Associations Among Multiple Markers and Complex Disease: Models, Algorithms, and Applications

Author(s):  
Themistocles L. Assimes ◽  
Adam B. Olshen ◽  
Balasubramanian Narasimhan ◽  
Richard A. Olshen
Genetics ◽  
2003 ◽  
Vol 164 (3) ◽  
pp. 1161-1173
Author(s):  
Guohua Zou ◽  
Deyun Pan ◽  
Hongyu Zhao

Abstract The identification of genotyping errors is an important issue in mapping complex disease genes. Although it is common practice to genotype multiple markers in a candidate region in genetic studies, the potential benefit of jointly analyzing multiple markers to detect genotyping errors has not been investigated. In this article, we discuss genotyping error detections for a set of tightly linked markers in nuclear families, and the objective is to identify families likely to have genotyping errors at one or more markers. We make use of the fact that recombination is a very unlikely event among these markers. We first show that, with family trios, no extra information can be gained by jointly analyzing markers if no phase information is available, and error detection rates are usually low if Mendelian consistency is used as the only standard for checking errors. However, for nuclear families with more than one child, error detection rates can be greatly increased with the consideration of more markers. Error detection rates also increase with the number of children in each family. Because families displaying Mendelian consistency may still have genotyping errors, we calculate the probability that a family displaying Mendelian consistency has correct genotypes. These probabilities can help identify families that, although showing Mendelian consistency, may have genotyping errors. In addition, we examine the benefit of available haplotype frequencies in the general population on genotyping error detections. We show that both error detection rates and the probability that an observed family displaying Mendelian consistency has correct genotypes can be greatly increased when such additional information is available.


Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1030-1030
Author(s):  
Joerg Schuettrumpf ◽  
Alexander Schlachterman ◽  
Jianxiang Zou ◽  
Christian Furlan Freguia ◽  
Stefano Baila ◽  
...  

Abstract The protein C (PC) pathway plays a major role in the interface between coagulation and inflammation. APC has both anticoagulant and anti-inflammatory properties and is the only effective treatment in patients with severe sepsis. However, assessment of APC’s therapeutic effect on other complex disease models has been compromised by its short half-life (15 min) and by difficulties in monitoring protein levels. To overcome this limitation we used adeno-associated viral (AAV) vectors encoding the PC zymogen or APC for hepatocyte specific gene expression. For direct APC secretion we introduced an extra cleavage site adjacent to the activation peptide for the intracellular protease PACE/furin. Three dose cohorts of C57Bl/6 mice (n=4–6 per group) were injected for either AAV-APC or AAV-PC. A single vector injection resulted in continuous sustained long-term PC or APC expression without signs of liver toxicity. APC functional activity was restricted to AAV-APC-treated mice in which APC plateau levels of 88±43, 162±48, or 263±64 ng/ml were determined in a dose dependent manner. Further, AAV-APC expression consisted mainly of APC because no PC was detected by a zymogen specific ELISA. Only APC expressing mice presented enhanced anticoagulation as determined by 11 to 41 % prolongation of the aPTT values (p<0.05–0.005) and decreased thrombin/antithrombin III complex (TAT) levels (from 30 at baseline to 20, 14, or 12 ng/ml, p<0.05–0.0005). Next, we tested whether APC or PC would provide protection against vascular injury at both micro- and macrocirculation levels of living animals. No thrombus formation was detected in APC expressing mice (n=4) following FeCl3-injury of the carotid artery in contrast to uninjected or PC expressing controls (7 thrombi in 7 mice, p<0.01). Anticoagulant efficacy was then evaluated by real-time imaging of thrombus formation following laser induced arteriole injury using widefield intravital microscopy. In AAV-APC treated mice we observed dose dependent anticoagulation: 8 thrombi /12 injury sites in mice expressing ~80 ng/ml, 3/10 at ~160 ng, and 1/7 at ~260 ng/ml APC compared to 42/42 in untreated controls (P<0.001-0.0001). Expression of PC resulted in prevention of thrombus formation only at the highest expression levels of 4000 ng/ml (5/7, p<0.02) but not at 2000 ng/ml (10/10). When these animals were challenged by tail clipping, blood loss was increased only for mice with the highest APC levels by 2-fold (p<0.05). Moreover, at all levels of APC no changes in wound healing rates were observed following punch biopsy. Treatment of homozygous mice for the factor V Leiden (FVL) mutation with the same vector doses (n=3/group) resulted in a similar anticoagulant effect based on the aPTT with 18–27 % prolongation (p<0.05), or based on TAT-levels, dropping from 56.9 ng/ml at baseline to 28.1, 12.9, or 8.0 ng/ml( p<0.05–0.0005). This data shows that continuous expression of APC can overcome the inherited proteolytic resistance of FVL to APC. In summary, these results demonstrate that APC levels, within the range already obtained in humans by protein infusion (up to 400 ng/ml), provide antithrombotic activity dependent on the injury and/or vessel size. In our model, human APC levels of 160 ng/ml present effective anticoagulant effect without increasing the risk of bleeding. This strategy ensures easy assessment of the role of APC in complex disease models at closely defined circulating levels.


2020 ◽  
Vol 36 (8) ◽  
pp. 2365-2374
Author(s):  
Xiaqiong Wang ◽  
Yalu Wen

Abstract Motivation The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. Results We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. Availability and implementation The R-package is available at https://github.com/YaluWen/Uomic. Supplementary information Supplementary data are available at Bioinformatics online.


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