequencing analysis identified 124 genes significantly differentially expressed between severe obesity cases (BMI ≥40 kg/m2) and controls (BMI <25 kg/m2); 33% replicated in an independent sample from the same population. Our integrative approach identified inflammatory genes, including IL4R, ZNF438, and LILRA5. Several genes displayed transcriptomic effects on severe obesity in subcutaneous adipose tissue. We further showed that the genetic regulation of these genes is associated with several traits in a large biobank, including bone fractures, obstructive sleep apnea, and hyperaldosteronism, illuminating potential risk mechanisms. Our findings furnish a molecular architecture of the severe obesity phenotype across multiple molecular domains.
p.Asp76Asn probands are distantly related
Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn,the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc)compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic dis-ease and refines the understanding of LQT5 penetrance and phenotype.