Identifying somatic fingerprints of cancers defined by germline and environmental risk factors
Genetic Epidemiology
by Saptarshi Chakraborty, Zoe Guan, Caroline E. Kostrzewa, Ronglai Shen, Colin B. Begg
1w ago
Abstract Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the ..read more
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Meta‐analysis of breast cancer risk for individuals with PALB2 pathogenic variants
Genetic Epidemiology
by Thanthirige L. M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas
2w ago
Abstract Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these ..read more
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A novel application of data‐consistent inversion to overcome spurious inference in genome‐wide association studies
Genetic Epidemiology
by Negar Janani, Kendra A. Young, Greg Kinney, Matthew Strand, John E. Hokanson, Yaning Liu, Troy Butler, Erin Austin
2w ago
Abstract The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants ..read more
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Shared genetic risk between major orofacial cleft phenotypes in an African population
Genetic Epidemiology
by Azeez Alade, Tabitha Peter, Tamara Busch, Waheed Awotoye, Deepti Anand, Oladayo Abimbola, Emmanuel Aladenika, Mojisola Olujitan, Oscar Rysavy, Phuong Fawng Nguyen, Thirona Naicker, Peter A. Mossey, Lord J. J. Gowans, Mekonen A. Eshete, Wasiu L. Adeyemo, Erliang Zeng, Eric Van Otterloo, Michael O'Rorke, Adebowale Adeyemo, Jeffrey C. Murray, Salil A. Lachke, Paul A. Romitti, Azeez Butali
2w ago
Abstract Nonsyndromic orofacial clefts (NSOFCs) represent a large proportion (70%–80%) of all OFCs. They can be broadly categorized into nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Although NSCL/P and NSCPO are considered etiologically distinct, recent evidence suggests the presence of shared genetic risks. Thus, we investigated the genetic overlap between NSCL/P and NSCPO using African genome-wide association study (GWAS) data on NSOFCs. These data consist of 814 NSCL/P, 205 NSCPO cases, and 2159 unrelated controls. We generated com ..read more
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Structured testing of genetic association with mixed clinical outcomes
Genetic Epidemiology
by Meiling Liu, Yu‐Ru Su, Yang Liu, Li Hsu, Qianchuan He
3w ago
Abstract Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we ..read more
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Hierarchical joint analysis of marginal summary statistics—Part I: Multipopulation fine mapping and credible set construction
Genetic Epidemiology
by Jiayi Shen, Lai Jiang, Kan Wang, Anqi Wang, Fei Chen, Paul J. Newcombe, Christopher A. Haiman, David V. Conti
3w ago
Abstract Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal varian ..read more
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OSCAA: A two‐dimensional Gaussian mixture model for copy number variation association analysis
Genetic Epidemiology
by Xuanxuan Yu, Xizhi Luo, Guoshuai Cai, Feifei Xiao
1M ago
Abstract Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrie ..read more
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Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies
Genetic Epidemiology
by John L. Hopper, Shuai Li, Robert J. MacInnis, James G. Dowty, Tuong L. Nguyen, Minh Bui, Gillian S. Dite, Vivienne F. C. Esser, Zhoufeng Ye, Enes Makalic, Daniel F. Schmidt, Benjamin Goudey, Karen Alpen, Miroslaw Kapuscinski, Aung Ko Win, Pierre‐Antoine Dugué, Roger L. Milne, Harindra Jayasekara, Jennifer D. Brooks, Sue Malta, Lucas Calais‐Ferreira, Alexander C. Campbell, Jesse T. Young, Tu Nguyen‐Dumont, Joohon Sung, Graham G. Giles, Daniel Buchanan, Ingrid Winship, Mary Beth Terry, Melissa C. Southey, Mark A. Jenkins
1M ago
Abstract Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family ..read more
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Issue Information
Genetic Epidemiology
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1M ago
Genetic Epidemiology, Volume 48, Issue 3, Page 101-102, April 2024 ..read more
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Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses
Genetic Epidemiology
by Shuai Li, Gillian S. Dite, Robert J. MacInnis, Minh Bui, Tuong L. Nguyen, Vivienne F. C. Esser, Zhoufeng Ye, James G. Dowty, Enes Makalic, Joohon Sung, Graham G. Giles, Melissa C. Southey, John L. Hopper
2M ago
Abstract A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the chang ..read more
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