scholarly journals Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing

2011 ◽  
pp. n/a-n/a ◽  
Author(s):  
Wei Pan
Keyword(s):  
Genomic Distance ◽  
Kernel Machine ◽  
Association Testing ◽  
Kernel Machine Regression

scholarly journals MiRKAT: kernel machine regression-based global association tests for the microbiome

2020 ◽  
Author(s):  
Nehemiah Wilson ◽  
Ni Zhao ◽  
Xiang Zhan ◽  
Hyunwook Koh ◽  
Weijia Fu ◽  
...  
Keyword(s):  
R Package ◽  
Effect Sizes ◽  
Supplementary Information ◽  
Time To Event ◽  
Kernel Machine ◽  
Association Testing ◽  
Higher Power ◽  
Kernel Machine Regression ◽  
Two Measures ◽  
Rv Coefficient

Abstract Summary Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. Availability and implementation MiRKAT is available on CRAN as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

2018 ◽  
Vol 29 (4) ◽  
pp. e2504 ◽  
Author(s):  
Shelley H. Liu ◽  
Jennifer F. Bobb ◽  
Birgit Claus Henn ◽  
Lourdes Schnaas ◽  
Martha M. Tellez-Rojo ◽  
...  
Keyword(s):  
Health Effects ◽  
Mean Field ◽  
Variational Bayes ◽  
Time Varying ◽  
Kernel Machine ◽  
Kernel Machine Regression ◽  
Environmental Mixtures

Abstract 077: In Utero Exposure To Metal Mixtures And Offspring Blood Pressure: An Analysis Of The Boston Birth Cohort Using Bayesian Kernel Machine Regression

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Mingyu Zhang ◽  
Tiange Liu ◽  
Guoying Wang ◽  
Jessie P Buckley ◽  
Eliseo Guallar ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Birth Cohort ◽  
In Utero ◽  
Linear Regression Models ◽  
Metal Mixtures ◽  
Kernel Machine ◽  
American Academy Of Pediatrics ◽  
Kernel Machine Regression ◽  
The Individual ◽  
Antioxidant Effects

Background: In utero exposure to metals lead (Pb), cadmium (Cd), and mercury (Hg) may be associated with higher childhood systolic blood pressure (SBP), while trace elements manganese (Mn) and selenium (Se) may have protective, antioxidant effects that modify metal-SBP associations. No study has examined how in utero co-exposure to these metals affect offspring SBP. Objectives: To examine the individual and joint effects of in utero exposure to Cd, Pb, Hg, Mn, and Se on offspring SBP. Methods: We used data from the Boston Birth Cohort (enrolled 2002-2013). We measured metals in maternal red blood cells collected 24-72 hours after delivery. We calculated child age-, sex-, and height-specific SBP percentile per 2017 American Academy of Pediatrics guidelines. We used linear regression models to estimate associations of each metal, and Bayesian kernel machine regression (BKMR) to examine metal co-exposures, with child SBP between 3 to 15 years of age. Results: Our analytic sample comprised 1194 mother-child pairs (61% Black, 20% Hispanic). Hg and Pb were not associated with child SBP. Se and Mn were inversely associated with child SBP: each log2(Se) and log2(Mn) increment was associated with a 6.23 (95% CI: 0.96-11.51) and a 2.62 (95% CI: 0.04-5.20) percentile lower child SBP, respectively. BKMR models showed similar results ( Panel A ). While Cd was not overall associated with child SBP, there was an antagonistic interaction between Cd and Mn (P-interaction = 0.036): the association of Mn and lower child SBP was stronger with higher levels of Cd ( Panel B ). Consistent with this finding, in utero exposure to cigarette smoke (a major source of Cd) modified the association of Mn and child SBP: among children born mothers who smoked cigarette in pregnancy, each log2(Mn) increment was associated with a 10.09 (95% CI: 2.15-18.03) percentile lower SBP ( Panel C ). Conclusion: Optimizing in utero Se levels, as well as Mn levels in pregnant women who had high Cd or smoked during pregnancy, may protect offspring from developing high BP during childhood.

