Previous GWAS hits in relation to young-onset breast cancer

Shi, Min; O’Brien, Katie M.; Sandler, Dale P.; Taylor, Jack A.; Zaykin, Dmitri V.; Weinberg, Clarice R.

doi:10.1007/s10549-016-4053-z

Cited by 12 publications

(24 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dysregulation of the expression of lincRNAs may be pervasive in human cancers and drives cancer development and progression [35]. Previous researches demonstrated that there are SNPs on LINC01376 associated with breast cancer [36,37]. Notably, it was the only lncRNA upregulated in our analysis with a logFC of 5.88, and it scored the best AUC (0.913) among all candidates.…”

Section: Resultsmentioning

confidence: 73%

“…Another lncRNA-AS that stood out in our analysis was ZNF667-AS1, with a logFC of approximately − 5.16 and an AUC of 0.717. Other studies have demonstrated that ZNF667-AS1 is commonly downregulated in several cancer types, including UCEC [36,37]. Vrba and colleagues [45] showed that it is expressed in all normal finite lifespan human cells examined to date and is downregulated or lost in immortalized human mammary epithelial cells.…”

Section: Resultsmentioning

confidence: 98%

See 1 more Smart Citation

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Mello

Freitas

Coutinho

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.

show abstract

Section: Resultsmentioning

confidence: 73%

Section: Resultsmentioning

confidence: 98%

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Mello

Freitas

Coutinho

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…With complete trios, previously reported statistics 8 become equivalent to statistics from the transmission-disequilibrium test and correlation among them is expected to follow the LD among SNPs. 8 We selected four candidate genes (TOX3, ESR1, FGFR2 and RAD51B ), for which Min et al 26,27 replicated several previously reported risk SNPs in relation to breast cancer.…”

Section: Combining Breast Cancer Association Statistics Within Candidmentioning

confidence: 89%

“…We applied our decorrelation method to a family-based GWAS study of breast cancer. 26,27 The data set was comprised of complete trios, i.e., families where genotypes of both parents and the affected offspring were available. With complete trios, previously reported statistics 8 become equivalent to statistics from the transmission-disequilibrium test and correlation among them is expected to follow the LD among SNPs.…”

Section: Combining Breast Cancer Association Statistics Within Candidmentioning

confidence: 99%

DOT: Gene-set analysis by combining decorrelated association statistics

Vsevolozhskaya

Shi

et al. 2020

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis.Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer breast cancer risk.

show abstract

“…(Figure 1 of Weinberg et al 18 ). In this paper we assess interactions between the same 77-SNP PRS 3 and 14 established risk factors for breast cancer using data from a family-based genome-wide association study (GWAS) of young-onset (under age 50) breast cancer, the Two Sister Study.…”

mentioning

confidence: 99%

Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer

Shi

O’Brien

Weinberg

2020

Sci Rep

View full text Add to dashboard Cite

Most gene-environmental studies have focused on breast cancers generally, the preponderance of which occur after age 50. Young-onset breast cancers (YOBC) tend to be aggressive and may be etiologically different. The goal of this analysis was to assess interactions between an established 77-SNP polygenic risk score (PRS) and non-genetic risk factors for YOBC. We constructed the PRS using a family-based study of 1,291 women diagnosed with breast cancer before age 50 and their parents and unaffected sisters. We used conditional logistic regression to analyze interactions between the PRS and 14 established risk factors. in further analyses we assessed the same interactions, but for invasive cancer, estrogen receptor (ER) positive cancer and with broader inclusion of racial/ethnic groups. Results showed a decreased association between the pRS and YoBc risk for women who had ever used hormonal birth control (odds ratio [OR] = 2.20 versus 3.89) and a stronger association between the PRS and YOBC risk in pre-menopausal women (OR = 2.46 versus 1.23). Restricting the analysis to ER+ cancers or invasive cancers or using samples from all ethnic groups produced similar results. in conclusion, the pRS may interact with hormonal birth control use and with menopausal status on risk of YoBc.

show abstract

Previous GWAS hits in relation to young-onset breast cancer

Cited by 12 publications

References 79 publications

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

DOT: Gene-set analysis by combining decorrelated association statistics

Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer

Contact Info

Product

Resources

About