Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis

Wu, Xueyao; Xiao, Chenghan; Han, Zhitong; Zhang, Li; Zhao, Xunying; Yu, Hong‐Ren; Xiao, Jianlin; Gallagher, C. Scott; Kraft, Peter; Li, Jiayuan; Jiang, Xia

doi:10.1016/j.ajhg.2022.05.015

Cited by 9 publications

(3 citation statements)

References 70 publications

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“…Pervasive genetic correlations between diseases allowed MTL to improve the PRS estimation broadly across diseases. While many cross-strait studies have shown the genetic correlation between specific pairs of diseases [18][19][20][21][22][23][24], our study suggested that various degrees of shared genetic basis may be very prevalent among many complex diseases. Our results highlighted the potential value of holistic association studies between the whole human phenome and the whole human genome for both risk variant discovery and PRS estimation.…”

Section: Plos Computational Biologycontrasting

confidence: 50%

“…Many complex diseases share a substantial amount of common genetic determinants. Genome-wide cross-trait analyses have been performed between obesity and cardiovascular diseases [18], between thyroid and breast cancers [19], between uterine leiomyoma and breast cancer [20], between asthma and cardiovascular diseases [21], between Alzheimer's disease and gastrointestinal tract disorders [22], between Alzheimer disease and major depressive disorder [23], between lung cancer and chronic bronchitis [24], and so on. These studies were often motivated by frequent co-occurrences of pairs of diseases in a population.…”

Section: Introductionmentioning

confidence: 99%

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Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis

Badré

Pan

2023

PLoS Comput Biol

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Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases’ polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.

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Section: Plos Computational Biologycontrasting

confidence: 50%

Section: Introductionmentioning

confidence: 99%

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis

Badré

Pan

2023

PLoS Comput Biol

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“…Additionally, a new genome-wide association study (GWAS) meta-analysis reported a significant genetic correlation of UFs with BC, especially a possible causal link with estrogen receptor (ER)-positive BC. They suggested that those findings indicate an intrinsic link underlying UFs and BC 28 .…”

Section: Discussionmentioning

confidence: 92%

Association between breast diseases and symptomatic uterine fibroids by using South Korean National Health Insurance database

Yuk,

Yang,

Yoon

et al. 2023

Sci Rep

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Both the uterus and breasts have sex hormone dependence, yet there are few studies on the association between breast disease and uterine fibroids (UFs). The purpose of this study was to investigate the incidence of benign breast disease (BBD), carcinoma in situ (CIS), and breast cancer (BC) in women treated for UFs compared to women who were not treated for UFs. This retrospective cohort study used national health insurance data from January 1st, 2011, to December 31st, 2020. We selected women between 20 and 50 years old who (1) were treated for UFs (UF group) or (2) visited medical institutions for personal health screening tests without UFs (control group). We analyzed independent variables such as age, socioeconomic status (SES), region, Charlson comorbidity index (CCI), delivery status, menopausal status, menopausal hormone therapy (MHT), endometriosis, hypertension (HTN), diabetes mellitus (DM), and dyslipidemia based on the first date of uterine myomectomy in the UF group and the first visiting date for health screening in the non-UF group. There were 190,583 and 439,940 participants in the UF and control groups, respectively. Compared with those of the control group, the RRs of BBD, CIS, and BC were increased in the UF group. The hazard ratios (HRs) of BBD, CIS, and BC in the UF group were 1.335 (95% confidence interval (CI) 1.299–1.372), 1.796 (95% CI 1.542–2.092), and 1.3 (95% CI 1.198–1.41), respectively. When we analyzed the risk of BC according to age at inclusion, UFs group had the increased risk of BCs in all age groups in comparison with control group. Women with low SES (HR 0.514, 95% CI 0.36–0.734) and living in rural areas (HR 0.889, 95% CI 0.822–0.962) had a lower risk of BC. Our study showed that women with UFs had a higher risk of BBD, CIS, and BC than those without UFs. This result suggests that women with UFs should be more conscious of BC than those without UFs. Therefore, doctors should consider recommending regular breast self-exams, mammography, or ultrasound for the early detection of BC in women with UFs.

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A cause-effect relationship between uterine diseases and breast cancer: A bidirectional Mendelian randomization study

Liu,

Jiang,

Wang

et al. 2024

Heliyon

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Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis

Cited by 9 publications

References 70 publications

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis

Association between breast diseases and symptomatic uterine fibroids by using South Korean National Health Insurance database

A cause-effect relationship between uterine diseases and breast cancer: A bidirectional Mendelian randomization study

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