A HapMap harvest of insights into the genetics of common disease

Overview of the development of personalized genomic medicine and surgery

Personalized genomic medicine and surgery (PGMS) represents a new approach to health care that customizes patients' medical treatment according to their own genetic information. This new approach is the result of increased knowledge of the human genome and ways this information can be applied by physicians in the medical and surgical management of their patients. A patient's genotype can yield important information concerning disease susceptibility and the effectiveness of medications, therefore guiding specific, targeted imaging and treatment therapies. This review summarizes major achievements of human genomic studies and applications of genomics in health care. Five years ago we developed a model for the development of PGMS in which genomic profile guides choice of therapy. In this article we discussed our progress, including an updating of the model, and a future vision of PGMS.

The PhenX Toolkit: get the most from your measures

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

Genome-wide association study of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Europe

BACKGROUND

Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) are rare but extremely severe cutaneous adverse drug reactions in which drug-specific associations with HLA-B alleles were described.

OBJECTIVES

To investigate genetic association at a genome-wide level on a large sample of SJS/TEN patients.

METHODS

We performed a genome wide association study on a sample of 424 European cases and 1,881 controls selected from a Reference Control Panel.

RESULTS

Six SNPs located in the HLA region showed significant evidence for association (OR range: 1.53-1.74). The haplotype formed by their risk allele was more associated with the disease than any of the single SNPs and was even much stronger in patients exposed to allopurinol (OR(allopurinol) = 7.77, 95%CI = [4.66; 12.98]). The associated haplotype is in linkage disequilibrium with the HLA-B*5801 allele known to be associated with allopurinol induced SJS/TEN in Asian populations.

CONCLUSION

The involvement of genetic variants located in the HLA region in SJS/TEN is confirmed in European samples, but no other locus reaches genome-wide statistical significance in this sample that is also the largest one collected so far. If some loci outside HLA play a role in SJS/TEN, their effect is thus likely to be very small.

Personalised medicine in Canada: a survey of adoption and practice in oncology, cardiology and family medicine

Introduction In order to provide baseline data on genetic testing as a key element of personalised medicine (PM), Canadian physicians were surveyed to determine roles, perceptions and experiences in this area. The survey measured attitudes, practice, observed benefits and impacts, and barriers to adoption. Methods A self-administered survey was provided to Canadian oncologists, cardiologists and family physicians and responses were obtained online, by mail or by fax. The survey was designed to be exploratory. Data were compared across specialties and geography. Results The overall response rate was 8.3%. Of the respondents, 43%, 30% and 27% were family physicians, cardiologists and oncologists, respectively. A strong majority of respondents agreed that genetic testing and PM can have a positive impact on their practice; however, only 51% agreed that there is sufficient evidence to order such tests. A low percentage of respondents felt that they were sufficiently informed and confident practicing in this area, although many reported that genetic tests they have ordered have benefited their patients. Half of the respondents agreed that genetic tests that would be useful in their practice are not readily available. A lack of practice guidelines, limited provider knowledge and lack of evidence-based clinical information were cited as the main barriers to practice. Differences across provinces were observed for measures relating to access to testing and the state of practice. Differences across specialties were observed for the state of practice, reported benefits and access to testing. Conclusions Canadian physicians recognise the benefits of genetic testing and PM; however, they lack the education, information and support needed to practice effectively in this area. Variability in practice and access to testing across specialties and across Canada was observed. These results support a need for national strategies and resources to facilitate physician knowledge, training and practice in PM.

The genomic architecture of sporadic heart failure

Common or sporadic systolic heart failure (heart failure) is the clinical syndrome of insufficient forward cardiac output resulting from myocardial disease. Most heart failure is the consequence of ischemic or idiopathic cardiomyopathy. There is a clear familial predisposition to heart failure, with a genetic component estimated to confer between 20% and 30% of overall risk. The multifactorial etiology of this syndrome has complicated identification of its genetic underpinnings. Until recently, almost all genetic studies of heart failure were designed and deployed according to the common disease-common variant hypothesis, in which individual risk alleles impart a small positive or negative effect and overall genetic risk is the cumulative impact of all functional genetic variations. Early studies used a candidate gene approach focused mainly on factors within adrenergic and renin-angiotensin pathways that affect heart failure progression and are targeted by standard pharmacotherapeutics. Many of these reported allelic associations with heart failure have not been replicated. However, the preponderance of data supports risk-modifier effects for the Arg389Gly polymorphism of β1-adrenergic receptors and the intron 16 in/del polymorphism of angiotensin-converting enzyme. Recent unbiased studies using genome-wide single nucleotide polymorphism microarrays have shown fewer positive results than when these platforms were applied to hypertension, myocardial infarction, or diabetes, possibly reflecting the complex etiology of heart failure. A new cardiovascular gene-centric subgenome single nucleotide polymorphism array identified a common heat failure risk allele at 1p36 in multiple independent cohorts, but the biological mechanism for this association is still uncertain. It is likely that common gene polymorphisms account for only a fraction of individual genetic heart failure risk, and future studies using deep resequencing are likely to identify rare gene variants with larger biological effects.

