Prevalence, risk factors, and surveillance patterns for gastric intestinal metaplasia among patients undergoing upper endoscopy with biopsy.

BACKGROUND AND AIMS: Gastric intestinal metaplasia (GIM) is an important precursor lesion to gastric cancer (GC), the second leading cause of cancer death worldwide. There exist few data regarding the prevalence of, risk factors for, and clinical practice patterns regarding GIM in the United States. Furthermore, there are currently no U.S. guidelines regarding screening/surveillance for GIM. METHODS: All consecutive upper endoscopic procedures from 2 academic medical centers in Seattle between 1999 and 2014 were reviewed. Demographic, clinical, and endoscopic covariates were recorded at time of endoscopy. Procedures with gastric biopsy were matched to final the histologic diagnoses, including the presence of Helicobacter pylori. Cases of GIM and dysplasia were recorded and compared with non-GIM controls using univariate and multivariable regression. Surveillance patterns for cases of GIM were recorded. RESULTS: Data from 36,799 upper endoscopies, 17,710 gastric biopsies, 2073 cases of GIM, 43 cases of dysplasia, and 78 cases of GC were captured. The point prevalence of GIM was 11.7% in patients who underwent gastric biopsy. Non-white race (P < .001), increasing age (P < .001), and presence of H pylori (P < .001) were associated with GIM. If GIM was present, increasing age (P < .001) and male gender (P < .001) were associated with progression, and the presence of H pylori (P < .001) was inversely associated with progression to dysplasia/GC. Few cases of GIM/dysplasia/GC were identified during procedures for GIM screening/surveillance. Only 16% of patients with a diagnosis of GIM received a recommendation for surveillance. CONCLUSIONS: There is a high prevalence of GIM among non-white and Hispanic Americans. Risk factors for development of GIM may be distinct from the risk factors for progression to GC.

Most genetic risk for autism resides with common variation.

A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from rare variation, especially de novo mutations. From this evidence, one might conclude that rare variation dominates the allelic spectrum in autism, yet recent studies show that common variation, individually of small effect, has substantial impact en masse. At issue is how much of an impact relative to rare variation this common variation has. Using a unique epidemiological sample from Sweden, new methods that distinguish total narrow-sense heritability from that due to common variation and synthesis of results from other studies, we reach several conclusions about autism's genetic architecture: its narrow-sense heritability is ∼52.4%, with most due to common variation, and rare de novo mutations contribute substantially to individual liability, yet their contribution to variance in liability, 2.6%, is modest compared to that for heritable variation.

REFINING GENETICALLY INFERRED RELATIONSHIPS USING TREELET COVARIANCE SMOOTHING.

Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically-inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multiscale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We illustrate our method with a large genome-wide association study and estimate the "heritability" of body mass index quite accurately. Traditionally heritability, defined as the fraction of the total trait variance attributable to additive genetic effects, is estimated from samples of closely related individuals using random effects models. We show that by using smoothed relationship matrices we can estimate heritability using population-based samples. Finally, while our methods have been developed for refining genetic relationship matrices and improving estimates of heritability, they have much broader potential application in statistics. Most notably, for error-in-variables random effects models and settings that require regularization of matrices with block or hierarchical structure.

Transport of Bacillus thuringiensis var. kurstaki via fomites.

The intentional and controlled release of an aerosolized bacterium provides an opportunity to investigate the implications of a biological attack. Since 2006, Los Alamos National Laboratory has worked with several urban areas, including Fairfax County, VA, to design experiments to evaluate biodefense concepts of operations using routine spraying of Bacillus thuringiensis var. kurstaki (Btk). Btk is dispersed in large quantities as a slurry to control the gypsy moth, Lymantria dispar. Understanding whether personnel and equipment pick up residual contamination during sampling activities and transport it to other areas is critical for the formulation of appropriate response and recovery plans. While there is a growing body of literature surrounding the transmission of viral diseases via fomites, there is limited information on the transport of Bacillus species via this route. In 2008, LANL investigated whether field sampling activities conducted near sprayed areas, post-spray, resulted in measurable cross-contamination of sampling personnel, equipment, vehicles, and hotel rooms. Viable Btk was detected in all sample types, indicating transport of the agent occurred via fomites.

An optimal transportation approach for nuclear structure-based pathology.

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

A SPECTRAL GRAPH APPROACH TO DISCOVERING GENETIC ANCESTRY.

Mapping human genetic variation is fundamentally interesting in fields such as anthropology and forensic inference. At the same time, patterns of genetic diversity confound efforts to determine the genetic basis of complex disease. Due to technological advances, it is now possible to measure hundreds of thousands of genetic variants per individual across the genome. Principal component analysis (PCA) is routinely used to summarize the genetic similarity between subjects. The eigenvectors are interpreted as dimensions of ancestry. We build on this idea using a spectral graph approach. In the process we draw on connections between multidimensional scaling and spectral kernel methods. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. The method is stable to outliers and can more easily incorporate different similarity measures of genetic data than PCA. We illustrate a new algorithm for genetic clustering and association analysis on a large, genetically heterogeneous sample.

Discovering genetic ancestry using spectral graph theory.

As one approach to uncovering the genetic underpinnings of complex disease, individuals are measured at a large number of genetic variants (usually SNPs) across the genome and these SNP genotypes are assessed for association with disease status. We propose a new statistical method called Spectral-GEM for the analysis of genome-wide association studies; the goal of Spectral-GEM is to quantify the ancestry of the sample from such genotypic data. Ignoring structure due to differential ancestry can lead to an excess of spurious findings and reduce power. Ancestry is commonly estimated using the eigenvectors derived from principal component analysis (PCA). To develop an alternative to PCA we draw on connections between multidimensional scaling and spectral graph theory. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. Often the results from Spectral-GEM are straightforward to interpret and therefore useful in association analysis. We illustrate the new algorithm with an analysis of the POPRES data [Nelson et al., 2008].

On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants.

Resources being amassed for genome-wide association (GWA) studies include "control databases" genotyped with a large-scale SNP array. How to use these databases effectively is an open question. We develop a method to match, by genetic ancestry, controls to affected individuals (cases). The impact of this method, especially for heterogeneous human populations, is to reduce the false-positive rate, inflate other spuriously small p values, and have little impact on the p values associated with true positive loci. Thus, it highlights true positives by downplaying false positives. We perform a GWA by matching Americans with type 1 diabetes (T1D) to controls from Germany. Despite the complex study design, these analyses identify numerous loci known to confer risk for T1D.

Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization.

We provide evidence that nonlinear dimensionality reduction, clustering, and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to noise. Our construction, which is based on a Markov random walk on the data, offers a general scheme of simultaneously reorganizing and subsampling graphs and arbitrarily shaped data sets in high dimensions using intrinsic geometry. We show that clustering in embedding spaces is equivalent to compressing operators. The objective of data partitioning and clustering is to coarse-grain the random walk on the data while at the same time preserving a diffusion operator for the intrinsic geometry or connectivity of the data set up to some accuracy. We show that the quantization distortion in diffusion space bounds the error of compression of the operator, thus giving a rigorous justification for k-means clustering in diffusion space and a precise measure of the performance of general clustering algorithms.