Gene hunting of the Genetic Analysis Workshop 16 rheumatoid arthritis data using rough set theory

We propose to use the rough set theory to identify genes affecting rheumatoid arthritis risk from the data collected by the North American Rheumatoid Arthritis Consortium. For each gene, we employ generalized dynamic reducts in the rough set theory to select a subset of single-nucleotide polymorphisms (SNPs) to represent the genetic information from this gene. We then group the study subjects into different clusters based on their genotype similarity at the selected markers. Statistical association between disease status and cluster membership is then studied to identify genes associated with rheumatoid arthritis. Based on our proposed approach, we are able to identify a number of statistically significant genes associated with rheumatoid arthritis. Aside from genes on chromosome 6, our identified genes include known disease-associated genes such as PTPN22 and TRAF1. In addition, our list contains other biologically plausible genes, such as ADAM15 and AGPAT2. Our findings suggest that ADAM15 and AGPAT2 may contribute to a genetic predisposition through abnormal angiogenesis and adipose tissue

Two-stage joint selection method to identify candidate markers from genome-wide association studies

The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study

Whole‐brain microcirculation detection after ischemic stroke based on swept‐source optical coherence tomography

The occurrence and development of ischemic stroke are closely related to cerebral blood flow. Real‐time monitoring of cerebral perfusion level is very useful for understanding the mechanisms of the disease. A wide field of view (FOV) is conducive to capturing lesions and observing the progression of the disease. In this paper, we attempt to monitor the whole‐brain microcirculation in middle cerebral artery occlusion (MCAO) rats over time using a wide FOV swept‐source OCT (SS‐OCT) system. A constrained image registration algorithm is used to remove motion artifacts that are prone to occur in a wide FOV angiography. During ischemia, cerebral perfusion levels in the left and right hemispheres, as well as in the whole brain were quantified and compared. Changes in the shape and location of blood vessels were also recorded. The results showed that the trend in cerebral perfusion levels of both hemispheres was highly consistent during MCAO, and the position of the blood vessels varied over time. This work will provide new insights of ischemic stroke and is helpful to assess the effectiveness of potential treatment strategies.En face maximum intensity projections (MIP) of the whole‐brain vascular networks obtained by wide field of view (FOV) swept‐source optical coherence tomography (SS‐OCT) system.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/1/jbio201900122_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/2/jbio201900122.pd

Impact of DWDM at 50GHz spacing in the 2µm waveban

In this paper, we show for the first time the impact of decreasing DWDM channel spacing to 50GHz in the 2mm waveband, using 6x12.5Gbit/s and 2x8Gbit/s OOK signals

FTO gene SNPs associated with extreme obesity in cases, controls and extremely discordant sister pairs

<p>Abstract</p> <p>Background</p> <p>FTO is a gene located in chromosome region 16q12.2. Recently two studies have found associations of several single nucleotide polymorphisms (SNPs) in FTO with body mass index (BMI) and obesity, particularly rs1421085, rs17817449, and rs9939609.</p> <p>Methods</p> <p>We examined these three SNPs in 583 extremely obese women with current BMI greater than 35 kg/m²and lifetime BMI greater than 40 kg/m², and 544 controls who were currently normal weight (BMI<25 kg/m²) and had never been overweight during their lifetimes.</p> <p>Results</p> <p>We detected highly significant associations of obesity with alleles in all three SNPs (p < 10^-9). The strongest association was with rs1421085 (p = 3.04 × 10^-10, OR = 1.75, CI = 1.47–2.08). A subset of 99 cases had extremely discordant sisters with BMI<25 kg/m². The discordant sisters differed in allele and genotype frequencies in parallel with the overall case and control sample. The strongest association was with rs17817449 (z = 3.57, p = 3.6 × 10^-4).</p> <p>Conclusion</p> <p>These results suggest common variability in FTO is associated with increased obesity risk or resistance and may in part account for differences between closely related individuals.</p

Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data

We consider the application of Efron’s empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by this distribution. Here, we generalize Efron’s method to allow for some of the peculiarities of the GAW17 data. In particular, we introduce two ways to extend Efron’s model: a weighted empirical Bayes model and a joint covariance model that allows the model to properly incorporate the annotation information of single-nucleotide polymorphisms (SNPs). In the course of our analysis, we examine several aspects of the possible simulation model, including the identity of the most important genes, the differing effects of synonymous and nonsynonymous SNPs, and the relative roles of covariates and genes in conferring disease risk. Finally, we compare the three methods to each other and to other classifiers (random forest and neural network)

Statistical Power of Model Selection Strategies for Genome-Wide Association Studies

Genome-wide association studies (GWAS) aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers. Because many genes are potentially involved in common diseases and a large number of markers are analyzed, it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects, while controlling false positives at the desired level. Although a number of model selection methods have been proposed in the literature, including marginal search, exhaustive search, and forward search, their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods. This article develops a novel statistical approach for power calculation, derives accurate formulas for the power of different model selection strategies, and then uses the formulas to evaluate and compare these strategies in genetic model spaces. In contrast to previous studies, our theoretical framework allows for random genotypes, correlations among test statistics, and a false-positive control based on GWAS practice. After the accuracy of our analytical results is validated through simulations, they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models. For a specific genetic model, our results clearly reveal how different factors, such as effect size, allele frequency, and interaction, jointly affect the statistical power of each strategy. An example is provided for the application of our approach to empirical research. The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings. We have developed an R package markerSearchPower to implement our formulas, which can be downloaded from the Comprehensive R Archive Network (CRAN) or http://bioinformatics.med.yale.edu/group/

Extended haplotype association study in Crohn’s disease identifies a novel, Ashkenazi Jewish-specific missense mutation in the NF-κB pathway gene, HEATR3

The Ashkenazi Jewish population has a several-fold higher prevalence of Crohn’s disease compared to non-Jewish European ancestry populations and has a unique genetic history. Haplotype association is critical to Crohn’s disease etiology in this population, most notably at NOD2, in which three causal, uncommon, and conditionally independent NOD2 variants reside on a shared background haplotype. We present an analysis of extended haplotypes which showed significantly greater association to Crohn’s disease in the Ashkenazi Jewish population compared to a non-Jewish population (145 haplotypes and no haplotypes with P-value < 10−3, respectively). Two haplotype regions, one each on chromosomes 16 and 21, conferred increased disease risk within established Crohn’s disease loci. We performed exome sequencing of 55 Ashkenazi Jewish individuals and follow-up genotyping focused on variants in these two regions. We observed Ashkenazi Jewish-specific nominal association at R755C in TRPM2 on chromosome 21. Within the chromosome 16 region, R642S of HEATR3 and rs9922362 of BRD7 showed genome-wide significance. Expression studies of HEATR3 demonstrated a positive role in NOD2-mediated NF-κB signaling. The BRD7 signal showed conditional dependence with only the downstream rare Crohn’s disease-causal variants in NOD2, but not with the background haplotype; this elaborates NOD2 as a key illustration of synthetic association