biostudies-literaturePing JNIEHS NIH HHSNIMHD NIH HHSNCI NIH HHSU.S. Department of Health &amp; Human Services | National Institutes of HealthNIH HHS3718https://www.ebi.ac.uk/biostudies/studies/S-EPMC11065893biostudies-literatureUnknown15(1)African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with riskNature communicationsUsing genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes.PMC11065893U01 CA164974P30 ES013508P30 CA068485P20 CA233307R01 CA228198S10 OD023680R01 CA235553P30 ES010126R01 MD013452R01CA235553R01 CA202981R01CA202981Yoshimatsu TCai QO'Brien KMPing JGarcia-Closas MGuo XLong JHuang MAmbs SAhearn TJia GHennis AJMReid SNtekim APalmer JRLi CITroester MAChen YGu JLi BHaiman CAJohn EMButler ENNemesure BOluwasanu MMBrewster AMAmbrosone CYao SSandler DPHuo DBarnard MENdom POlopade OIZheng WOlshan AFNathanson KPress MFSanderson MAdejumo POTao RPal TMakumbi THu JJfalseUsing genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes.African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk2024-01-01T00:00:00Z2024 May2026-06-01T05:38:08.906Z2026-04-08T09:43:06.946ZS-EPMC110658933869799810.1038/s41467-024-47650-5