Powerful eQTL mapping through low-coverage RNA sequencing

Tommer Schwarz; Toni Boltz; Kangcheng Hou; Merel Bot; Chenda Duan; Loes Olde Loohuis; Marco P. Boks; René S. Kahn; Roel A. Ophoff; Bogdan Pasaniuc

doi:10.1016/j.xhgg.2022.100103

Powerful eQTL mapping through low-coverage RNA sequencing

Tommer Schwarz^*, Toni Boltz, Kangcheng Hou, Merel Bot, Chenda Duan, Loes Olde Loohuis, Marco P. Boks, René S. Kahn, Roel A. Ophoff, Bogdan Pasaniuc

^*Corresponding author for this work

Psychiatry

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Abstract

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

Original language	English
Article number	100103
Journal	Human Genetics and Genomics Advances
Volume	3
Issue number	3
DOIs	https://doi.org/10.1016/j.xhgg.2022.100103
Publication status	Published - 14 Jul 2022

Bibliographical note

Funding Information:
We thank the study subjects for their willingness to provide specimens and clinical data. We thank Yi Ding, Kathryn Burch, Ruthie Johnson, Arjun Bhattacharya, and Malika Freund for meaningful discussion in helping make this work possible. T.S. was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award no. T32NS048004 . T.B. was supported by the NIH (grant no. 5T32HG002536-19 ). This research was supported by the National Institute of Mental Health of the National Institutes of Health under award no. 5R01MH115676-04 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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© 2022 The Authors

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Powerful eQTL mapping through low-coverage RNA sequencingFinal published version, 1.15 MBLicence: CC BY

Cite this

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title = "Powerful eQTL mapping through low-coverage RNA sequencing",

abstract = "Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.",

author = "Tommer Schwarz and Toni Boltz and Kangcheng Hou and Merel Bot and Chenda Duan and Loohuis, {Loes Olde} and Boks, {Marco P.} and Kahn, {Ren{\'e} S.} and Ophoff, {Roel A.} and Bogdan Pasaniuc",

note = "Funding Information: We thank the study subjects for their willingness to provide specimens and clinical data. We thank Yi Ding, Kathryn Burch, Ruthie Johnson, Arjun Bhattacharya, and Malika Freund for meaningful discussion in helping make this work possible. T.S. was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award no. T32NS048004 . T.B. was supported by the NIH (grant no. 5T32HG002536-19 ). This research was supported by the National Institute of Mental Health of the National Institutes of Health under award no. 5R01MH115676-04 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2022 The Authors",

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AU - Boltz, Toni

AU - Hou, Kangcheng

AU - Bot, Merel

AU - Duan, Chenda

AU - Loohuis, Loes Olde

AU - Boks, Marco P.

AU - Kahn, René S.

AU - Ophoff, Roel A.

AU - Pasaniuc, Bogdan

N1 - Funding Information: We thank the study subjects for their willingness to provide specimens and clinical data. We thank Yi Ding, Kathryn Burch, Ruthie Johnson, Arjun Bhattacharya, and Malika Freund for meaningful discussion in helping make this work possible. T.S. was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award no. T32NS048004 . T.B. was supported by the NIH (grant no. 5T32HG002536-19 ). This research was supported by the National Institute of Mental Health of the National Institutes of Health under award no. 5R01MH115676-04 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: © 2022 The Authors

PY - 2022/7/14

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N2 - Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

AB - Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

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M1 - 100103

ER -

Powerful eQTL mapping through low-coverage RNA sequencing

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