Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

Ollie Pain; Ashley Jones; Ahmad Al Khleifat; Devika Agarwal; Dzmitry Hramyka; Hajer Karoui; Jędrzej Kubica; David J. Llewellyn; Janice M. Ranson; Zhi  Yao; Alfredo Iacoangeli; Ammar Al-Chalabi

doi:10.1016/j.heliyon.2024.e35342

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

Ollie Pain, Ashley Jones, Ahmad Al Khleifat, Devika Agarwal, Dzmitry Hramyka, Hajer Karoui, Jędrzej Kubica, David J. Llewellyn, Janice M. Ranson, Zhi Yao, Alfredo Iacoangeli, Ammar Al-Chalabi

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R² = 5.1 %; p-value = 3.2 × 10⁻²⁷) and clinical characteristics. Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Original language	English
Article number	e35342
Journal	Heliyon
Volume	10
Issue number	15
DOIs	https://doi.org/10.1016/j.heliyon.2024.e35342
Publication status	Published - 15 Aug 2024

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10.1016/j.heliyon.2024.e35342

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Pain, O., Jones, A., Al Khleifat, A., Agarwal, D., Hramyka, D., Karoui, H., Kubica, J., Llewellyn, D. J., Ranson, J. M., Yao, Z., Iacoangeli, A., & Al-Chalabi, A. (2024). Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction. Heliyon, 10(15), Article e35342. https://doi.org/10.1016/j.heliyon.2024.e35342

@article{896962c356a640cdacdb5c8c8ce40374,

title = "Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction",

abstract = "Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.",

author = "Ollie Pain and Ashley Jones and {Al Khleifat}, Ahmad and Devika Agarwal and Dzmitry Hramyka and Hajer Karoui and J{\c e}drzej Kubica and Llewellyn, {David J.} and Ranson, {Janice M.} and Zhi Yao and Alfredo Iacoangeli and Ammar Al-Chalabi",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors",

year = "2024",

month = aug,

day = "15",

doi = "10.1016/j.heliyon.2024.e35342",

language = "English",

volume = "10",

journal = "Heliyon",

issn = "2405-8440",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

AU - Pain, Ollie

AU - Jones, Ashley

AU - Al Khleifat, Ahmad

AU - Agarwal, Devika

AU - Hramyka, Dzmitry

AU - Karoui, Hajer

AU - Kubica, Jędrzej

AU - Llewellyn, David J.

AU - Ranson, Janice M.

AU - Yao, Zhi

AU - Iacoangeli, Alfredo

AU - Al-Chalabi, Ammar

PY - 2024/8/15

Y1 - 2024/8/15

N2 - Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

AB - Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

UR - http://www.scopus.com/inward/record.url?scp=85199904027&partnerID=8YFLogxK

U2 - 10.1016/j.heliyon.2024.e35342

DO - 10.1016/j.heliyon.2024.e35342

M3 - Article

AN - SCOPUS:85199904027

SN - 2405-8440

VL - 10

JO - Heliyon

JF - Heliyon

IS - 15

M1 - e35342

ER -

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

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