Pankratz, V. Shane

The most recent late-onset Alzheimer's disease (LOAD) genome-wide association study revealed genome-wide significant association of two new loci: rs744373 near BIN1 (p = 1.6 × 10 −11 ) and rs597668 near EXOC3L2/BLOC1S3/MARK4 (p = 6.5 × 10 −9 ). We have genotyped these variants in a large (3,287 LOAD, 4,396 controls), independent dataset comprising eleven case-control series from the USA and Europe. We performed meta-analyses of the association of these variants with LOAD and also tested for association using logistic regression adjusted by age-at-diagnosis, gender, and APOE ε4 status. Meta-analysis results showed no evidence of series heterogeneity and logistic regression analysis successfully replicated the association of BIN1 (rs744373) with LOAD with an odds ratio (OR = 1.17, p = 1.1 × 10 −4 ) comparable to that previously reported (OR = 1.15). The variant near EXOC3L2 (rs597668) showed only suggestive association with LOAD (p = 0.09) after correcting for the presence of the APOE ε4 allele. Addition of our follow-up data to the results previously reported increased the strength of evidence for association with BIN1 (11,825 LOAD, 32,570 controls, rs744373 Fisher combined p = 3.8 × 10 −20 ). We also tested for epistatic interaction between these variants and APOE ε4 as well as with the previously replicated LOAD GWAS genes (CLU: rs11136000, CR1: rs3818361, and PICALM: rs3851179). No significant interactions between these genes were detected. In summary, we provide additional evidence for the variant near BIN1 (rs744373) as a LOAD risk modifier, but our results indicate that the effect of EXOC3L2 independent of APOE ε4 should be studied further.

Purpose Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24]). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42]). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.

A number of familial diseases have an age‐of‐onset component, which can be considered as a censored quantitative trait. However, few software resources are available for the use of time‐to‐event endpoints in linkage analysis. The purpose of this analysis was to examine the use of martingale residuals from Cox survival models as quantitative traits for familial diseases with variable age at onset. We used these residuals as quantitative traits in variance components linkage scans for the 50 replicates of the general population simulated data for chromosomes 6 and 7. The region on chromosome 6 containing markers D06G034 and D06G035 demonstrated evidence for linkage, consistent with the underlying genetic model. This analysis demonstrates the applicability of using martingale residuals as a quantitative trait in linkage analyses of diseases that depend on age of onset. © 2001 Wiley‐Liss, Inc.

2 Background: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but accurate personalized risk prediction is desirable to facilitate individual clinical management decisions. Currently used models provide poor BC risk prediction for women with AH. Our goal was to develop and validate an improved risk prediction model for women with AH. Methods: From a cohort of 13,538 women with benign breast disease from 1967-2001, pathology review confirmed 699 with AH. Clinical risk factors and histologic features of the tissue biopsy were recorded, and BC events were ascertained from study questionnaires, tumor registry, and review of medical records. Using a lasso approach, 23 variables were assessed for model inclusion. Lasso-identified features were then fit in a Cox regression model to estimate BC risk. Model discrimination was assessed with C-statistics in the model-building set and in a separate external validation set. Calibration was assessed by comparing observed to predicted breast cancer counts. Results: The model-building set comprised 699 women with 142 BC events (median follow-up 8.1 years), and the external validation set comprised 461 women with 114 BC events (median follow-up 11.4 years). The final model included three covariates: age at biopsy, age squared, and number of foci of AH. Model performance was good, with a C-statistic of 0.622 (SE = 0.027) in the model-building set and 0.594 (SE = 0.029) in the external validation set. The model is well-calibrated, with observed to expected numbers of BCs nearly equal across all post-biopsy follow-up years. Conclusions: We propose a new model for predicting BC risk in women with AH based on age at biopsy and number of foci of atypia. This model provides absolute risk estimates for women with AH, has good discriminatory ability, is well-calibrated, and validates in an external cohort.

Abstract Introduction Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. Methods We conducted a transethnic genome‐wide association study (GWAS) for late‐onset AD in Stage 1 sample including whites of European Ancestry, African‐Americans, Japanese, and Israeli‐Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Results Genome‐wide significant (GWS) associations in single‐nucleotide polymorphism (SNP)–based tests ( P < 5 × 10 −8 ) were identified for SNPs in PFDN1/HBEGF , USP6NL/ECHDC3 , and BZRAP1‐AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence ( P < 2.7 × 10 −6 ) for gene‐based association in the total sample with a novel locus, TPBG ( P = 1.8 × 10 −6 ). Discussion Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci.