Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

Demographic and Genome Wide Association Analyses According to Muscle Mass Using Data of the Korean Genome and Epidemiology Study

BACKGROUND

Sarcopenia is commonly found in the elderly due to a decline in muscle mass. Many researchers have performed genome-wide association studies (GWAS) to find genetic risk factors of sarcopenia. Although many studies have discovered sarcopenia associated single nucleotide polymorphisms (SNPs), most of them are studies targeting Caucasians. The purpose of this study was to evaluate genetic correlation according to muscle mass in middle aged Koreans using data of the Korean Genome and Epidemiology Study (KOGES), a large population-based genomic cohort study.

METHODS

Baseline participants were 10,030 subjects aged 40 to 69 years who were from Ansan or Anseong in Gyeonggi-do, South Korea. Among them, 9,351 subjects with laboratory data available were included in this study. To identify sarcopenia associated variants, those in the top 30% and bottom 30% of muscle mass index (MMI) were compared. A total of 7,452 people with an MMI of 30-70% were excluded. A total of 1,004 people were also excluded due to missing data. Finally, 895 people were selected for this study. The Korea Biobank Array generated 500,568 SNPs for this dataset.

RESULTS

When subjects were divided into top 30% and bottom 30% of MMI, the top 30% had 169 men and 308 women and the bottom 30% had 220 men and 198 women. In men, age, body mass index (BMI), waist and hip were significantly (P < 0.005) different between top 30% and bottom 30% MMI groups. In women, age, BMI, waist, hip, and hypertension history were significantly different between the two MMI groups. There were 13 significant SNPs in men and 14 significant SNPs in women. Genes associated with variants in men based on the single-nucleotide polymorphism database (dbSNP) were LRP1B containing rs11679458 and RGS6 containing rs11848300. A gene associated with variants in women was Pi4K2A, which contained rs1189312 as a variant. In addition, rs11189312 was associated with expression quantitative trait loci (eQTL) of ZFYVE27 in skeletal muscles and other SNPs of ZFYVE27 (rs10882883, rs17108378, rs35077384) known to be associated with spastic paraplegia. The eQTL analysis revealed that rs11189312 was a variant associated with SNPs of ZFYVE27.

CONCLUSIONS

In the demographic study, significant results were found in BMI, waist, hip, history of hyperlipidemia, and sedentary life status in male group, and significant results were found in BMI, waist, hip, and hypertension history in female group. Variant rs11189312 was found to be a novel variant affecting ZFYVE27 expressed in skeletal muscles, suggesting that rs11189312 might be related to sarcopenia as a novel discovery of this study. Further study is needed to determine the association between sarcopenia and ZFYVE27 known to be associated with spastic paraplegia.

Poisson reduced-rank models with an application to political text data

We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover, we develop consistent rules for determining the dimension of the location from the discrete data. Our main motivation for studying Poisson reduced-rank models arises from applications to political text data, where word counts in a political document are modelled by Poisson random variables. We apply our method to party manifesto data taken from German political parties across seven federal elections following German reunification, to make statistical inferences on the multi-dimensional evolution of party positions.

Transformed low-rank ANOVA models for high-dimensional variable selection

High-dimensional data are often encountered in biomedical, environmental, and other studies. For example, in biomedical studies that involve high-throughput omic data, an important problem is to search for genetic variables that are predictive of a particular phenotype. A conventional solution is to characterize such relationships through regression models in which a phenotype is treated as the response variable and the variables are treated as covariates; this approach becomes particularly challenging when the number of variables exceeds the number of samples. We propose a general framework for expressing the transformed mean of high-dimensional variables in an exponential distribution family via ANOVA models in which a low-rank interaction space captures the association between the phenotype and the variables. This alternative method transforms the variable selection problem into a well-posed problem with the number of observations larger than the number of variables. In addition, we propose a model selection criterion for the new model framework with a diverging number of parameters, and establish the consistency of the selection criterion. We demonstrate the appealing performance of the proposed method in terms of prediction and detection accuracy through simulations and real data analyses.

