Eleanor Feingold

Genome-partitioning of genetic variation for complex traits using common SNPs

We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5–1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

Detectable clonal mosaicism from birth to old age and its relationship to cancer

We detected clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells with the same abnormal karyotype (>5–10%; presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (<0.5%) from birth until 50 years of age, after which it rapidly rises to 2–3% in the elderly. Many of the mosaic anomalies are characteristic of those found in hematological cancers and identify common deleted regions with genes previously associated with these cancers. Although only 3% of subjects with detectable clonal mosaicism had any record of hematological cancer before DNA sampling, those without a previous diagnosis have an estimated tenfold higher risk of a subsequent hematological cancer (95% confidence interval = 6–18).

Comprehensive literature review and statistical considerations for GWAS meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Decreased expression of miR-125b and miR-100 in oral cancer cells contributes to malignancy.

Altered microRNA (miRNA) expression profiles have been observed in numerous malignancies, including oral squamous cell carcinoma (OSCC). However, their role in disease is not entirely clear. Several genetic aberrations are characteristic of OSCC, with amplification of chromosomal band 11q13 and loss of distal 11q being among the most prevalent. It is not known if the expression levels of miRNAs in these regions are altered or whether they play a role in disease. We hypothesize that the expression of miRNAs mapping to 11q are altered in OSCC because of loss or amplification of chromosomal material, and that this contributes to the development and progression of OSCC. We found that miR‐125b and miR‐100 are down‐regulated in OSCC tumor and cell lines, and that transfecting cells with exogenous miR‐125b and miR‐100 significantly reduced cell proliferation and modified the expression of target and nontarget genes, including some that are overexpressed in radioresistant OSCC cells. In conclusion, the down‐regulation of miR‐125b and miR‐100 in OSCC appears to play an important role in the development and/or progression of disease and may contribute to the loss of sensitivity to ionizing radiation.

Genome-wide association study of Alzheimer's disease

In addition to apolipoprotein E (APOE), recent large genome-wide association studies (GWASs) have identified nine other genes/loci (CR1, BIN1, CLU, PICALM, MS4A4/MS4A6E, CD2AP, CD33, EPHA1 and ABCA7) for late-onset Alzheimers disease (LOAD). However, the genetic effect attributable to known loci is about 50%, indicating that additional risk genes for LOAD remain to be identified. In this study, we have used a new GWAS data set from the University of Pittsburgh (1291 cases and 938 controls) to examine in detail the recently implicated nine new regions with Alzheimers disease (AD) risk, and also performed a meta-analysis utilizing the top 1% GWAS single-nucleotide polymorphisms (SNPs) with P<0.01 along with four independent data sets (2727 cases and 3336 controls) for these SNPs in an effort to identify new AD loci. The new GWAS data were generated on the Illumina Omni1-Quad chip and imputed at ∼2.5 million markers. As expected, several markers in the APOE regions showed genome-wide significant associations in the Pittsburg sample. While we observed nominal significant associations (P<0.05) either within or adjacent to five genes (PICALM, BIN1, ABCA7, MS4A4/MS4A6E and EPHA1), significant signals were observed 69–180 kb outside of the remaining four genes (CD33, CLU, CD2AP and CR1). Meta-analysis on the top 1% SNPs revealed a suggestive novel association in the PPP1R3B gene (top SNP rs3848140 with P=3.05E–07). The association of this SNP with AD risk was consistent in all five samples with a meta-analysis odds ratio of 2.43. This is a potential candidate gene for AD as this is expressed in the brain and is involved in lipid metabolism. These findings need to be confirmed in additional samples.

The Gene, Environment Association Studies Consortium (GENEVA): Maximizing the Knowledge Obtained from GWAS by Collaboration Across Studies of Multiple Conditions

Genome‐wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene‐trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene‐environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention. Genet. Epidemiol. 34: 364–372, 2010.

Peripheral Blood Mononuclear Cell Gene Expression Profiles Predict Poor Outcome in Idiopathic Pulmonary Fibrosis

Genome-scale transcriptomic profiling of peripheral blood mononuclear cells from patients with idiopathic pulmonary fibrosis reveals that decreased expression of CD28, ICOS, LCK, and ITK predicts mortality. Gene Signature Predicts Mortality Idiopathic pulmonary fibrosis (IPF) is a fatal disease that progresses at different rates. Although no therapies exist, giving patients a more accurate prognosis is highly desirable. To this end, Herazo-Maya and colleagues searched the genomes of cells circulating in the blood of IPF patients and found that four genes may be indicators of poor outcome. Patients were recruited into discovery or replication cohorts from two different medical centers in the United States and followed until death or completion of the study. In both groups, genetic material was isolated from the patients’ peripheral blood mononuclear cells (PBMCs) and analyzed for increased or decreased expression. These gene expression profiles were then correlated with transplant-free survival (TFS). In the discovery cohort, Herazo-Maya et al. found that underexpression of the genes CD28, ICOS, LCK, and ITK was associated with decreased TFS. These findings were confirmed in the replication cohort. This “genomic model” incorporating the four genes was combined with the clinical outputs age, gender, and forced vital capacity to create an even stronger predictor of poor outcome. The authors suggest that the decreased expression of these genes might be linked to lower percentages of CD4+CD28+ T cells in the PBMC population, which could contribute to a mechanistic understanding of why some IPF patients progress differently than others. The findings of this study have the potential to affect the care of patients with IPF as well as the understanding of disease mechanism. However, the combined genomic and clinical predictor will need to be validated in additional independent cohorts before translation. We aimed to identify peripheral blood mononuclear cell (PBMC) gene expression profiles predictive of poor outcomes in idiopathic pulmonary fibrosis (IPF) by performing microarray experiments of PBMCs in discovery and replication cohorts of IPF patients. Microarray analyses identified 52 genes associated with transplant-free survival (TFS) in the discovery cohort. Clustering the microarray samples of the replication cohort using the 52-gene outcome-predictive signature distinguished two patient groups with significant differences in TFS. We studied the pathways associated with TFS in each independent microarray cohort and identified decreased expression of “The costimulatory signal during T cell activation” Biocarta pathway and, in particular, the genes CD28, ICOS, LCK, and ITK, results confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). A proportional hazards model, including the qRT-PCR expression of CD28, ICOS, LCK, and ITK along with patient’s age, gender, and percent predicted forced vital capacity (FVC%), demonstrated an area under the receiver operating characteristic curve of 78.5% at 2.4 months for death and lung transplant prediction in the replication cohort. To evaluate the potential cellular source of CD28, ICOS, LCK, and ITK expression, we analyzed and found significant correlation of these genes with the PBMC percentage of CD4+CD28+ T cells in the replication cohort. Our results suggest that CD28, ICOS, LCK, and ITK are potential outcome biomarkers in IPF and should be further evaluated for patient prioritization for lung transplantation and stratification in drug studies.

