A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels

The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals.However,the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels(i.e.,sparse,low sparse,non-sparse).Actually,the real association patterns related to different host phenotypes are not unique.Here,we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels,designated as MiATDS.In particular,we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information.We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method.We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power.By applying to real data analysis,MiATDS displays reliable practicability too.The R package is available at https://github.com/XiaoyunHuang33/MiATDS.

Implicit Hypotheses Are Hidden Power Droppers in Family-Based Association Studies of Secondary Outcomes

Family-based tests of association between a genetic marker and a disease constitute a common design to dissect the genetic architecture of complex traits. The FBAT software is one of the most popular tools to perform such studies. However, researchers are also often interested in the genetic contribution to a more specific manifestation of the phenotype (e.g. severe vs. non-severe form) known as a secondary outcome. Here, what we demonstrate is the limited power of the classical formulation of the FBAT statistic to detect the effect of genetic variants that influence a secondary outcome, in particular when these variants also impact on the onset of the disease, the primary outcome. We prove that this loss of power is driven by an implicit hypothesis, and we propose a derivation of the original FBAT statistic, free from this implicit hypothesis. Finally, we demonstrate analytically that our new statistic is robust and more powerful than FBAT for the detection of association between a genetic variant and a secondary outcome.

Factors Related to the Reading Nutritional Labels by Consumers

This study aimed to evaluate the features related to consumers’ reading nutritional labels in a city in the interior of the São Paulo State, Brazil. A questionnaire was answered by 100 consumers of a supermarket chain, sociodemographic information and data related to label reading habits were collected. Tables with percentage values and bar graphs were used. Chi-square tests and logistic regression models were performed to verify the association between the variables and the label reading habits. The factors that showed significant associations with the reading labels were gender, ease to understand the labels and access to their information (p 0.10). People who had already read labels reported to have more difficulty to understand the information contained on them, and people who had already received instructions on the labels were three and a half times more likely to read the instructions contained on them than those who hadn’t received any guidance. This study points to the need to expand the disclosure to consumers about the contents present on the labels, through more accessible language, so that the labels fulfill their role to instruct consumers in their choices.

Intensity of the Reverse Sexual Double Standards in Females and Males and Possible Factors

Despite past researches on the sexual double standards(SDS),recent research has found that another type of sexual double standard exists,which is the reverse sexual double standard(i.e.reverse SDS,which means that men are judged more harshly than women in comparative sexual behaviors).According to previous researchers,the reverse SDS may stem from women’s worry about being infected by transmitted sexual diseases,women’s preference for men’s virginity in sexual behaviors,and the shift in attitude towards men’s sexual behaviors.Also,women tend to judge men’s sexual behaviors based on men’s attractiveness(body appearance and financial status).The present study investigated people’s implicit endorsement of the reverse SDS among 200 adults in Shanghai(20 to 50 years old,110 women,90 men)using the Implicit Association Test(IAT)with a priming procedure to limit participants’awareness including different levels of men’s body appearance and financial status.Additionally,SDSS(sexual double standard scale)was used to measure the explicit endorsement for the reverse SDS.According to my results,adults in Shanghai endorsed both an implicit and explicit reverse SDS,but women tend to endorse it more strongly than men.Also,when men are more attractive,women will judge them less harshly.These results indicate that the reverse SDS also exists in men.Implications and limitations are indicated for future researchers to call for attention to the reverse SDS and gender equality.

A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

Headache is one of the commonest complaints that doctors need to address in clinical settings.The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated.In this study,we performed a meta-analysis of genome-wide association studies(GWAS)on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test(metaUSAT)software for genetically correlated phenotypes(N=397,385).We identified 38 loci for headaches,of which 34 loci have been reported before and four loci were newly suggested.The LDL receptor related protein 1(LRP1)-Signal Transducer and Activator of Transcription 6(STAT6)-Short chain Dehydrogenase/Reductase family 9C member 7(SDR9C7)region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24×10^(-62)of rs11172113.The One Cut homeobox 2(ONECUT2)gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29×10^(-9)of rs673939.Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches.This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.

Transcriptome wide association studies: general framework and methods

Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.

The abstract of doctoral dissertation‘Some research on hypothesis testing and nonparametric variable screening problems for high dimensional data’

In this thesis,we construct test statistic for association test and independence test in high dimension,respectively,and study the corresponding theoretical properties under some regularity conditions.Meanwhile,we propose a nonparametric variable screening procedure for sparse additive model with multivariate response in untra-high dimension and established some screening properties.

Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease

Background:Whole-exome sequencing(WES)studies have identified multiple genes enriched for de novo mutations(DNMs)in congenital heart disease(CHD)probands.However,risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene.Methods:In this manuscript,we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants.Through integrative modeling of multiple types of genetic variants,gene-level annotations,and reference data from large population cohorts,our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only.Results:Applied to WES data of 2,645 CHD proband-parent trios,our method identified 15 significant genes,half of which are novel,leading to new insights into the genetic bases of CHD.Conclusion:These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.