A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Research output: Working paper › Preprint › Research

Standard

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. / Li, Xihao; Chen, Han; Selvaraj, Margaret Sunitha; Van Buren, Eric; Zhou, Hufeng; Wang, Yuxuan; Sun, Ryan; McCaw, Zachary R; Yu, Zhi; Arnett, Donna K; Bis, Joshua C; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Carson, April P; Carlson, Jenna C; Chami, Nathalie; Chen, Yii-Der Ida; Curran, Joanne E; de Vries, Paul S; Fornage, Myriam; Franceschini, Nora; Freedman, Barry I; Gu, Charles; Heard-Costa, Nancy L; He, Jiang; Hou, Lifang; Hung, Yi-Jen; Irvin, Marguerite R; Kaplan, Robert C; Kardia, Sharon L R; Kelly, Tanika; Konigsberg, Iain; Kooperberg, Charles; Kral, Brian G; Li, Changwei; Loos, Ruth J F; Mahaney, Michael C; Martin, Lisa W; Mathias, Rasika A; Minster, Ryan L; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium.

bioRxiv, 2023.

Research output: Working paper › Preprint › Research

Harvard

Li, X, Chen, H, Selvaraj, MS, Van Buren, E, Zhou, H, Wang, Y, Sun, R, McCaw, ZR, Yu, Z, Arnett, DK, Bis, JC, Blangero, J, Boerwinkle, E, Bowden, DW, Brody, JA, Cade, BE, Carson, AP, Carlson, JC, Chami, N, Chen, Y-DI, Curran, JE, de Vries, PS, Fornage, M, Franceschini, N, Freedman, BI, Gu, C, Heard-Costa, NL, He, J, Hou, L, Hung, Y-J, Irvin, MR, Kaplan, RC, Kardia, SLR, Kelly, T, Konigsberg, I, Kooperberg, C, Kral, BG, Li, C, Loos, RJF, Mahaney, MC, Martin, LW, Mathias, RA, Minster, RL, Mitchell, BD, Montasser, ME, Morrison, AC, Palmer, ND, Peyser, PA, Psaty, BM, Raffield, LM & NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium 2023 'A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies' bioRxiv. https://doi.org/10.1101/2023.10.30.564764

APA

Li, X., Chen, H., Selvaraj, M. S., Van Buren, E., Zhou, H., Wang, Y., Sun, R., McCaw, Z. R., Yu, Z., Arnett, D. K., Bis, J. C., Blangero, J., Boerwinkle, E., Bowden, D. W., Brody, J. A., Cade, B. E., Carson, A. P., Carlson, J. C., Chami, N., ... NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium (2023). A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. bioRxiv. https://doi.org/10.1101/2023.10.30.564764

Vancouver

Li X, Chen H, Selvaraj MS, Van Buren E, Zhou H, Wang Y et al. A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. bioRxiv. 2023. https://doi.org/10.1101/2023.10.30.564764

Author

Li, Xihao ; Chen, Han ; Selvaraj, Margaret Sunitha ; Van Buren, Eric ; Zhou, Hufeng ; Wang, Yuxuan ; Sun, Ryan ; McCaw, Zachary R ; Yu, Zhi ; Arnett, Donna K ; Bis, Joshua C ; Blangero, John ; Boerwinkle, Eric ; Bowden, Donald W ; Brody, Jennifer A ; Cade, Brian E ; Carson, April P ; Carlson, Jenna C ; Chami, Nathalie ; Chen, Yii-Der Ida ; Curran, Joanne E ; de Vries, Paul S ; Fornage, Myriam ; Franceschini, Nora ; Freedman, Barry I ; Gu, Charles ; Heard-Costa, Nancy L ; He, Jiang ; Hou, Lifang ; Hung, Yi-Jen ; Irvin, Marguerite R ; Kaplan, Robert C ; Kardia, Sharon L R ; Kelly, Tanika ; Konigsberg, Iain ; Kooperberg, Charles ; Kral, Brian G ; Li, Changwei ; Loos, Ruth J F ; Mahaney, Michael C ; Martin, Lisa W ; Mathias, Rasika A ; Minster, Ryan L ; Mitchell, Braxton D ; Montasser, May E ; Morrison, Alanna C ; Palmer, Nicholette D ; Peyser, Patricia A ; Psaty, Bruce M ; Raffield, Laura M ; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. / A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. bioRxiv, 2023.

