Zhaoxia Yu

Simultaneous Genotype Calling and Haplotype Phasing Improves Genotype Accuracy and Reduces False-Positive Associations for Genome-wide Association Studies

We present a novel method for simultaneous genotype calling and haplotype-phase inference. Our method employs the computationally efficient BEAGLE haplotype-frequency model, which can be applied to large-scale studies with millions of markers and thousands of samples. We compare genotype calls made with our method to genotype calls made with the BIRDSEED, CHIAMO, GenCall, and ILLUMINUS genotype-calling methods, using genotype data from the Illumina 550K and Affymetrix 500K arrays. We show that our method has higher genotype-call accuracy and yields fewer uncalled genotypes than competing methods. We perform single-marker analysis of data from the Wellcome Trust Case Control Consortium bipolar disorder and type 2 diabetes studies. For bipolar disorder, the genotype calls in the original study yield 25 markers with apparent false-positive association with bipolar disorder at a p < 10(-7) significance level, whereas genotype calls made with our method yield no associated markers at this significance threshold. Conversely, for markers with replicated association with type 2 diabetes, there is good concordance between genotype calls used in the original study and calls made by our method. Results from single-marker and haplotypic analysis of our methods genotype calls for the bipolar disorder study indicate that our method is highly effective at eliminating genotyping artifacts that cause false-positive associations in genome-wide association studies. Our new genotype-calling methods are implemented in the BEAGLE and BEAGLECALL software packages.

Effect of phenylephrine and ephedrine bolus treatment on cerebral oxygenation in anaesthetized patients

BACKGROUND How phenylephrine and ephedrine treatments affect global and regional haemodynamics is of major clinical relevance. Cerebral tissue oxygen saturation (Sct(O2) )-guided management may improve postoperative outcome. The physiological variables responsible for Sct(O2) changes induced by phenylephrine and ephedrine bolus treatment in anaesthetized patients need to be defined. METHODS A randomized two-treatment cross-over trial was conducted: one bolus dose of phenylephrine (100-200 µg) and one bolus dose of ephedrine (5-20 mg) were given to 29 ASA I-III patients anaesthetized with propofol and remifentanil. , mean arterial pressure (MAP), cardiac output (CO), and other physiological variables were recorded before and after treatments. The associations of changes were analysed using linear-mixed models. RESULTS The CO decreased significantly after phenylephrine treatment [▵CO = -2.1 (1.4) litre min(-1), P<0.001], but was preserved after ephedrine treatment [▵CO = 0.5 (1.4) litre min(-1), P>0.05]. The was significantly decreased after phenylephrine treatment [▵ = -3.2 (3.0)%, P<0.01] but preserved after ephedrine treatment [▵ = 0.04 (1.9)%, P>0.05]. CO was identified to have the most significant association with (P<0.001). After taking CO into consideration, the other physiological variables, including MAP, were not significantly associated with (P>0.05). CONCLUSIONS Associated with changes in CO, decreased after phenylephrine treatment, but remained unchanged after ephedrine treatment. The significant correlation between CO and implies a cause-effect relationship between global and regional haemodynamics.

SNP-based pathway enrichment analysis for genome-wide association studies

BackgroundRecently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.ResultsWe describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA.ConclusionsThe SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

Accuracy and Precision of Continuous Noninvasive Arterial Pressure Monitoring Compared with Invasive Arterial Pressure: A Systematic Review and Meta-analysis

Background: Continuous noninvasive arterial pressure monitoring devices are available for bedside use, but the accuracy and precision of these devices have not been evaluated in a systematic review and meta-analysis. Methods: The authors performed a systematic review and meta-analysis of studies comparing continuous noninvasive arterial pressure monitoring with invasive arterial pressure monitoring. Random-effects pooled bias and SD of bias for systolic arterial pressure, diastolic arterial pressure, and mean arterial pressure were calculated. Continuous noninvasive arterial pressure monitoring was considered acceptable if pooled estimates of bias and SD were not greater than 5 and 8 mmHg, respectively, as recommended by the Association for the Advancement of Medical Instrumentation. Results: Twenty-eight studies (919 patients) were included. The overall random-effect pooled bias and SD were −1.6 ± 12.2 mmHg (95% limits of agreement −25.5 to 22.2 mmHg) for systolic arterial pressure, 5.3 ± 8.3 mmHg (−11.0 to 21.6 mmHg) for diastolic arterial pressure, and 3.2 ± 8.4 mmHg (−13.4 to 19.7 mmHg) for mean arterial pressure. In 14 studies focusing on currently commercially available devices, bias and SD were −1.8 ± 12.4 mmHg (−26.2 to 22.5 mmHg) for systolic arterial pressure, 6.0 ± 8.6 mmHg (−10.9 to 22.9 mmHg) for diastolic arterial pressure, and 3.9 ± 8.7 mmHg (−13.1 to 21.0 mmHg) for mean arterial pressure. Conclusions: The results from this meta-analysis found that inaccuracy and imprecision of continuous noninvasive arterial pressure monitoring devices are larger than what was defined as acceptable. This may have implications for clinical situations where continuous noninvasive arterial pressure is being used for patient care decisions.

Methods to impute missing genotypes for population data

For large-scale genotyping studies, it is common for most subjects to have some missing genetic markers, even if the missing rate per marker is low. This compromises association analyses, with varying numbers of subjects contributing to analyses when performing single-marker or multi-marker analyses. In this paper, we consider eight methods to infer missing genotypes, including two haplotype reconstruction methods (local expectation maximization-EM, and fastPHASE), two k-nearest neighbor methods (original k-nearest neighbor, KNN, and a weighted k-nearest neighbor, wtKNN), three linear regression methods (backward variable selection, LM.back, least angle regression, LM.lars, and singular value decomposition, LM.svd), and a regression tree, Rtree. We evaluate the accuracy of them using single nucleotide polymorphism (SNP) data from the HapMap project, under a variety of conditions and parameters. We find that fastPHASE has the lowest error rates across different analysis panels and marker densities. LM.lars gives slightly less accurate estimate of missing genotypes than fastPHASE, but has better performance than the other methods.

Speckle attenuation in optical coherence tomography by curvelet shrinkage

We describe an algorithm based on shrinkage in the curvelet domain to attenuate speckles in optical coherence tomography (OCT) images. The algorithm exploits the curvelet transforms sparse representation of edge discontinuities that are common in OCT images and its ability to map signals and noise into different areas in the curvelet domain. The speckle attenuation is controlled by a single parameter that determines the threshold in the curvelet domain. Applying the algorithm to OCT images shows significant improvement of image quality.

Impact of phenylephrine administration on cerebral tissue oxygen saturation and blood volume is modulated by carbon dioxide in anaesthetized patients

BACKGROUND Multiple studies have shown that cerebral tissue oxygen saturation (Sct(O(2))) is decreased after phenylephrine treatment. We hypothesized that the negative impact of phenylephrine administration on Sct(O(2)) is affected by arterial blood carbon dioxide partial pressure (Pa(CO(2))) because CO(2) is a powerful modulator of cerebrovascular tone. METHODS In 14 anaesthetized healthy patients, i.v. phenylephrine bolus was administered to increase the mean arterial pressure ~20-30% during hypocapnia, normocapnia, and hypercapnia. Sct(O(2)) and cerebral blood volume (CBV) were measured using frequency domain near-infrared spectroscopy, a quantitative technology. Data collection occurred before and after each treatment. RESULTS Phenylephrine caused a significant decrease in Sct(O(2)) during hypocapnia [ΔSct(O(2)) =-3.4 (1.5)%, P<0.001], normocapnia [ΔSct(O(2)) =-2.4 (1.5)%, P<0.001], and hypercapnia [ΔSct(O(2)) =-1.4 (1.5)%, P<0.01]. Decreases in Sct(O(2)) were significantly different between hypocapnia, normocapnia, and hypercapnia (P<0.001). Phenylephrine also caused a significant decrease in CBV during hypocapnia (P<0.01), but not during normocapnia or hypercapnia. CONCLUSION The negative impact of phenylephrine treatment on Sct(O(2)) and CBV is intensified during hypocapnia while blunted during hypercapnia.

Accuracy of continuous noninvasive hemoglobin monitoring: a systematic review and meta-analysis.