scholarly journals Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

Genetics ◽  
2015 ◽  
Vol 201 (4) ◽  
pp. 1329-1339 ◽  
Author(s):  
Qi Yan ◽  
Daniel E. Weeks ◽  
Juan C. Celedón ◽  
Hemant K. Tiwari ◽  
Bingshan Li ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Regression Method ◽  
Kernel Machine ◽  
Kernel Machine Regression

scholarly journals Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249236
Author(s):  
Lauren Hoskovec ◽  
Wande Benka-Coker ◽  
Rachel Severson ◽  
Sheryl Magzamen ◽  
Ander Wilson
Keyword(s):  
Health Outcomes ◽  
Response Function ◽  
Simulation Study ◽  
Linear Models ◽  
Model Choice ◽  
Chemical Mixtures ◽  
Kernel Machine ◽  
Exposure Response ◽  
Kernel Machine Regression ◽  
Children With Asthma

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

scholarly journals Bayesian kernel machine regression‐causal mediation analysis

2022 ◽  
Author(s):  
Katrina L. Devick ◽  
Jennifer F. Bobb ◽  
Maitreyi Mazumdar ◽  
Birgit Claus Henn ◽  
David C. Bellinger ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Kernel Machine ◽  
Causal Mediation ◽  
Kernel Machine Regression ◽  
Causal Mediation Analysis

scholarly journals Associations of Plasma Folate and Vitamin B6 With Blood DNA Methylation Age: An Analysis of One-Carbon Metabolites in the VA Normative Aging Study

2020 ◽  
Author(s):  
Jamaji C Nwanaji-Enwerem ◽  
Elena Colicino ◽  
Xu Gao ◽  
Cuicui Wang ◽  
Pantel Vokonas ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Vitamin B6 ◽  
Regression Models ◽  
Biological Aging ◽  
Kernel Machine ◽  
Normative Aging Study ◽  
Kernel Machine Regression ◽  
Aging Study ◽  
Dna Methylation Age ◽  
Normative Aging

Abstract One-carbon metabolism is an important contributor to aging-related diseases; nevertheless, relationships of one-carbon metabolites with novel DNA methylation-based measures of biological aging remain poorly characterized. We examined relationships of one-carbon metabolites with 3 DNA methylation-based measures of biological aging: DNAmAge, GrimAge, and PhenoAge. We measured plasma levels of 4 common one-carbon metabolites (vitamin B6, vitamin B12, folate, and homocysteine) in 715 VA Normative Aging Study participants with at least 1 visit between 1999 and 2008 (observations = 1153). DNA methylation age metrics were calculated using the HumanMethylation450 BeadChip. We utilized Bayesian Kernel Machine Regression models adjusted for chronological age, lifestyle factors, age-related diseases, and study visits to determine metabolites important to the aging outcomes. Bayesian Kernel Machine Regression models allowed for the estimation of the relationships of single metabolites and the cumulative metabolite mixture with methylation age. Log vitamin B6 was selected as important to PhenoAge (β = −1.62 years, 95% CI: −2.28, −0.96). Log folate was selected as important to GrimAge (β = 0.75 years, 95% CI: 0.41, 1.09) and PhenoAge (β = 1.62 years, 95% CI: 0.95, 2.29). Compared to a model where each metabolite in the mixture is set to its 50th percentile, the log cumulative mixture with each metabolite at its 30th (β = −0.13 years, 95% CI: −0.26, −0.005) and 40th percentile (β = −0.06 years, 95% CI: −0.11, −0.005) was associated with decreased GrimAge. Our results provide novel characterizations of the relationships between one-carbon metabolites and DNA methylation age in a human population study. Further research is required to confirm these findings and establish their generalizability.

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