Pavement Acceptance Testing: Risk-Controlled Sampling Strategy

Acceptance testing is a critical aspect of the quality control and quality assurance (QC/QA) program to ensure the reliable long-term performance of pavement. A typical acceptance testing specification includes acceptable quality characteristics (AQCs), testing methods, number of samples, sample locations, and acceptance criteria. In the current practice, Indiana Department of Transportation (INDOT) accepts pavement by sampling and testing materials with a pre-determined, very low frequency at random locations, leading to a significant problem: testing results are not truly “representative” of the project because sampling is neither based on a statistical foundation, nor on the reliability concept. This study developed a systematic guideline that has addressed the aforementioned problem of material acceptance testing in four aspects: identifying key material properties for testing, selecting sample locations, designing acceptance criteria, and determining optimal sample size. Key material properties that are critical to the pavement long-term performance are identified by comparing with sensitive material properties in MEPDG. A random sampling mechanism was devised based on two spatial indices to control the spatial pattern of samples to minimize the influence from spatial autocorrelation. Risk-based acceptance criteria was proposed based on statistical methods to control the agency’s risk at a desired level given a specific sampling and testing strategy, based on which optimal sample size is determined from a risk perspective. Cost analysis approaches were developed to estimate the total cost of acceptance testing by integrating the risk of making incorrect decision and enable the determination of optimal sample size from a cost perspective. Additionally, quality control chart was exploited as a complementary tool to ensure the consistency of the pavement quality of a project. The results of this study were validated using real data from INDOT projects, and a web tool that incorporates the newly created methods in this study was developed to assist the field pavement QA practice

Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium

The high genomic density of the single-nucleotide polymorphism (SNP) sets that are typically surveyed in genome-wide association studies (GWAS) now allows the application of haplotype-based methods. Although the choice of haplotype-based vs. individual-SNP approaches is expected to affect the results of association studies, few empirical comparisons of method performance have been reported on the genome-wide scale in the same set of individuals. To measure the relative ability of the two strategies to detect associations, we used a large dataset from the North American Rheumatoid Arthritis Consortium to: 1) partition the genome into haplotype blocks, 2) associate haplotypes with disease, and 3) compare the results with individual-SNP association mapping. Although some associations were shared across methods, each approach uniquely identified several strong candidate regions. Our results suggest that the application of both haplotype-based and individual-SNP testing to GWAS should be adopted as a routine procedure

Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression

Expression quantitative trait loci (eQTL) mapping concerns finding genomic variation to elucidate variation of expression traits. This problem poses significant challenges due to high dimensionality of both the gene expression and the genomic marker data. We propose a multivariate response regression approach with simultaneous variable selection and dimension reduction for the eQTL mapping problem. Transcripts with similar expression are clustered into groups, and their expression profiles are viewed as a multivariate response. Then, we employ our recently developed sparse partial least-squares regression methodology to select markers associated with each cluster of genes. We demonstrate with extensive simulations that our eQTL mapping with multivariate response sparse partial least-squares regression (M-SPLS eQTL) method overcomes the issue of multiple transcript- or marker-specific analyses, thereby avoiding potential elevation of type I error. Additionally, joint analysis of multiple transcripts by multivariate response regression increases power for detecting weak linkages. We illustrate that M-SPLS eQTL compares competitively with other approaches and has a number of significant advantages, including the ability to handle highly correlated genotype data and computational efficiency. We provide an application of this methodology to a mouse data set concerning obesity and diabetes

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large "p" and small "n" paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. Copyright Journal compilation (c) 2010 Royal Statistical Society.