Twenty years since Joinpoint 1.0: Two major enhancements, their justification, and impact.

Since its release of Version 1.0 in 1998, Joinpoint software developed for cancer trend analysis by a team at the US National Cancer Institute has received a considerable attention in the trend analysis community and it became one of most widely used software for trend analysis. The paper published in Statistics in Medicine in 2000 (a previous study) describes the permutation test procedure to select the number of joinpoints, and Joinpoint Version 1.

Data-driven choice of a model selection method in joinpoint regression.

Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC (). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial to determine the weight between BIC and .

LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.

Large case/control genome-wide association studies (GWAS) often include groups of related individuals with known relationships. When testing for associations at a given locus, current methods incorporate only the familial relationships between individuals. Here, we introduce the chromosome-based Quasi Likelihood Score (cQLS) statistic that incorporates local Identity-By-Descent (IBD) to increase the power to detect associations.

Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies.

We report a new method to estimate the predictive performance of polygenic models for risk prediction and assess predictive performance for ten complex traits or common diseases. Using estimates of effect-size distribution and heritability derived from current studies, we project that although 45% of the variance of height has been attributed to SNPs, a model trained on one million people may only explain 33.4% of variance of the trait.

Pathway analysis of genome-wide association study data highlights pancreatic development genes as susceptibility factors for pancreatic cancer.

Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic cancer cases and 3934 control participants pooled from 12 cohort studies and 8 case-control studies (PanScan).