MicroRNA profiling of dogs with transitional cell carcinoma of the bladder using blood and urine samples

Background

Early signs of canine transitional cell carcinoma (TCC) are frequently assumed to be caused by other lower urinary tract diseases (LUTD) such as urinary tract infections, resulting in late diagnosis of TCC which could be fatal. The development of a non-invasive clinical test for TCC could dramatically reduce mortality. To determine whether microRNAs (miRNAs) can be used as non-invasive diagnostic biomarkers, we assessed miRNA expression in blood and/or urine from dogs with clinically normal bladders (n = 28), LUTD (n = 25), and TCC (n = 17). Expression levels of 5 miRNA associated with TCC pathophysiology (miR-34a, let-7c, miR-16, miR-103b, and miR-106b) were assessed by quantitative real-time PCR.

Results

Statistical analyses using ranked ANOVA identified significant differences in miR-103b and miR-16 levels between urine samples from LUTD and TCC patients (miR-103b, p = 0.002; and miR-16, p = 0.016). No statistically significant differences in miRNA levels were observed between blood samples from LUTD versus TCC patients. Expression levels of miR-34a trended with miR-16, let-7c, and miR-103b levels in individual normal urine samples, however, this coordination was completely lost in TCC urine samples. In contrast, co-ordination of miR-34a, miR-16, let-7c, and miR-103b expression levels was maintained in blood samples from TCC patients.

Conclusions

Our combined data indicate a potential role for miR-103b and miR-16 as diagnostic urine biomarkers for TCC, and that further investigation of miR-103b and miR-16 in the dysregulation of coordinated miRNA expression in bladder carcinogenesis is warranted.

Electronic supplementary material

The online version of this article (10.1186/s12917-017-1259-1) contains supplementary material, which is available to authorized users.

multiDE: a dimension reduced model based statistical method for differential expression analysis using RNA-sequencing data with multiple treatment conditions

Background

The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis using RNA-seq data in the presence of multiple treatment conditions.

Results

We propose a novel method, multiDE, for facilitating DE analysis using RNA-seq read count data with multiple treatment conditions. The read count is assumed to follow a log-linear model incorporating two factors (i.e., condition and gene), where an interaction term is used to quantify the association between gene and condition. The number of the degrees of freedom is reduced to one through the first order decomposition of the interaction, leading to a dramatically power improvement in testing DE genes when the number of conditions is greater than two. In our simulation situations, multiDE outperformed the benchmark methods (i.e. edgeR and DESeq2) even if the underlying model was severely misspecified, and the power gain was increasing in the number of conditions. In the application to two real datasets, multiDE identified more biologically meaningful DE genes than the benchmark methods. An R package implementing multiDE is available publicly at http://homepage.fudan.edu.cn/zhangh/softwares/multiDE.

Conclusions

When the number of conditions is two, multiDE performs comparably with the benchmark methods. When the number of conditions is greater than two, multiDE outperforms the benchmark methods.

PLNseq: a multivariate Poisson lognormal distribution for high-throughput matched RNA-sequencing read count data

High-throughput RNA-sequencing (RNA-seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA-seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be evaluated and taken into account in deriving tests of differential expression. We proposed a novel method 'PLNseq', which uses a multivariate Poisson lognormal distribution to model matched read count data. The correlation is directly modeled through Gaussian random effects, and inferences are made by likelihood methods. A three-stage numerical algorithm is developed to estimate unknown parameters and conduct differential expression analysis. Results using simulated data demonstrate that our method performs reasonably well in terms of parameter estimation, DE analysis power, and robustness. PLNseq also has better control of FDRs than the benchmarks edgeR and DESeq2 in the situations where the correlation is different across the genes but can still be accurately estimated. Furthermore, direct evaluation of correlation through PLNseq enables us to develop a new and more powerful test for DE analysis. Application to a lung cancer study is provided to illustrate the practical utilities of our method. An R package implementing the method is also publicly available.