Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model
Genetic Epidemiology
by Georg Hahn, Dmitry Prokopenko, Julian Hecker, Sharon M. Lutz, Kristina Mullin, Rudolph E. Tanzi, Stacia DeSantis, Christoph Lange
1w ago
Abstract The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defin ..read more
Visit website
Proteome‐wide association study using cis and trans variants and applied to blood cell and lipid‐related traits in the Women's Health Initiative study
Genetic Epidemiology
by Brian D. Chen, Chanhwa Lee, Amanda L. Tapia, Alexander P. Reiner, Hua Tang, Charles Kooperberg, JoAnn E. Manson, Yun Li, Laura M. Raffield
3w ago
Abstract In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's He ..read more
Visit website
Issue Information
Genetic Epidemiology
by
3w ago
Genetic Epidemiology, Volume 48, Issue 5, Page 201-202, July 2024 ..read more
Visit website
Hierarchical joint analysis of marginal summary statistics—Part II: High‐dimensional instrumental analysis of omics data
Genetic Epidemiology
by Lai Jiang, Jiayi Shen, Burcu F. Darst, Christopher A. Haiman, Nicholas Mancuso, David V. Conti
1M ago
Abstract Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of c ..read more
Visit website
Interpreting disease genome‐wide association studies and polygenetic risk scores given eligibility and study design considerations
Genetic Epidemiology
by Catherine Mary Schooling, Mary Beth Terry
1M ago
Abstract Genome-wide association studies (GWAS) have been helpful in identifying genetic variants predicting cancer risk and providing new insights into cancer biology. Increasing use of genetically informed care, as well as genetically informed prevention and treatment strategies, have also drawn attention to some of the inherent limitations of cancer genetic data. Specifically, genetic endowment is lifelong. However, those recruited into cancer studies tend to be middle-aged or older people, meaning the exposure most likely starts before recruitment, as opposed to exposure and recruitment al ..read more
Visit website
Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma
Genetic Epidemiology
by Diptavo Dutta, Ananda Sen, Jaya M. Satagopan
2M ago
Abstract Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA ..read more
Visit website
Issue Information
Genetic Epidemiology
by
2M ago
Genetic Epidemiology, Volume 48, Issue 4, Page 149-150, June 2024 ..read more
Visit website
Identifying somatic fingerprints of cancers defined by germline and environmental risk factors
Genetic Epidemiology
by Saptarshi Chakraborty, Zoe Guan, Caroline E. Kostrzewa, Ronglai Shen, Colin B. Begg
2M ago
Abstract Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the ..read more
Visit website
Meta‐analysis of breast cancer risk for individuals with PALB2 pathogenic variants
Genetic Epidemiology
by Thanthirige L. M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas
3M ago
Abstract Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these ..read more
Visit website
A novel application of data‐consistent inversion to overcome spurious inference in genome‐wide association studies
Genetic Epidemiology
by Negar Janani, Kendra A. Young, Greg Kinney, Matthew Strand, John E. Hokanson, Yaning Liu, Troy Butler, Erin Austin
3M ago
Abstract The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants ..read more
Visit website

Follow Genetic Epidemiology on FeedSpot

Continue with Google
Continue with Apple
OR