scholarly journals Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog:PRSWeb

2018 ◽  
Author(s):  
Lars G. Fritsche ◽  
Lauren J. Beesley ◽  
Peter VandeHaar ◽  
Robert B. Peng ◽  
Maxwell Salvatore ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Genetic Risk ◽  
Population Based ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Cancer Subtypes ◽  
Construction Methods ◽  
The Uk

AbstractPolygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

scholarly journals Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

PLoS Genetics ◽  
2019 ◽  
Vol 15 (6) ◽  
pp. e1008202 ◽  
Author(s):  
Lars G. Fritsche ◽  
Lauren J. Beesley ◽  
Peter VandeHaar ◽  
Robert B. Peng ◽  
Maxwell Salvatore ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
The Uk

scholarly journals Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

2018 ◽  
Author(s):  
Kristi Läll ◽  
Maarja Lepamets ◽  
Marili Palover ◽  
Tõnu Esko ◽  
Andres Metspalu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Population Based ◽  
Risk Scores ◽  
Full Potential ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
The Uk

AbstractBackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

Abstract P879: Differences in Statistical Performance of Polygenic Risk Scores for Cardiovascular Disease Across Different Race/Ethnicities

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Julian N Acosta ◽  
Cameron Both ◽  
Natalia Szejko ◽  
Stacy Brown ◽  
Kevin N Sheth ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Genetic Risk ◽  
Regression Models ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Logistic Regression Models ◽  
The Uk ◽  
Significant Health

Introduction: Genome-wide association studies have identified numerous genetic risk variants for stroke and myocardial infarction (MI) in Europeans. However, the limited applicability of these results to non-Europeans due to racial/ethnic differences in the genetic architecture of cardiovascular disease (CVD), coupled with the limited availability of genomic data in non-Europeans, may create significant health disparities now that genomic-based precision medicine is a reality. We tested the hypothesis that the performance of polygenic risk scores (PRS) for CVD differ in Europeans versus non-Europeans. Methods: We conducted a nested study within the UK Biobank, a prospective, population-based study that enrolled ~500,000 participants across the UK. For this study, we identified self-reported black participants and randomly matched them 1:1 by age and sex with white participants. We created a PRS using previously discovered loci for stroke and MI. We then tested whether this PRS representing the aggregate polygenic susceptibility to CVD yielded similar precision in black versus white participants in logistic regression models. Results: Of the 502,536 participants enrolled in the UK Biobank, 8,061 were self-reported blacks, with 7,644 having available data for our analyses. We randomly matched these participants with white individuals, leading to a total sample size of 15,288 (mean age 51.9 [SD 8.1], female 8,722 [57%]). The total number of events was 741 overall, with 363 happening in blacks and 378 happening in whites. In logistic regression models including age, sex, and 5 principal components, the statistical precision (e.g. narrower confidence intervals) for the PRS was substantially higher for whites (OR 1.22, 95%CI 1.08 - 1.37; p<0.0001) compared to blacks (OR 1.24, 95%CI 1.05-1.47; p=0.01). Secondary analyses using genetically-determined ancestry yielded similar results. Conclusion: Because CVD-related PRSs are derived mainly using genetic risk factors identified in populations of European ancestry, their statistical performance is lower in non-European populations. This asymmetry can lead to significant health disparities now that these tools are being evaluated in multiple precision medicine approaches.

scholarly journals Polygenic risk, lifestyle, and cardiovascular mortality: a prospective population-based UK Biobank study

2021 ◽  
Author(s):  
Jae-Seung Yun ◽  
Sang-Hyuk Jung ◽  
Manu Shivakumar ◽  
Brenda Xiao ◽  
Amit V. Khera ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Genetic Risk ◽  
Risk Groups ◽  
Population Based ◽  
Risk Scores ◽  
Uk Biobank ◽  
Lifestyle Behavior ◽  
Polygenic Risk ◽  
High Genetic Risk ◽  
Very High

