Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

Clustering of gene expression patterns.

Kenneth A. Field (488614)
Joseph S. Johnson (297737)
Thomas M. Lilley (802265)
Sophia M. Reeder (802266)
Elizabeth J. Rogers (802267)
Melissa J. Behr (248000)
DeeAnn M. Reeder (157774)

October 2015

<p>(A) Bootstrap analysis of regularized-log transformed counts to assign confidence levels to sampl...

Generalized Adaptive Shrinkage Methods and Applications in Genomics Studies

Lu, Mengyin

April 2019

Shrinkage procedures have played an important role in helping improve estimation accuracy for a vari...

Some New Theories and Applications on L1 Shrinkage Estimation

Liu, Hongmei

August 2017

In this thesis, I develop some new variable selection and statistical modeling techniques in the fra...

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Araujo, Daniel S
Nguyen, Chris
Hu, Xiaowei
Mikhaylova, Anna V
Gignoux, Chris
Ardlie, Kristin
Taylor, Kent D
Durda, Peter
Liu, Yongmei
Papanicolaou, George
Cho, Michael H
Rich, Stephen S
Rotter, Jerome I
NHLBI TOPMed Consortium
Im, Hae Kyung
Manichaikul, Ani
Wheeler, Heather E

January 2023

This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as...

Multivariate Adaptive Shrinkage Improves Cross-Population Transcriptome Prediction and Association Studies in Underrepresented Populations

Araujo, Daniel
Nguyen, Chris
Hu, Xiaowei
Mikhaylova, Anna V.
Gignoux, Christopher R.
Ardlie, Kristin
Taylor, Kent D.
Durda, Peter
Liu, Yongmei
Papanicolaou, George
Cho, Michael H.
Rich, Stephen S.
Rotter, Jerome I.
NHLBI TOPMed Consortium
Im, Hae Kyung
Manichaikul, Ani
Wheeler, Heather

October 2023

Transcriptome prediction models built with data from European-descent individuals are less accurate ...

Population Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate

Geoffroy, E (via Mendeley Data)

November 2020

Here are the input datasets for the paper "Population Matched Transcriptome Prediction Increases TWA...

Transcriptome prediction performance across machine learning models and diverse ancestries

Okoro, Paul C
Schubert, Ryan
Guo, Xiuqing
Johnson, W Craig
Rotter, Jerome I
Hoeschele, Ina
Liu, Yongmei
Im, Hae Kyung
Luke, Amy
Dugas, Lara R
Wheeler, Heather E

April 2021

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait m...

Cross-population Transcriptome Prediction Models for Transcriptome-Wide Association Studies in Diverse Populations

Nguyen, Christopher
Wheeler, Dr. Heather
Araujo, Daniel

April 2022

Genome-wide association studies (GWAS) have been utilized to find associations between single nucleo...

Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries

Okoro, Paul Chukwuebuka
Schubert, Ryan
Guo, Xiuqing
Johnson, W. Craig
Rotter, Jerome I.
Hoeschele, Ina
Liu, Yongmei
Kyung Im, Hae
Luke, Amy
Dugas, Lara R
Wheeler, Heather

April 2021

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait m...

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

Rezaee Oshternian, S
Loipfinger, S
Bhattacharya, A
Fehrmann, R.S.N

June 2023

This repository contains supplementary materials for the research paper "Exploring combinations of d...

Building and Testing Expression Models

Nguyen, Christopher

April 2021

When analyzing complex traits among populations, statistical methods such as genome-wide association...

Statistical Methods for Transcriptome Data

Kim, Tae Hyun

September 2020

Transcriptome data provides key information about molecular mechanism for phenotypic diversity. Adva...

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Pividori, Milton
Rajagopal, Padma S.
Barbeira, Alvaro N.
Liang, Yanyu
Melia, Owen
Bastarache, Lisa
Park, YoSon
The GTEx Consortium
Wen, Xiaoquan
Im, Hae Kyung

February 2020

Data generated as part of the PhenomeXcan project, which contains processed results for S-MultiXcan,...

Data_Sheet_1_Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations.pdf

Anna V. Mikhaylova (6547499)
Timothy A. Thornton (3987395)

April 2019

Using genetic data to predict gene expression has garnered significant attention in recent years. Pr...

Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries. Data analysis code repository.

Yanyu Liang

November 2021

This is a release for zenodo containing data analysis code for paper "Polygenic transcriptome risk s...

Clustering of gene expression patterns.

Kenneth A. Field (488614)
Joseph S. Johnson (297737)
Thomas M. Lilley (802265)
Sophia M. Reeder (802266)
Elizabeth J. Rogers (802267)
Melissa J. Behr (248000)
DeeAnn M. Reeder (157774)

October 2015

<p>(A) Bootstrap analysis of regularized-log transformed counts to assign confidence levels to sampl...

