Schaid, Daniel J.

A faster expansion rate of abdominal aortic aneurysm (AAA) increases the risk of rupture. Women are at higher risk of rupture than men, but the mechanisms underlying this increased risk are unknown. We investigated whether genetic variants that influence susceptibility for AAA ( CDKN2A-2B, SORT1, DAB2IP, LRP1 and LDLR) are associated with AAA expansion and whether these associations differ by sex in 650 patients with AAA (mean age 70±8 years, 17% women) enrolled in the Mayo Clinic Vascular Disease Biorepository. Women had a mean aneurysm expansion 0.41 mm/year greater than men after adjustment for baseline AAA size. In addition to baseline size, mean arterial pressure (MAP), non-diabetic status, SORT1-rs599839[G] and DAB2IP-rs7025486[A] were associated with greater aneurysm expansion (all p<0.05). The associations of MAP and rs599839[G] were similar in both sexes, while the associations of baseline size, pulse pressure (PP) and rs7025486[A] were stronger in women than men (all p - sex interaction ≤0.02). A three-way interaction of PP*sex* rs7025486[A] was noted in a full-factorial analysis (p=0.007) independent of baseline size and MAP. In the high PP group (≥median), women had a mean growth rate 0.68 mm/year greater per [A] of rs7025486 than men (p - sex interaction =0.003), whereas there was no difference in the low PP group (p - sex interaction =0.8). We demonstrate that variants DAB2IP-rs7025486[A] and SORT1-rs599839[G] are associated with AAA expansion. The association of rs7025486[A] is stronger in women than men and amplified by high PP, contributing to sex differences in aneurysm expansion.

PURPOSE: To estimate the efficacy of contralateral prophylactic mastectomy in women with a personal and family history of breast cancer. PATIENTS AND METHODS: We followed the course of 745 women with a first breast cancer and a family history of breast and/or ovarian cancer who underwent contralateral prophylactic mastectomy at the Mayo Clinic between 1960 and 1993. Family history information and cancer follow-up information were obtained from the medical record, a study-specific questionnaire, and telephone follow-up. Life-tables for contralateral breast cancers, which consider age at first breast cancer, current age, and type of family history, were used to calculate the number of breast cancers expected in our cohort had they not had a prophylactic mastectomy. RESULTS: Of the 745 women in our cohort, 388 were premenopausal (age < 50 years) and 357 were post- menopausal. Eight women developed a contralateral breast cancer. Six events were observed among the premenopausal women, compared with 106.2 predicted, resulting in a risk reduction of 94.4% (95% confidence interval [CI], 87.7% to 97.9%). For the 357 postmenopausal women, 50.3 contralateral breast cancers were predicted, whereas only two were observed, representing a 96.0% risk reduction (95% CI, 85.6% to 99.5%). CONCLUSION: The incidence of contralateral breast cancer seems to be reduced significantly after contralateral prophylactic mastectomy in women with a personal and family history of breast cancer.

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

Abstract Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

AbstractGenetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k+1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.