AbstractOBJECTIVETo assess the prognostic ability of polygenic risk scores (PRSs) for coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) for cardiovascular (CV) mortality, independent of traditional risk factors, and further investigate the additive effect between lifestyle behavior and PRS on CV mortality.DESIGNProspective population-based cohort study.SETTINGUK Biobank.PARTICIPANTSA total 377,909 unrelated participants of white British descent were included in the analyses from the UK Biobank cohort.MAIN OUTCOME MEASURESGenome-wide PRSs were constructed using >6 million genetic variants. We stratified patients into four PRS risk groups: low (0 to 19th percentile), intermediate (20 to 79th percentile), high (80 to 98th percentile), and very high (99th percentile). We defined a favorable and unfavorable lifestyle with four modifiable lifestyle components, including smoking, obesity, physical activity, and diet. Cox proportional hazard models were used to analyze the relationship between PRS and CV mortality with stratification by age, sex, disease status, and lifestyle behavior.RESULTSOf 377,909 UK Biobank participants having European ancestry, 3,210 (0.8%) died due to CV disease during a median follow-up of 8.9 years. CV mortality risk was significantly associated with CAD PRS (low vs. very high genetic risk groups, CAD PRS hazard ratio [HR] 2.61 [2.02 to 3.36]) and T2DM PRS (HR 2.08 [1.58 to 2.73]), respectively. These relationships remained significant even after adjustment for a comprehensive range of demographic and clinical factors. In the very high genetic risk group, adherence to an unfavorable lifestyle was further associated with a substantially increased risk of CV mortality (favorable versus unfavorable lifestyle with very high genetic risk for CAD PRS, HR 8.31 [5.12 to 13.49]; T2DM PRS, HR 5.84 [3.39 to 10.04]). Across all genetic risk groups, 32.1% of CV mortality was attributable to lifestyle behavior (population attributable fraction [PAF] 32.1% [95% CI 28.8 to 35.3%]) and 14.1% was attributable to smoking (PAF 14.1% [95% CI 12.4 to 15.7%]). There was no evidence of significant interaction between PRSs and age, sex, or lifestyle behavior in predicting the risk of CV mortality.CONCLUSIONPRSs for CAD or T2DM and lifestyle behaviors are independent predictive factors for future CV mortality in the white, middle-aged population. PRS-based risk assessment could be useful to identify individuals who need intensive behavioral or therapeutic interventions to reduce the risk of CV mortality.Summary BoxWhat is already known on this topicPolygenic risk scores quantify the inherited risk conferred by the cumulative impact of common variants into a quantitative risk estimate.Previous studies primarily targeted the ability of polygenic risk scores to predict a specific disease, and only a few studies have investigated the association between genetic risk scores and cardiovascular mortality.The majority of previous analyses calculated polygenic risk scores from only a small number of genetic variants or adjusted for only a few risk factors, and no studies have examined whether the association of polygenic risk score with cardiovascular mortality differs by lifestyle behavior.What this study addsGenetic risk and lifestyle are independent predictive factors for cardiovascular mortality, even after adjustment for a comprehensive range of demographic and clinical factors.A healthy lifestyle is associated with relative risk reduction for cardiovascular mortality across all genetic risk categories, a finding that indicates the potential benefit of intensive lifestyle modification in overcoming genetic risk for cardiovascular mortality.

scholarly journals Associations Between Major Psychiatric Disorder Polygenic Risk Scores and Blood-Based Markers in UK Biobank

2020 ◽  
Author(s):  
Michael D.E. Sewell ◽  
Xueyi Shen ◽  
Lorena Jiménez-Sánchez ◽  
Amelia J. Edmondson-Stait ◽  
Claire Green ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Smoking Status ◽  
Hdl Cholesterol ◽  
Risk Scores ◽  
Uk Biobank ◽  
Linear Regression Models ◽  
Polygenic Risk ◽  
Bbb Permeability ◽  
The Uk ◽  
Peripheral Markers

AbstractBackgroundMajor depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD) have both shared and discrete genetic risk factors and abnormalities in blood-based measures of inflammation and blood-brain barrier (BBB) permeability. The relationships between such genetic architectures and blood-based markers are however unclear. We investigated relationships between polygenic risk scores for these disorders and peripheral biomarkers in the UK Biobank cohort.MethodsWe calculated polygenic risk scores (PRS) for samples of n = 367,329 (MDD PRS), n = 366,465 (SCZ PRS), and n = 366,383 (BD PRS) individuals from the UK Biobank cohort. We examined associations between each disorder PRS and 62 blood markers, using two generalized linear regression models: ‘minimally adjusted’ controlling for variables including age and sex, and ‘fully adjusted’ including additional lifestyle covariates such as alcohol and smoking status.Results12/62, 13/62 and 9/62 peripheral markers were significantly associated with MDD, SCZ and BD PRS respectively for both models. Most associations were disorder PRS-specific, including several immune-related markers for MDD and SCZ. We also identified several BBB-permeable marker associations, including vitamin D for all three disorder PRS, IGF-1 and triglycerides for MDD PRS, testosterone for SCZ PRS, and HDL cholesterol for BD PRS.ConclusionsThis study suggests that MDD, SCZ and BD have shared and distinct peripheral markers associated with disorder-specific genetic risk. The results implicate BBB permeability disruptions in all three disorders and inflammatory dysfunction in MDD and SCZ, and enrich our understanding of potential underlying pathophysiological mechanisms in major psychiatric disorders.