Molecular Anatomy of an Intracranial Aneurysm Coordinated Expression of Genes Involved in Wound Healing and Tissue Remodeling

Background and Purpose— Approximately 6% of human beings harbor an unruptured intracranial aneurysm. Each year in the United States, >30 000 people suffer a ruptured intracranial aneurysm, resulting in subarachnoid hemorrhage. Despite the high incidence and catastrophic consequences of a ruptured intracranial aneurysm and the fact that there is considerable evidence that predisposition to intracranial aneurysm has a strong genetic component, very little is understood with regard to the pathology and pathogenesis of this disease. Methods— To begin characterizing the molecular pathology of intracranial aneurysm, we used a global gene expression analysis approach (SAGE-Lite) in combination with a novel data-mining approach to perform a high-resolution transcript analysis of a single intracranial aneurysm, obtained from a 3-year-old girl. Results— SAGE-Lite provides a detailed molecular snapshot of a single intracranial aneurysm. These data suggest that, at least in this specific case, aneurysmal dilation results in a highly dynamic cellular environment in which extensive wound healing and tissue/extracellular matrix remodeling are taking place. Specifically, we observed significant overexpression of genes encoding extracellular matrix components (eg, COL3A1, COL1A1, COL1A2, COL6A1, COL6A2, elastin) and genes involved in extracellular matrix turnover (TIMP-3, OSF-2), cell adhesion and antiadhesion (SPARC, hevin), cytokinesis (PNUTL2), and cell migration (tetraspanin-5). Conclusions— Although these are preliminary data, representing analysis of only one individual, we present a unique first insight into the molecular basis of aneurysmal disease and define numerous candidate markers for future biochemical, physiological, and genetic studies of intracranial aneurysm. Products of these genes will be the focus of future studies in wider sample sets.

New insights into human nondisjunction of chromosome 21 in oocytes.

Nondisjunction of chromosome 21 is the leading cause of Down syndrome. Two risk factors for maternal nondisjunction of chromosome 21 are increased maternal age and altered recombination. In order to provide further insight on mechanisms underlying nondisjunction, we examined the association between these two well established risk factors for chromosome 21 nondisjunction. In our approach, short tandem repeat markers along chromosome 21 were genotyped in DNA collected from individuals with free trisomy 21 and their parents. This information was used to determine the origin of the nondisjunction error and the maternal recombination profile. We analyzed 615 maternal meiosis I and 253 maternal meiosis II cases stratified by maternal age. The examination of meiosis II errors, the first of its type, suggests that the presence of a single exchange within the pericentromeric region of 21q interacts with maternal age-related risk factors. This observation could be explained in two general ways: 1) a pericentromeric exchange initiates or exacerbates the susceptibility to maternal age risk factors or 2) a pericentromeric exchange protects the bivalent against age-related risk factors allowing proper segregation of homologues at meiosis I, but not segregation of sisters at meiosis II. In contrast, analysis of maternal meiosis I errors indicates that a single telomeric exchange imposes the same risk for nondisjunction, irrespective of the age of the oocyte. Our results emphasize the fact that human nondisjunction is a multifactorial trait that must be dissected into its component parts to identify specific associated risk factors.

Association between Maternal Age and Meiotic Recombination for Trisomy 21

Altered genetic recombination has been identified as the first molecular correlate of chromosome nondisjunction in both humans and model organisms. Little evidence has emerged to link maternal age--long recognized as the primary risk factor for nondisjunction--with altered recombination, although some studies have provided hints of such a relationship. To determine whether an association does exist, chromosome 21 recombination patterns were examined in 400 trisomy 21 cases of maternal meiosis I origin, grouped by maternal age. These recombination patterns were used to predict the chromosome 21 exchange patterns established during meiosis I. There was no statistically significant association between age and overall rate of exchange. The placement of meiotic exchange, however, differed significantly among the age groups. Susceptible patterns (pericentromeric and telomeric exchanges) accounted for 34% of all exchanges among the youngest class of women but only 10% of those among the oldest class. The pattern of exchanges among the oldest age group mimicked the pattern observed among normally disjoining chromosomes 21. These results suggest that the greatest risk factor for nondisjunction among younger women is the presence of a susceptible exchange pattern. We hypothesize that environmental and age-related insults accumulate in the ovary as a woman ages, leading to malsegregation of oocytes with stable exchange patterns. It is this risk, due to recombination-independent factors, that would be most influenced by increasing age, leading to the observed maternal age effect.