Publications by authors named "Margaret S Selvaraj"
Article Synopsis
- This study identifies and characterizes rare coding alleles linked to genetic dyslipidemia, a major risk factor for coronary artery disease, using data from over 1.1 million individuals across various ancestries.
- It discovered 800 significant variants across 209 genes, with a notable focus on non-European populations, and included a diverse cohort of participants to enhance genetic understanding.
- The findings highlight potential therapeutic targets, particularly new genes that may help lower LDL cholesterol levels, providing valuable insights for future genetic disease research and drug development.
Background: Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort.
Methods: Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes.
Article Synopsis
- Coronary artery calcification (CAC) is linked to heart disease and assessed through a genome-wide association study (GWAS) involving 22,400 participants from various backgrounds.
- The study confirmed connections with four known genetic loci and discovered two new loci related to CAC, with supportive replication findings for both.
- Functional tests suggest that ARSE promotes calcification in vascular smooth muscle cells and its variants may influence CAC levels, identifying ARSE as a key target for potential treatments in vascular calcific diseases.
Article Synopsis
- Large-scale whole-genome sequencing (WGS) studies have enhanced our understanding of how rare genetic variants affect complex human traits through better analysis techniques.* -
- Current methods for analyzing multiple traits are limited in their ability to handle rare variants in large WGS datasets, prompting the development of MultiSTAAR.* -
- MultiSTAAR enables more powerful analysis by considering relatedness, population structure, and the correlation between traits, leading to the discovery of new genetic associations in lipid traits that single-trait analyses missed.*
Article Synopsis
- Long non-coding RNAs (lncRNAs) play crucial roles in regulating lipid metabolism and have been studied in relation to genetic variants and complex traits.
- This research utilized high-coverage whole-genome sequencing of over 66,000 diverse participants to assess how rare variants in lncRNA genes affect blood lipid levels, using a statistical framework to analyze the associations.
- The study found 83 lncRNA variants significantly linked to lipid levels, with many being independent of common genetic variations, and replicated a majority of these findings with data from another large cohort.
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy.
View Article and Find Full Text PDFRecently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R ~ 83-97%).
View Article and Find Full Text PDFArticle Synopsis
- Long non-coding RNAs (lncRNAs) play key roles in regulating biological functions, and new genomic studies allow researchers to explore their connection to complex traits, like blood lipid levels.
- This research involved high-coverage whole genome sequencing from over 66,000 participants, focusing on the influence of rare variants in 165,375 lncRNA genes on lipid variability.
- The study found 83 rare lncRNA variant sets linked to blood lipid levels, with many of these associations being independent of common variants, suggesting potential new avenues for therapeutic interventions.
Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis.
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- MetaSTAAR is a new framework designed for analyzing rare genetic variants in large studies, specifically whole genome and whole exome sequencing (WGS/WES).
- It effectively manages relatedness and population differences while analyzing various traits, enhancing the ability to detect significant rare variant associations by utilizing functional annotations.
- In tests with over 30,000 diverse samples, MetaSTAAR yielded results similar to pooled data analysis and successfully identified significant rare variant associations related to lipid traits.
Article Synopsis
- Large-scale whole-genome sequencing studies allow researchers to examine associations between rare noncoding variants and complex diseases, although current methods struggle with the noncoding genome analysis.
- The STAARpipeline framework offers a comprehensive solution for detecting noncoding rare variant associations through various analytical approaches, including gene-centric and non-gene-centric analyses that utilize functional annotations.
- The effectiveness of STAARpipeline is demonstrated through its application in identifying significant noncoding RV sets linked to lipid traits in over 21,000 samples, with successful replication in an additional group, and further analysis of other traits.
Purpose: We aimed to discover loci associated with triglyceride (TG) levels in the context of type 2 diabetes (T2D). We conducted a genome-wide association study (GWAS) in 424,120 genotyped participants of the UK Biobank (UKB) with T2D status and TG levels.
Methods: We stratified the cohort based on T2D status and conducted association analyses of TG levels for genetic variants with minor allele count (MAC) at least 20 in each stratum.
Article Synopsis
- * A study involving 66,329 participants from diverse ancestries discovered 428 million variants linked to lipid levels, many of which had not been explored in previous genetic research.
- * The research identified significant associations between blood lipid levels and both common and rare genetic variants, including a clinically significant rare non-coding variant model, enhancing understanding of lipid genetics across different populations.
Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples.
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