Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq

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Comparison of quantitative trait loci methods : Total expression and allelic imbalance method in brain RNA-seq. / Gådin, Jesper R; Buil, Alfonso; Colantuoni, Carlo; Jaffe, Andrew E; Nielsen, Jacob; Shin, Joo-Heon; Hyde, Thomas M; Kleinman, Joel E; Plath, Niels; Eriksson, Per; Brunak, Søren; Didriksen, Michael; Weinberger, Daniel R; Folkersen, Lasse; BrainSeq Consortium.

In: PLoS ONE, Vol. 14, No. 6, e0217765, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Gådin, JR, Buil, A, Colantuoni, C, Jaffe, AE, Nielsen, J, Shin, J-H, Hyde, TM, Kleinman, JE, Plath, N, Eriksson, P, Brunak, S, Didriksen, M, Weinberger, DR, Folkersen, L & BrainSeq Consortium 2019, 'Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq', PLoS ONE, vol. 14, no. 6, e0217765. https://doi.org/10.1371/journal.pone.0217765

APA

Gådin, J. R., Buil, A., Colantuoni, C., Jaffe, A. E., Nielsen, J., Shin, J-H., ... BrainSeq Consortium (2019). Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq. PLoS ONE, 14(6), [e0217765]. https://doi.org/10.1371/journal.pone.0217765

Vancouver

Gådin JR, Buil A, Colantuoni C, Jaffe AE, Nielsen J, Shin J-H et al. Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq. PLoS ONE. 2019;14(6). e0217765. https://doi.org/10.1371/journal.pone.0217765

Author

Gådin, Jesper R ; Buil, Alfonso ; Colantuoni, Carlo ; Jaffe, Andrew E ; Nielsen, Jacob ; Shin, Joo-Heon ; Hyde, Thomas M ; Kleinman, Joel E ; Plath, Niels ; Eriksson, Per ; Brunak, Søren ; Didriksen, Michael ; Weinberger, Daniel R ; Folkersen, Lasse ; BrainSeq Consortium. / Comparison of quantitative trait loci methods : Total expression and allelic imbalance method in brain RNA-seq. In: PLoS ONE. 2019 ; Vol. 14, No. 6.

Bibtex

@article{dc2d27bb8dbc46e395c054eb9b4288c9,
title = "Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq",
abstract = "BACKGROUND: Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.METHODS: We applied both the eQTL and aeQTL analysis to a biobank of deeply sequenced RNA from 680 dorso-lateral pre-frontal cortex (DLPFC) samples. For each of 340 genes proximal to the SZ risk-SNPs, we asked how much SNP-genotype affected total expression (eQTL), as well as how much the expression ratio between the two alleles differed from 1:1 as a consequence of the risk-SNP genotype (aeQTL).RESULTS: We analyzed overlap with comparable eQTL-findings: 16 of the 30 risk-SNPs known to have gene-level eQTL also had gene-level aeQTL effects. 6 of 21 risk-SNPs with known splice-eQTL had exon-aeQTL effects. 12 novel potential risk genes were identified with the aeQTL approach, while 55 tested SNP-pairs were found as eQTL but not aeQTL. Of the tested 108 loci we could find at least one gene to be associated with 21 of the risk-SNPs using gene-level aeQTL, and with an additional 18 risk-SNPs using exon-level aeQTL.CONCLUSION: Our results suggest that the aeQTL strategy complements the eQTL approach to susceptibility gene identification.",
author = "G{\aa}din, {Jesper R} and Alfonso Buil and Carlo Colantuoni and Jaffe, {Andrew E} and Jacob Nielsen and Joo-Heon Shin and Hyde, {Thomas M} and Kleinman, {Joel E} and Niels Plath and Per Eriksson and S{\o}ren Brunak and Michael Didriksen and Weinberger, {Daniel R} and Lasse Folkersen and {BrainSeq Consortium}",
year = "2019",
doi = "10.1371/journal.pone.0217765",
language = "English",
volume = "14",
journal = "P L o S One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "6",

}

RIS

TY - JOUR

T1 - Comparison of quantitative trait loci methods

T2 - Total expression and allelic imbalance method in brain RNA-seq

AU - Gådin, Jesper R

AU - Buil, Alfonso

AU - Colantuoni, Carlo

AU - Jaffe, Andrew E

AU - Nielsen, Jacob

AU - Shin, Joo-Heon

AU - Hyde, Thomas M

AU - Kleinman, Joel E

AU - Plath, Niels

AU - Eriksson, Per

AU - Brunak, Søren

AU - Didriksen, Michael

AU - Weinberger, Daniel R

AU - Folkersen, Lasse

AU - BrainSeq Consortium

PY - 2019

Y1 - 2019

N2 - BACKGROUND: Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.METHODS: We applied both the eQTL and aeQTL analysis to a biobank of deeply sequenced RNA from 680 dorso-lateral pre-frontal cortex (DLPFC) samples. For each of 340 genes proximal to the SZ risk-SNPs, we asked how much SNP-genotype affected total expression (eQTL), as well as how much the expression ratio between the two alleles differed from 1:1 as a consequence of the risk-SNP genotype (aeQTL).RESULTS: We analyzed overlap with comparable eQTL-findings: 16 of the 30 risk-SNPs known to have gene-level eQTL also had gene-level aeQTL effects. 6 of 21 risk-SNPs with known splice-eQTL had exon-aeQTL effects. 12 novel potential risk genes were identified with the aeQTL approach, while 55 tested SNP-pairs were found as eQTL but not aeQTL. Of the tested 108 loci we could find at least one gene to be associated with 21 of the risk-SNPs using gene-level aeQTL, and with an additional 18 risk-SNPs using exon-level aeQTL.CONCLUSION: Our results suggest that the aeQTL strategy complements the eQTL approach to susceptibility gene identification.

AB - BACKGROUND: Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.METHODS: We applied both the eQTL and aeQTL analysis to a biobank of deeply sequenced RNA from 680 dorso-lateral pre-frontal cortex (DLPFC) samples. For each of 340 genes proximal to the SZ risk-SNPs, we asked how much SNP-genotype affected total expression (eQTL), as well as how much the expression ratio between the two alleles differed from 1:1 as a consequence of the risk-SNP genotype (aeQTL).RESULTS: We analyzed overlap with comparable eQTL-findings: 16 of the 30 risk-SNPs known to have gene-level eQTL also had gene-level aeQTL effects. 6 of 21 risk-SNPs with known splice-eQTL had exon-aeQTL effects. 12 novel potential risk genes were identified with the aeQTL approach, while 55 tested SNP-pairs were found as eQTL but not aeQTL. Of the tested 108 loci we could find at least one gene to be associated with 21 of the risk-SNPs using gene-level aeQTL, and with an additional 18 risk-SNPs using exon-level aeQTL.CONCLUSION: Our results suggest that the aeQTL strategy complements the eQTL approach to susceptibility gene identification.

U2 - 10.1371/journal.pone.0217765

DO - 10.1371/journal.pone.0217765

M3 - Journal article

C2 - 31206532

VL - 14

JO - P L o S One

JF - P L o S One

SN - 1932-6203

IS - 6

M1 - e0217765

ER -

ID: 222692695