Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.
Fang ChenXingyan WangSeon-Kyeong JangBryan C QuachJ Dylan WeissenkampenChachrit KhunsriraksakulLina YangRenan SauteraudChristine M AlbertNicholette D D AllredDonna K ArnettAllison Ashley KochKathleen C BarnesR Graham BarrDiane M BeckerLawrence F BielakJoshua C BisJohn BlangeroMeher Preethi BoorgulaDaniel I ChasmanSameer ChavanYii-Der I ChenLee-Ming ChuangAdolfo CorreaJoanne E CurranSean P DavidLisa de Las FuentesRanjan DekaRavindranath DuggiralaJessica D FaulMelanie E GarrettSina A GharibXiuqing GuoMichael E HallNicola L HawleyJiang HeBrian D HobbsJohn E HokansonChao A HsiungShih-Jen HwangThomas M HydeMarguerite R IrvinAndrew E JaffeEric O JohnsonRobert KaplanSharon L R KardiaJoel D KaufmanTanika N KellyJoel E KleinmanCharles KooperbergI-Te LeeDaniel LevySharon M LutzAni W ManichaikulLisa W MartinOlivia MarxStephen T McGarveyRyan L MinsterMatthew MollKarine A MoussaTake NaseriKari E NorthElizabeth C OelsnerJuan M PeraltaPatricia A PeyserBruce M PsatyNicholas RafaelsLaura M RaffieldMuagututi'a Sefuiva ReupenaStephen S RichJerome I RotterDavid A SchwartzAladdin H ShadyabWayne H-H SheuMario SimsJennifer A SmithXiao SunKent D TaylorMarilyn J TelenHarold WatsonDaniel E WeeksDavid R WeirLisa R YanekKendra A YoungKristin L YoungWei ZhaoDana B HancockBibo JiangScott I VriezeDajiang J LiuPublished in: Nature genetics (2023)
Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.
Keyphrases
- genome wide association study
- genome wide
- magnetic resonance
- gene expression
- high resolution
- poor prognosis
- dna methylation
- emergency department
- electronic health record
- machine learning
- climate change
- risk assessment
- long non coding rna
- human health
- genome wide identification
- big data
- mass spectrometry
- high density
- drug discovery