Powerful Identification of Cis-regulatory SNPs in Human Primary Monocytes Using Allele-Specific Gene Expression

A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.     

eQTL mapping using allele-specific count data is computationally feasible,powerful, and provides individual-specific estimates of genetic effects

Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.     

Genome-wide identification of allele-specific effects on gene expression for single and multiple individuals

The analysis of allele-specific gene expression (ASE) is essential for the mapping of genetic variants that affect gene regulation, and for the identification of alleles that modify disease risk. Although RNA sequencing offers the opportunity to measure expression at allele levels, the availability of powerful statistical methods for mapping ASE in single or multiple individuals is limited. We developed a maximum likelihood model to characterize ASE in the human genome. Approximately 17% of genes displayed an allele-specific effect on gene expression in a single individual. Simulations using our model gave a better performance and improved robustness when compared with the binomial test, with different coverage levels, allelic expression fractions and random noise. In addition, our method can identify ASE in multiple individuals, with enhanced performance. This is helpful in understanding the mechanism of genetic regulation leading to expression changes, alternative splicing variants and even disease susceptibility.     

Cancer outlier differential gene expression detection

We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic (ORT), which is intuitively motivated from the t-statistic, the most commonly used differential gene expression detection method. Using real and simulation studies, we compare the ORT to the recently proposed cancer outlier profile analysis (Tomlins and others, 2005) and the outlier sum statistic of Tibshirani and Hastie (2006). The proposed method often has more detection power and smaller false discovery rates. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/ort.html.     

Genetic mechanisms of age regulation of protein C and blood coagulation.

Blood coagulation activity in humans increases with age. We previously identified two genetic elements, age-related stability element (ASE; GAGGAAG) and age-related increase element (AIE; unique stretch of dinucleotide repeats), which were responsible for age-related stable and increasing expression patterns, respectively, and together recapitulated normal age regulation of the human factor IX (hFIX) gene. Here we report the age-regulatory mechanisms of human anticoagulant protein C (hPC), which shows an age-stable pattern of circulatory levels. The murine protein C gene showed an age-related stable expression pattern in general agreement with that of the hPC. Through longitudinal analyses of transgenic mice carrying hPC minigenes, the hPC gene was found to have a functional age-related stability element (hPC ASE; CAGGAAG) in the 5'-upstream proximal region but was found to lack any age-related increase element. Three other ASE-like sequences present in the hPC gene, GAGGAAA and (G/C)AGGATG, also bound nuclear proteins but were not active in the age regulation of the hPC gene. Functional hPC ASE and hFIX ASE were apparently generated through convergent evolution, and hFIX ASE can fully substitute for the hPC ASE in conferring age-related stable expression pattern of the hPC gene. In the presence of the hPC ASE, hFIX AIE can convert the age-stable expression pattern of the hPC gene to a hFIX-like age-related increase pattern. These results support the universality of ASE and AIE functions across different genes. Clearance of hPC protein from the circulation was not significantly affected by age. We now have established the basic mechanisms responsible for the age-related increase of blood coagulation activity.     

A statistical framework for eQTL mapping using RNA-seq data

RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.     

Use of RNA isolated from feces as a promising tool for the early detection of colorectal cancer

Colorectal cancer is one of the most common forms of cancer worldwide. Early detection would allow patients to be treated surgically and halt the progression of the disease; however, the current methods of early detection are invasive (colonoscopy and sigmoidoscopy) or have low sensitivity (fecal occult blood test). The altered expression of genes in stool samples of patients with colorectal cancer can be determined by RT-PCR. This is a noninvasive and highly sensitive technique for colorectal cancer screening. According to information gathered in this review and our own experience, the use of fecal RNA to determine early alterations in gene expression due to malignancy appears to be a promising alternative to the current detection methods and owing to its low cost could be implemented in public health services.     

Validating Discovered Cis-Acting Regulatory Genetic Variants: Application of an Allele Specific Expression Approach to HapMap Populations

Background

Localising regulatory variants that control gene expression is a challenge for genome research. Several studies have recently identified non-coding polymorphisms associated with inter-individual differences in gene expression. These approaches rely on the identification of signals of association against a background of variation due to other genetic and environmental factors. A complementary approach is to use an Allele-Specific Expression (ASE) assay, which is more robust to the effects of environmental variation and trans-acting genetic factors.

Methodology/Principal Findings

Here we apply an ASE method which utilises heterozygosity within an individual to compare expression of the two alleles of a gene in a single cell. We used individuals from three HapMap population groups and analysed the allelic expression of genes with cis-regulatory regions previously identified using total gene expression studies. We were able to replicate the results in five of the six genes tested, and refined the cis- associated regions to a small number of variants. We also showed that by using multi-populations it is possible to refine the associated cis-effect DNA regions.

Conclusions/Significance

We discuss the efficacy and drawbacks of both total gene expression and ASE approaches in the discovery of cis-acting variants. We show that the ASE approach has significant advantages as it is a cleaner representation of cis-acting effects. We also discuss the implication of using different populations to map cis-acting regions and the importance of finding regulatory variants which contribute to human phenotypic variation.     

Pathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data

We explore the utility of p-value weighting for enhancing the power to detect differential metabolites in a two-sample setting. Related gene expression information is used to assign an a priori importance level to each metabolite being tested. We map the gene expression to a metabolite through pathways and then gene expression information is summarized per-pathway using gene set enrichment tests. Through simulation we explore four styles of enrichment tests and four weight functions to convert the gene information into a meaningful p-value weight. We implement the p-value weighting on a prostate cancer metabolomic dataset. Gene expression on matched samples is used to construct the weights. Under certain regulatory conditions, the use of weighted p-values does not inflate the type I error above what we see for the un-weighted tests except in high correlation situations. The power to detect differential metabolites is notably increased in situations with disjoint pathways and shows moderate improvement, relative to the proportion of enriched pathways, when pathway membership overlaps.     

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.