AI Article Synopsis
- Genome-wide association studies (GWAS) have gained traction due to cheaper genotyping technologies, leading to the development of the multitrait multilocus (MTML) framework for analyzing multiple genetic traits simultaneously.
- The MTML framework improves upon previous models by simplifying calculations, reducing complexity, and combining contributions of individual markers to multiple traits, resulting in more powerful tests for detecting genetic associations.
- Simulation results indicate that MTML outperforms existing methods in identifying quantitative trait nucleotides and controlling error rates, while real data from Arabidopsis thaliana demonstrates its effectiveness in discovering pleiotropic genetic associations.
Article Abstract
Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/bimj.202300130 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Polygenic risk score portability for common diseases across genetically diverse populations.
Hum Genomics
September 2024
Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA.
Background: Polygenic risk scores (PRS) derived from European individuals have reduced portability across global populations, limiting their clinical implementation at worldwide scale. Here, we investigate the performance of a wide range of PRS models across four ancestry groups (Africans, Europeans, East Asians, and South Asians) for 14 conditions of high-medical interest.
Methods: To select the best-performing model per trait, we first compared PRS performances for publicly available scores, and constructed new models using different methods (LDpred2, PRS-CSx and SNPnet).
MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test.
Biom J
September 2024
School of Mathematics and Statistics, Qingdao University, Qingdao, China.
Article Synopsis
- Genome-wide association studies (GWAS) have gained traction due to cheaper genotyping technologies, leading to the development of the multitrait multilocus (MTML) framework for analyzing multiple genetic traits simultaneously.
- The MTML framework improves upon previous models by simplifying calculations, reducing complexity, and combining contributions of individual markers to multiple traits, resulting in more powerful tests for detecting genetic associations.
- Simulation results indicate that MTML outperforms existing methods in identifying quantitative trait nucleotides and controlling error rates, while real data from Arabidopsis thaliana demonstrates its effectiveness in discovering pleiotropic genetic associations.
MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.
Molecules
January 2023
CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain.
Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP).
View Article and Find Full Text PDFValidating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research.
Hum Genomics
September 2022
Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA.
Introduction: A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate.
View Article and Find Full Text PDFGenetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran Program.
Circulation
October 2020
VA Palo Alto Health Care System (C.T., J.M.S., T.L.A., P.S.T.), CA.
Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability.
Methods: We performed a genome-wide association study in the Million Veteran Program testing ≈18 million DNA sequence variants with AAA (7642 cases and 172 172 controls) in veterans of European ancestry with independent replication in up to 4972 cases and 99 858 controls.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!