Bibtex

@techreport{6c7fbb905ac24b1aa34dc934d63a7e3a,
title = "A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies",
abstract = "Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.",
author = "Xihao Li and Han Chen and Selvaraj, {Margaret Sunitha} and {Van Buren}, Eric and Hufeng Zhou and Yuxuan Wang and Ryan Sun and McCaw, {Zachary R} and Zhi Yu and Arnett, {Donna K} and Bis, {Joshua C} and John Blangero and Eric Boerwinkle and Bowden, {Donald W} and Brody, {Jennifer A} and Cade, {Brian E} and Carson, {April P} and Carlson, {Jenna C} and Nathalie Chami and Chen, {Yii-Der Ida} and Curran, {Joanne E} and {de Vries}, {Paul S} and Myriam Fornage and Nora Franceschini and Freedman, {Barry I} and Charles Gu and Heard-Costa, {Nancy L} and Jiang He and Lifang Hou and Yi-Jen Hung and Irvin, {Marguerite R} and Kaplan, {Robert C} and Kardia, {Sharon L R} and Tanika Kelly and Iain Konigsberg and Charles Kooperberg and Kral, {Brian G} and Changwei Li and Loos, {Ruth J F} and Mahaney, {Michael C} and Martin, {Lisa W} and Mathias, {Rasika A} and Minster, {Ryan L} and Mitchell, {Braxton D} and Montasser, {May E} and Morrison, {Alanna C} and Palmer, {Nicholette D} and Peyser, {Patricia A} and Psaty, {Bruce M} and Raffield, {Laura M} and {NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium}",
year = "2023",
doi = "10.1101/2023.10.30.564764",
language = "English",
publisher = "bioRxiv",
type = "WorkingPaper",
institution = "bioRxiv",

}

RIS

TY - UNPB

T1 - A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

AU - Li, Xihao

AU - Chen, Han

AU - Selvaraj, Margaret Sunitha

AU - Van Buren, Eric

AU - Zhou, Hufeng

AU - Wang, Yuxuan

AU - Sun, Ryan

AU - McCaw, Zachary R

AU - Yu, Zhi

AU - Arnett, Donna K

AU - Bis, Joshua C

AU - Blangero, John

AU - Boerwinkle, Eric

AU - Bowden, Donald W

AU - Brody, Jennifer A

AU - Cade, Brian E

AU - Carson, April P

AU - Carlson, Jenna C

AU - Chami, Nathalie

AU - Chen, Yii-Der Ida

AU - Curran, Joanne E

AU - de Vries, Paul S

AU - Fornage, Myriam

AU - Franceschini, Nora

AU - Freedman, Barry I

AU - Gu, Charles

AU - Heard-Costa, Nancy L

AU - He, Jiang

AU - Hou, Lifang

AU - Hung, Yi-Jen

AU - Irvin, Marguerite R

AU - Kaplan, Robert C

AU - Kardia, Sharon L R

AU - Kelly, Tanika

AU - Konigsberg, Iain

AU - Kooperberg, Charles

AU - Kral, Brian G

AU - Li, Changwei

AU - Loos, Ruth J F

AU - Mahaney, Michael C

AU - Martin, Lisa W

AU - Mathias, Rasika A

AU - Minster, Ryan L

AU - Mitchell, Braxton D

AU - Montasser, May E

AU - Morrison, Alanna C

AU - Palmer, Nicholette D

AU - Peyser, Patricia A

AU - Psaty, Bruce M

AU - Raffield, Laura M

AU - NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

PY - 2023

Y1 - 2023

N2 - Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

AB - Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

U2 - 10.1101/2023.10.30.564764

DO - 10.1101/2023.10.30.564764

M3 - Preprint

C2 - 37961350

BT - A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

PB - bioRxiv

ER -

ID: 379175018