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Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction.

Guochong JiaJie PingXingyi GuoYaohua YangRan TaoBingshan LiStefan AmbsMollie E BarnardYu ChenMontserrat Garcia-ClosasJian GuJennifer J HuDezheng HuoEsther M JohnChristopher I LiJames L LiKatherine L NathansonBarbara NemesureOlufunmilayo I OlopadeTuya PalMichael F PressMaureen SandersonDale R SandlerXiao-Ou ShuMelissa A TroesterSong YaoPrisca O AdejumoThomas U AhearnAbenaa M BrewsterAnselm J M HennisTimothy MakumbiPaul NdomKatie M O'BrienAndrew F OlshanMojisola M OluwasanuSonya ReidEbonee N ButlerMaosheng HuangAtara I NtekimHuijun QianHaoyu ZhangChristine B AmbrosoneQiuyin CaiJirong LongJulie R PalmerChristopher A HaimanQuan Long
Published in: Nature genetics (2024)
We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10 -8 ), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.
Keyphrases
  • breast cancer risk
  • genome wide association study
  • genome wide association
  • copy number
  • genome wide
  • type diabetes
  • machine learning
  • electronic health record
  • dna methylation
  • gene expression
  • polycystic ovary syndrome