Schaid, Daniel J.

Abstract Background: Studies demonstrate breast cancer risk reduction of 50-65% with the use of endocrine therapy (ET) and yet drug uptake and adherence in this setting is suboptimal even among high risk women. A Polygenic Risk Score (PRS) comprised of 77 BC genetic susceptibility loci (Single Nucleotide Polymorphisms (SNP)) can provide a personalized risk assessment and potentially influence ET adherence. We assessed ET adherence at 1 year comparing women whose risk estimate increased due to PRS versus women whose risk estimate decreased due to PRS. The effect of ET use on quality of life (hot flashes, night sweats, vaginal dryness, weight gain, joint pain) was evaluated. Methods: Eligible women required either a 5 year Gail Model risk of ≥3% or 10 year IBIS ( International Breast Intervention Study or Tyrer-Cuzick model) of ≥5%. Women with a history of breast cancer (BC) or hereditary BC syndrome were excluded. High risk women were counseled at baseline using their Gail and IBIS risk scores and ET options were discussed including benefits and risks. Participants completed a self-reported questionnaire at baseline to assess their understanding of breast cancer risk and decision to take preventive ET. Blood samples were obtained and genotyped for 77 SNPs, and the odds ratio from the SNPs were used to modify the IBIS and Gail risk estimates. The BC -PRS risk estimate information was shared with study participants that reflected the IBIS and Gail risk estimates for 5 year, 10 year, & lifetime BC risk with and without the PRS. Follow up questionnaires at year 1 were administered to assess drug adherence and ET usage on quality of life. Results: 151 women were enrolled at Mayo Clinic Rochester and CancerCare Manitoba from 2016 to 2017. The median age was 56.1 (range 36-76.4), 35.6% were premenopausal, 98.7% were Caucasian and 64.7% had>1 family member with BC. Median 5yr, 10yr, & lifetime IBIS- PRS risk estimates were 3.8% (2.0-11.5), 7.9% (5.0-23.1), and 25.3% (5.5 to 92.2). At year 1 (n=112 women) 46 % of those with an increase in risk when considering the BC-PRS score and 16 % with a decrease in risk were taking ET ( p< 0.001). Types of ET taken: tamoxifen-18, raloxifene- 3, exemestane -14 and missing-1. Women taking ET reported weight gain ( 19.4% vs 6.7%, p=0.04) and more joint pain ( 27.8 % vs 12%, p=0.04) when compared to women not taking ET. Conclusion: In high risk women, BC-PRS risk estimates in addition to standard BC risk calculators had a significant impact on preventive ET adherence. ET use was associated with weight gain and joint pain. Citation Format: Sandhya Pruthi, Julian O Kim, Daniel Schaid, Andrew Cooke, Christina Kim, Benjamin Goldenberg, Jason Sinnwell, Debjani Grenier, Fergus Couch, Celine Vachon. Impact of the breast cancer polygenic risk score on preventive endocrine therapy adherence and endocrine therapy usage on quality of life - The Genetic Risk Estimate (GENRE) trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD3-03.

1501 Background: Despite BC risk reduction of 50-65% by preventive endocrine therapy (ET), very few at-risk women choose to take them. A woman’s perceived BC risk correlates with uptake of ET. A PRS comprised of 77 BC genetic susceptibility loci (Single Nucleotide Polymorphisms (SNP) improves the accuracy of risk prediction for BC. We examined the impact of the addition of individualized PRS BC risk prediction to standard risk calculator estimates on intent to take BC prevention medication. Methods: Eligible women had ≥5% 10 yr BC Tyrer-Cuzick risk (IBIS) or 5 year Gail score ≥3%, with no history of BC or hereditary BC syndrome. Standard BC risk estimates (IBIS or Gail) were incorporated into the counselling on BC preventive ET. A self-reported questionnaire at baseline quantified intention to take ET and explored factors associated with this decision. Blood samples were obtained and genotyped for 77 SNPs, individualized PRS were calculated then incorporated into IBIS and Gail predictions for 5 yr, 10 yr, & lifetime BC risk. At a second visit, PRS risk & prevention recommendations were revisited. Post visit questionnaires assessed change in intent to take ET. Multivariable linear regression was performed to assess impact of baseline variables on change in intent to take medication. Results: From 2016 to 2017, 151 women in Canada & USA were enrolled, median age: 56.1 (range 36-76.4), 35.6% were premenopausal, 98.7% were Caucasian. Median 5yr, 10yr, & lifetime IBIS risk estimates were 3.8% (2.0-11.5), 7.9% (5.0-23.1), and 25.3% (5.5 to 92.2). PRS increased BC risk estimates in 84 (55.6%) and reduced BC risk estimates in 67 (44%) women. After PRS risk counselling, intention to take ET significantly changed (p<0.001): 41.9% of those with increased PRS were more inclined, and 46.7% of women with decreased PRS were less inclined to take ET. On multivariable regression, increase in PRS (p<0.0001) and less concern about ET side effects (p<0.0001) were associated with greater intent to take ET. Conclusions: In high risk women, PRS significantly changed BC risk estimates & intent to take preventive ET. Further assessments of the impact of PRS scores on compliance with ET are warranted. Clinical trial information: NCT02517593.

Objective— It is unclear to what extent genetic susceptibility variants are shared between peripheral artery disease (PAD) and coronary heart disease (CHD), both manifestations of atherosclerotic vascular disease. We investigated whether common and low-frequency/rare variants in loci associated with CHD are also associated with PAD. Approach and Results— Targeted sequencing of 41 genomic regions associated with CHD in genome-wide association studies was performed in 1749 PAD cases (65±11 years, 61% men) and 1855 controls (60±11 years, 56% men) of European ancestry. PAD cases had a resting/postexercise ankle-brachial index ≤0.9, or history of lower extremity revascularization; controls had no history of PAD. We tested the association of common (defined as minor allele frequency ≥5%) variants with PAD assuming an additive genetic model with adjustment for age and sex. To identify low-frequency/rare variants (minor allele frequency <5%) associated with PAD, we conducted gene-level analyses using sequence kernel association test and permutation test. After Bonferroni correction, we found common variants in SH2B3, ABO , and ZEB2 to be associated with PAD ( P <4.5×10 −5 ). At the gene level, the strongest associations were for LPL and SH2B3 . Conclusions— Targeted sequencing of 41 genomic regions associated with CHD revealed several common variants/genes to be associated with PAD, highlighting the basis of shared genetic susceptibility between CHD and PAD.

AbstractThe familial recurrence risk is the probability a person will have disease, given a reported family history. When family histories are obtained as simple counts of disease among family members, as often obtained in cancer registries or surveys, we propose methods to estimate recurrence risks based on truncated binomial distributions. By this approach, we are able to obtain unbiased estimates of risk for a person with at least k‐affected relatives, where k can be specified to determine how risk varies with k. We also derive robust variances of the recurrence risk estimate, to account for correlations within families, such as those induced by shared genes or shared environment, without explicitly modeling the factors that cause familial correlations. Furthermore, we illustrate how mixture models can be used to account for a sample composed of low‐ and high‐risk families. Using simulations, we illustrate the properties of the proposed methods. Application of our methods to a family history survey of prostate cancer shows that the recurrence risk for prostate cancer increased from 16%, when there was at least one affected relative, to 52%, when there was at least five affected relatives.

ABSTRACT Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

AbstractRecent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT.