A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with

Influence of air supply velocity on temperature field in the self heating process of coal

The air supply velocity is an important factor affecting the spontaneous combustion of coal. The appropriate air velocity can not only provide the oxygen required for the oxidation reaction, but maintains the good heat storage environment. Therefore, it is necessary to study the influence of the actual air velocity in the pore space on the self-heating process of coal particles. This paper focuses on studying the real space piled up by spherical particles. CFD simulation software is used to establish the numerical model from pore scale. Good fitness of the simulation results with the existing results verifies the feasibility of the calculation method. Later, the calculation conditions are changed to calculate and analyze the velocity field and the temperature field for self-heating of some particles (the surface of the particles is at a certain temperature) and expound the effect of different air supply velocities on gathering and dissipating the heat

Genome-wide association tests by using block information in family data

Abstract By applying an association test to analyze the data sets from Genetic Analysis Workshop 15 Problem 3, we compare power using different haplotype-block information. The results from using both of the two different coding schemes show that the test using tight blocks with limited haplotype diversity within each block is more powerful than that using evenly spaced blocks, and the latter is more powerful than that using single-marker blocks. By using carefully chosen haplotype blocks, the power of association tests may be enhanced.http://deepblue.lib.umich.edu/bitstream/2027.42/117371/1/12919_2007_Article_2513.pd

Testing optimally weighted combination of variants for hypertension

© 2014 Zhao et al.; licensee BioMed Central Ltd. Testing rare variants directly is possible with next-generation sequencing technology. In this article, we propose a sliding-window-based optimal-weighted approach to test for the effects of both rare and common variants across the whole genome. We measured the genetic association between a disease and a combination of variants of a single-nucleotide polymorphism window using the newly developed tests TOW and VW-TOW and performed a sliding-window technique to detect disease-susceptible windows. By applying the new approach to unrelated individuals of Genetic Analysis Workshop 18 on replicate 1 chromosome 3, we detected 3 highly susceptible windows across chromosome 3 for diastolic blood pressure and identified 10 of 48,176 windows as the most promising for both diastolic and systolic blood pressure. Seven of 9 top variants influencing diastolic blood pressure and 8 of 9 top variants influencing systolic blood pressure were found in or close to our top 10 windows

Optical Multipath RF Self-Interference Cancellation Based on Phase Modulation for Full-Duplex Communication

Optical multipath RF self-interference cancellation (SIC) based on phase modulation for full-duplex communication is proposed and demonstrated experimentally. Phase modulation is utilized to convert the RF signal into optical domain, in which the time delay tuning, amplitude tuning and phase inversion for multipath RF SIC are completed. The comprehensive theoretical model of the optical multipath RF SIC system is established, and the factors affecting SIC performance including the time delay, amplitude and phase deviations are analyzed. The experimental results verify the feasibility of the proposed scheme for full-duplex communication with the cancellation depth of 26 dB and 28 dB over 100 MHz at central frequency of 6 GHz and 10 GHz, respectively. A figure of merit of the maximum interference to signal of interest ratio is defined to characterize the SOI recovery capability of optical RF SIC system