scholarly journals Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

PLoS Medicine ◽  
2021 ◽  
Vol 18 (10) ◽  
pp. e1003782
Author(s):  
Michael Wainberg ◽  
Samuel E. Jones ◽  
Lindsay Melhuish Beaupre ◽  
Sean L. Hill ◽  
Daniel Felsky ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Sleep Duration ◽  
Sleep Efficiency ◽  
Risk Scores ◽  
Psychiatric Diagnoses ◽  
Uk Biobank ◽  
Major Depressive ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
The Uk

Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

scholarly journals A machine-learning heuristic to improve gene score prediction of polygenic traits

2017 ◽  
Author(s):  
Guillaume Paré ◽  
Shihong Mao ◽  
Wei Q. Deng
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Learning Techniques ◽  
Diabetes Status ◽  
Polygenic Traits ◽  
The Uk ◽  
Prediction Problems

AbstractMachine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N=130K; 1.98M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p<2.2x10−16) and BMI (p<1.57x10−4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N=8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

scholarly journals Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

2022 ◽  
Author(s):  
Ganesh B Chand ◽  
Pankhuri Singhal ◽  
Dominic B Dwyer ◽  
Junhao Wen ◽  
Guray Erus ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Gray Matter Volume ◽  
Control Sample ◽  
Population Level ◽  
Population Based ◽  
Risk Scores ◽  
Support Vector ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
Independent Population

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Abstract 16105: Depression Modulates Polygenic Risk of Cardiovascular and Cardiometabolic Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Michael C Honigberg ◽  
Amy Sarma ◽  
Nandita Scott ◽  
Malissa J Wood ◽  
Pradeep Natarajan
Keyword(s):  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Health Behaviors ◽  
Genetic Risk ◽  
Cardiometabolic Disease ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Artery Disease ◽  
The Uk

Introduction: Depression is associated with an increased risk of coronary artery disease (CAD). Whether depression modifies genetic risk of cardiovascular and cardiometabolic disease is unknown. Methods: We included genotyped, unrelated European ancestry individuals in the UK Biobank. Using genome-wide significant single nucleotide polymorphisms (SNPs) from studies external to the UK Biobank, we generated polygenic risk scores (PRS) for coronary artery disease (CAD, 74 SNPs), hypertension (75 SNPs), type 2 diabetes (T2D, 64 SNPs), atrial fibrillation (25 SNPs), and ischemic stroke (11 SNPs). Participants were stratified by PRS for each condition as low (quintile 1), intermediate (quintiles 2-4), and high (quintile 5) genetic risk. Cox models tested the association of depression frequency with each incident condition among individuals with high PRS, with adjustment for age, sex, the first 20 principal components, genotyping array, and Townsend deprivation index. Additional models further adjusted for health behaviors (exercise, tobacco and alcohol use, vegetable and fresh fruit intake) and tested associations across the PRS spectrum. Results: Among 348,083 individuals, 78,664 (22.6%) reported depression in the past 2 weeks, including 14,776 (4.2%) with depression more than half of days. Depression burden modified the risk of incident CAD across the spectrum of CAD polygenic risk (Figure 1A). Among individuals with high PRS, lack of depression was associated with lower risk of incident CAD (HR 0.70, 95% 0.58-0.86), hypertension (HR 0.58, 95% CI 0.50-0.67), T2D (HR 0.48, 95% CI 0.41-0.55), and atrial fibrillation (HR 0.74, 95% CI 0.62-0.89) compared to those with a high burden of depression. These risk reductions were minimally attenuated after further adjustment for health behaviors (Figure 1B). Conclusions: Lower burden of depression was associated was decreased risks of cardiovascular disease among individuals at high genetic cardiovascular risk.

Chromosomal scale length variation of germline DNA can predict individual cancer risk

2018 ◽  
Author(s):  
Chris Toh ◽  
James P. Brody
Keyword(s):  
Cancer Risk ◽  
Genetic Risk ◽  
Germ Line ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Length Variation ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
The Uk ◽  
Scale Length

AbstractInherited factors are thought to be responsible for a substantial fraction of many different forms of cancer. However, individual cancer risk cannot currently be well quantified by analyzing germ line DNA. Most analyses of germline DNA focus on the additive effects of single nucleotide polymorphisms (SNPs) found. Here we show that chromosomal-scale length variation of germline DNA can be used to predict whether a person will develop cancer. In two independent datasets, the Cancer Genome Atlas (TCGA) project and the UK Biobank, we could classify whether or not a patient had a certain cancer based solely on chromosomal scale length variation. In the TCGA data, we found that all 32 different types of cancer could be predicted better than chance using chromosomal scale length variation data. We found a model that could predict ovarian cancer in women with an area under the receiver operator curve, AUC=0.89. In the UK Biobank data, we could predict breast cancer in women with an AUC=0.83. This method could be used to develop genetic risk scores for other conditions known to have a substantial genetic component and complements genetic risk scores derived from SNPs.

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