Generalized Adaptive Shrinkage Methods and Applications in Genomics Studies

Lu, Mengyin

April 2019

Shrinkage procedures have played an important role in helping improve estimation accuracy for a vari...

Some New Theories and Applications on L1 Shrinkage Estimation

Liu, Hongmei

August 2017

In this thesis, I develop some new variable selection and statistical modeling techniques in the fra...

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Araujo, Daniel S
Nguyen, Chris
Hu, Xiaowei
Mikhaylova, Anna V
Gignoux, Chris
Ardlie, Kristin
Taylor, Kent D
Durda, Peter
Liu, Yongmei
Papanicolaou, George
Cho, Michael H
Rich, Stephen S
Rotter, Jerome I
NHLBI TOPMed Consortium
Im, Hae Kyung
Manichaikul, Ani
Wheeler, Heather E

January 2023

This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as...

Multivariate Adaptive Shrinkage Improves Cross-Population Transcriptome Prediction and Association Studies in Underrepresented Populations

Araujo, Daniel
Nguyen, Chris
Hu, Xiaowei
Mikhaylova, Anna V.
Gignoux, Christopher R.
Ardlie, Kristin
Taylor, Kent D.
Durda, Peter
Liu, Yongmei
Papanicolaou, George
Cho, Michael H.
Rich, Stephen S.
Rotter, Jerome I.
NHLBI TOPMed Consortium
Im, Hae Kyung
Manichaikul, Ani
Wheeler, Heather

October 2023

Transcriptome prediction models built with data from European-descent individuals are less accurate ...

Population Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate

Geoffroy, E (via Mendeley Data)

November 2020

Here are the input datasets for the paper "Population Matched Transcriptome Prediction Increases TWA...

Transcriptome prediction performance across machine learning models and diverse ancestries

Okoro, Paul C
Schubert, Ryan
Guo, Xiuqing
Johnson, W Craig
Rotter, Jerome I
Hoeschele, Ina
Liu, Yongmei
Im, Hae Kyung
Luke, Amy
Dugas, Lara R
Wheeler, Heather E

April 2021

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait m...

Cross-population Transcriptome Prediction Models for Transcriptome-Wide Association Studies in Diverse Populations

Nguyen, Christopher
Wheeler, Dr. Heather
Araujo, Daniel

April 2022

Genome-wide association studies (GWAS) have been utilized to find associations between single nucleo...

Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries

Okoro, Paul Chukwuebuka
Schubert, Ryan
Guo, Xiuqing
Johnson, W. Craig
Rotter, Jerome I.
Hoeschele, Ina
Liu, Yongmei
Kyung Im, Hae
Luke, Amy
Dugas, Lara R
Wheeler, Heather

April 2021

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait m...

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

Rezaee Oshternian, S
Loipfinger, S
Bhattacharya, A
Fehrmann, R.S.N

June 2023

This repository contains supplementary materials for the research paper "Exploring combinations of d...

Building and Testing Expression Models

Nguyen, Christopher

April 2021

When analyzing complex traits among populations, statistical methods such as genome-wide association...

Statistical Methods for Transcriptome Data

Kim, Tae Hyun

September 2020

Transcriptome data provides key information about molecular mechanism for phenotypic diversity. Adva...

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Pividori, Milton
Rajagopal, Padma S.
Barbeira, Alvaro N.
Liang, Yanyu
Melia, Owen
Bastarache, Lisa
Park, YoSon
The GTEx Consortium
Wen, Xiaoquan
Im, Hae Kyung

February 2020

Data generated as part of the PhenomeXcan project, which contains processed results for S-MultiXcan,...

Data_Sheet_1_Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations.pdf

Anna V. Mikhaylova (6547499)
Timothy A. Thornton (3987395)

April 2019

Using genetic data to predict gene expression has garnered significant attention in recent years. Pr...

Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries. Data analysis code repository.

Yanyu Liang

November 2021

This is a release for zenodo containing data analysis code for paper "Polygenic transcriptome risk s...

Clustering of gene expression patterns.

Kenneth A. Field (488614)
Joseph S. Johnson (297737)
Thomas M. Lilley (802265)
Sophia M. Reeder (802266)
Elizabeth J. Rogers (802267)
Melissa J. Behr (248000)
DeeAnn M. Reeder (157774)

October 2015

<p>(A) Bootstrap analysis of regularized-log transformed counts to assign confidence levels to sampl...

Generalized Adaptive Shrinkage Methods and Applications in Genomics Studies

Lu, Mengyin

April 2019

Shrinkage procedures have played an important role in helping improve estimation accuracy for a vari...

Some New Theories and Applications on L1 Shrinkage Estimation

Liu, Hongmei

August 2017

In this thesis, I develop some new variable selection and statistical modeling techniques in the fra...

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

Abstract

Extracted data

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

Abstract

Extracted data

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