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Online supplementary
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Incorporating family disease history in risk prediction models with large-scale genetic data substantially reduces unexplained variation
- Wrote by Jungsoo Gim
- Last updated: 2017-07-27
About the work
In this work, a new method evaluating the posterior mean of disease risk for individuals in a pedigree is proposed
based on the liability threshold model. With the (posterior) conditional mean and important clinical features as covariates,
20,000 pre-screened genetic variants (or SNPs) are included into the penalized prediction model for type 2 diabetes (T2D).
In regularization framework, the proposed model describes the 32.5% of the T2D' variability with 5k BLUP-filtered SNPs and additional
6.3% of variation with the proposed (posterior) conditional mean. The findings in this work illustrate that the family history
can be used to provide invaluable information for disease prediction and missing heritability.
The work consists of the following steps and you can download the analysis scripts
Evaluating the posterior mean of disease risk of a subject using family history
SNP filtering based BLUP
Penalized regression
Model building
Estimating variation explained by variables using penalized logistic regression with binary phenotypes
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Authors
Jungsoo Gim, Ph.D <iedenkim@gmail.com> or <jgim80@snu.ac.kr>
Wonji Kim, Ph.D Candidate <dnjswlzz@snu.ac.kr>
Soo Heon Kwak, MD <shkwak@snu.ac.kr>
Hosik Choi, Ph.D <choi.hosik@gmail.com>
Changyi Park, Ph.D <park463@uos.ac.kr>
Kyong Soo Park, MD <kspark@snu.ac.kr>
Sunghoon Kwon, Ph.D <shkwon0522@gmail.com>
Sungho Won, Ph.D <won1@snu.ac.kr>
Wrote and maintained by Jungsoo Gim
Any comments will be welcome and send it to <iedenkim@gmail.com>.
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Download
Manuscript The initial version of the manuscript including detailed methods
Calculating the posterior mean R function evaluating the posterior mean from family information
Calculating BLUP An example R code evaluating BLUP of SNPs
SCAD R function performing SCAD penalized regression
Truncated Ridge R function performing truncated ridge penalized regression
MultiBLUP Core shell script performing MultiBLUP tool
Prediction with MultiBLUP R script performing prediction using MultiBLUP
Prediction with penalized regression R script performing prediction using various penalized regression methods
Variability estimate R script evaluating variability of penalized regression components using log-likelihood of penalized regression
R package (familyRisk) evaluating familial risk The lastest version of R package for evaluating familial disease risk
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An example script for R package
Download an example FAM file: example_family.fam
Dependency
Package dependency: tmvtnorm; kinship2
Installation
> library(devtools)
> install_github("JungsooGim/familyRisk")
Analysis
> (fam = read.table("example_family.fam", straingsAsFactor = FALSE))
> library(familyRisk)
> cal_rp(fam)
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External Links
R-package evaluating familial risk
LDAK or MultiBLUP official web-site
GCTA web-site
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Citation:
Jungsoo Gim, et al, (2017) Under revision in Genetics
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