Publications by authors named "Buu Truong"

Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assess the association and classification performance of cataloged PRS for three complex traits.

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The relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS weight for a homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic tance-assisted PRS mbination Pipeline for erse Genetic ncestrie () to interpolate a harmonized PRS for diverse, especially admixed, ancestries, leveraging multiple PRS weights fine-tuned within single-ancestry samples and genetic distance. DiscoDivas treats ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries.

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Background: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.

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Background And Aims: Somatic mutations in the TET2 gene that lead to clonal haematopoiesis (CH) are associated with accelerated atherosclerosis development in mice and a higher risk of atherosclerotic disease in humans. Mechanistically, these observations have been linked to exacerbated vascular inflammation. This study aimed to evaluate whether colchicine, a widely available and inexpensive anti-inflammatory drug, prevents the accelerated atherosclerosis associated with TET2-mutant CH.

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Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assessed association and classification performance of cataloged PRS for three complex traits.

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Article Synopsis
  • Despite improvements in managing traditional risk factors, coronary artery disease (CAD) continues to be a leading cause of death, highlighting the need to understand new risk factors, including the role of the spleen.* -
  • Researchers employed deep learning and radiomics techniques on MRI data from 42,059 UK Biobank participants to identify splenic features linked to CAD, uncovering 10 relevant features and 219 genetic loci associated with these features.* -
  • The study suggests a genetic connection between the spleen and CAD, particularly with the 9p21 variant, and demonstrates how integrating deep learning with genomics can enhance our understanding of CAD mechanisms.*
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Background: Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited.

Methods: In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry.

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Background: The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD.

Methods: Participants were of European ancestry from the UK Biobank without prevalent CAD.

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Background: Coronary artery disease (CAD) is a leading cause of death among the 38.4 million people with HIV globally. The extent to which cardiovascular polygenic risk scores (PRSs) derived in non-HIV populations generalize to people with HIV is not well understood.

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Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.

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Article Synopsis
  • A study was conducted to develop a polygenic risk score (PRS) specifically for aortic stenosis and compare its effectiveness against traditional clinical risk factors.
  • This research involved analyzing data from large cohorts, including over 135,000 participants from the Million Veteran Program and various clinical trials between 2011 and 2020.
  • The findings indicated that the new aortic stenosis PRS, which utilized millions of genetic variants, may provide significant risk estimation for the condition compared to established clinical methods.
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  • The study aims to identify important cell types linked to various brain diseases by combining genome-wide association studies (GWAS) with detailed cell data from scATAC-seq and scRNA-seq.
  • Researchers analyzed data from 28 brain-related conditions and found key fetal and adult brain cell types associated with those diseases using scATAC-seq and scRNA-seq methodologies.
  • Significant findings include specific cell types associated with conditions such as major depressive disorder and schizophrenia, enhancing our understanding of brain diseases and guiding future research.
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  • Hypertensive disorders of pregnancy (HDPs), such as gestational hypertension and preeclampsia, significantly increase risks of maternal health issues and long-term cardiovascular disease across the globe.
  • The study aimed to link specific proteins in the blood to HDPs using genetic data, employing two-sample mendelian randomization for analysis.
  • Findings involved a broad dataset, including over 393,000 women for gestational hypertension and nearly 607,000 for preeclampsia, uncovering associations between 90 candidate cardiovascular-related proteins and these hypertensive disorders.
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Importance: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.

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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.

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Importance: To address systemic disparities in biomedical research, the All of Us (AoU) Research Program was created to identify the root causes and consequences of health outcomes in the US. However, the extent of AoU's racial and ethnic diversity is unknown.

Objective: To quantify representation of key racial and ethnic groups in the accruing AoU nationwide health cohort and compare with their actual representation in the US.

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Article Synopsis
  • Preeclampsia is a serious condition during pregnancy that affects mothers and babies significantly, yet there's no effective treatment available.
  • The study aimed to explore how genetic factors influence the levels of certain placental proteins in early pregnancy and their possible links to developing preeclampsia or gestational hypertension.
  • By analyzing serum samples from over 2,300 women, researchers found strong genetic associations with specific placental proteins, suggesting a complex interplay between these proteins and the risk of preeclampsia.
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The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease.

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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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Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture.

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Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects.

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Purpose: To prospectively examine the association between clonal hematopoiesis (CH) and subsequent risk of lung cancer.

Methods: Among 200,629 UK Biobank (UKBB) participants with whole-exome sequencing, CH was identified in a nested case-control study of 832 incident lung cancer cases and 3,951 controls (2006-2019) matched on age and year at blood draw, sex, race, and smoking status. A similar nested case-control study (141 cases/652 controls) was conducted among 27,975 participants with whole-exome sequencing in the Mass General Brigham Biobank (MGBB, 2010-2021).

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Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project).

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Purpose: The aim of this study was to describe a new type of medical device that allows for internet-enabled patient self-screening, without the aid of an ophthalmic professional, through biomicroscopy self-imaging and self-measurement of the best-corrected visual acuity (BCVA).

Methods: In this prospective nonrandomized comparative study, 56 patients were instructed to screen their own eyes using a custom-built e-Device containing miniaturized slitlamp optics and a visual acuity Snellen chart virtually projected at 20 ft. BCVA measurements were recorded, and biomicroscopic videos were scored for image quality of the anterior segment status on a scale from 1 to 5 (1 = poor and 5 = excellent) by a blinded observer.

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Background: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced.

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