BACKGROUND:Noninvasive hemoglobin (Hb) monitoring devices are available in the clinical setting, but their accuracy and precision against central laboratory Hb measurements have not been evaluated in a systematic review and meta-analysis. METHODS:We conducted a comprehensive search of the literature (2005 to August 2013) with PubMed, Web of Science and the Cochrane Library, reviewed references of retrieved articles, and contacted manufactures to identify studies assessing the accuracy of noninvasive Hb monitoring against central laboratory Hb measurements. Two independent reviewers assessed the quality of studies using recommendations for reporting guidelines and quality criteria for method comparison studies. Pooled mean difference and standard deviation (SD) (95% limits of agreement) across studies were calculated using the random-effects model. Heterogeneity was assessed using the I2 statistic. RESULTS:A total of 32 studies (4425 subjects, median sample size of 44, ranged from 10 to 569 patients per study) were included in this meta-analysis. The overall pooled random-effects mean difference (noninvasive—central laboratory) and SD were 0.10 ± 1.37 g/dL (−2.59 to 2.80 g/dL, I2 = 95.9% for mean difference and 95.0% for SD). In subgroup analysis, pooled mean difference and SD were 0.39 ± 1.32 g/dL (−2.21 to 2.98 g/dL, I2 = 93.0%, 71.4%) in 13 studies conducted in the perioperative setting and were −0.51 ± 1.59 g/dL (−3.63 to 2.62 g/dL, I2 = 83.7%, 96.4%) in 5 studies performed in the intensive care unit setting. CONCLUSIONS:Although the mean difference between noninvasive Hb and central laboratory measurements was small, the wide limits of agreement mean clinicians should be cautious when making clinical decisions based on these devices.

Lipoprotein(a) and Apolipoprotein(a) Isoforms No Association With Coronary Artery Calcification in The Dallas Heart Study

Background—Elevated plasma levels of lipoprotein(a) [Lp(a)] are an independent risk factor for cardiovascular disease in whites. Blacks have 2- to 3-fold higher plasma levels of Lp(a) than whites and yet do not have a correspondingly higher rate of coronary events. It remains unclear whether elevated plasma levels of Lp(a) are an independent risk factor for coronary atherosclerosis in individuals of African descent. Methods and Results—The relationship between plasma levels of Lp(a), apolipoprotein(a) isoform sizes, and the presence of coronary calcium was examined in 761 blacks and 527 whites (men aged >40 years, women aged >45 years) from a population-based sample. No relationship was found between plasma levels of Lp(a), apolipoprotein(a) isoform size, or a combination of these 2 variables and coronary artery calcium (CAC) in whites or blacks. No correlation was observed between plasma levels of Lp(a) and coronary calcium scores in any group, although all black men with very high plasma levels of Lp(a) (>300 &mgr;mol/L; n=7) were CAC-positive. Whites with high plasma levels of Lp(a) plus elevated plasma levels of LDL cholesterol (men) or reduced levels of HDL cholesterol (men and women) or who smoked (women) had a higher prevalence of CAC. In contrast, no joint effects between plasma levels of Lp(a) and other cardiovascular risk factors on coronary calcium were found in blacks. Conclusions—No consistent independent relationship between plasma levels of Lp(a) or apolipoprotein(a) isoform size and coronary calcium was found in whites or blacks.

Implementing random scan Gibbs samplers

SummaryThe Gibbs sampler, being a popular routine amongst Markov chain Monte Carlo sampling methodologies, has revolutionized the application of Monte Carlo methods in statistical computing practice. The performance of the Gibbs sampler relies heavily on the choice of sweep strategy, that is, the means by which the components or blocks of the random vector X of interest are visited and updated. We develop an automated, adaptive algorithm for implementing the optimal sweep strategy as the Gibbs sampler traverses the sample space. The decision rules through which this strategy is chosen are based on convergence properties of the induced chain and precision of statistical inferences drawn from the generated Monte Carlo samples. As part of the development, we analytically derive closed form expressions for the decision criteria of interest and present computationally feasible implementations of the adaptive random scan Gibbs sampler via a Gaussian approximation to the target distribution. We illustrate the results and algorithms presented by using the adaptive random scan Gibbs sampler developed to sample multivariate Gaussian target distributions, and screening test and image data.