An examination of single nucleotide polymorphism selection prioritization strategies for tests of gene-gene interaction

BACKGROUND

Given that genome-wide association studies (GWAS) of psychiatric disorders have identified only a small number of convincingly associated variants (single nucleotide polymorphism [SNP]), there is interest in seeking additional evidence for associated variants with tests of gene-gene interaction. Comprehensive pair-wise single SNP-SNP interaction analysis is computationally intensive, and the penalty for multiple testing is severe, given the number of interactions possible. Aiming to minimize these statistical and computational burdens, we have explored approaches to prioritize SNPs for interaction analyses.

METHODS

Primary interaction analyses were performed with the Wellcome Trust Case-Control Consortium bipolar disorder GWAS (1868 cases, 2938 control subjects). Replication analyses were performed with the Genetic Association Information Network bipolar disorder dataset (1001 cases, 1033 control subjects). The SNPs were prioritized for interaction analysis that showed evidence for association that surpassed a number of nominally significant thresholds, are within genome-wide significant genes, or are within genes that are functionally related.

RESULTS

For no set of prioritized SNPs did we obtain evidence to support the hypothesis that the selection strategy identified pairs of variants that were enriched for true (statistical) interactions.

CONCLUSIONS

The SNPs prioritized according to a number of criteria do not have a raised prior probability for significant interaction that is detectable in samples of this size. We argue that the use of significance levels reflecting only the number of tests performed, as is now widely accepted for single SNP analysis, does not offer an appropriate degree of protection against the potential for GWAS studies to generate an enormous number of false positive interactions.

Contributions of dopamine-related genes and environmental factors to highly sensitive personality: a multi-step neuronal system-level approach

Traditional behavioral genetic studies (e.g., twin, adoption studies) have shown that human personality has moderate to high heritability, but recent molecular behavioral genetic studies have failed to identify quantitative trait loci (QTL) with consistent effects. The current study adopted a multi-step approach (ANOVA followed by multiple regression and permutation) to assess the cumulative effects of multiple QTLs. Using a system-level (dopamine system) genetic approach, we investigated a personality trait deeply rooted in the nervous system (the Highly Sensitive Personality, HSP). 480 healthy Chinese college students were given the HSP scale and genotyped for 98 representative polymorphisms in all major dopamine neurotransmitter genes. In addition, two environment factors (stressful life events and parental warmth) that have been implicated for their contributions to personality development were included to investigate their relative contributions as compared to genetic factors. In Step 1, using ANOVA, we identified 10 polymorphisms that made statistically significant contributions to HSP. In Step 2, these polymorphism's main effects and interactions were assessed using multiple regression. This model accounted for 15% of the variance of HSP (p<0.001). Recent stressful life events accounted for an additional 2% of the variance. Finally, permutation analyses ascertained the probability of obtaining these findings by chance to be very low, p ranging from 0.001 to 0.006. Dividing these loci by the subsystems of dopamine synthesis, degradation/transport, receptor and modulation, we found that the modulation and receptor subsystems made the most significant contribution to HSP. The results of this study demonstrate the utility of a multi-step neuronal system-level approach in assessing genetic contributions to individual differences in human behavior. It can potentially bridge the gap between the high heritability estimates based on traditional behavioral genetics and the lack of reproducible genetic effects observed currently from molecular genetic studies.

The emerging role for rat models in gene discovery

Rat models have been used for many decades to study physiological and pathophysiological mechanisms. Prior to the release of the rat genome and new technologies for targeting gene manipulation, the rat had been the underdog in the genomics era, despite the abundance of physiological data compared to the mouse. The overarching goal of biomedical research is to improve health and advance medical science. Translating human disease gene discovery and validation in the rat, through the use of emerging technologies and integrated tools and databases, is providing power to understand the genetics, environmental influences, and biology of disease. In this review we briefly outline the rat models, bioinformatics tools, and technologies that are changing the landscape of translational research. The strategies used to translate disease traits to genes to function, and, ultimately, to improve human health is discussed. Finally, our perspective on how rat models will continue to positively impact biomedical research is provided.

Susceptibility genes, social environmental risk factors and their interactions in internalizing disorders among mainland Chinese undergraduates

BACKGROUND

Analyses of large scale psychiatric epidemiology surveys suggest that common mental disorders can be generalized into two underlying dimensions, internalizing disorders (e.g. depression, and anxiety) and externalizing disorders (e.g. addictions). The present study explores the interactions among the genes (SLC6A4, BDNF, and MAOA) and selected environment factors (G×E), and gene×gene (G×G) interactions for internalizing disorders in the sample of Chinese university students.

METHODS

This is a genetic case control study. 259 undergraduates affected with internalizing disorders and 269 healthy controls were included. Multiple logistic regression was used to explore the potential environmental risk factors. G×E interactions were analyzed using the method developed by Mukherjee et al. (2008). Analyses of G×G interactions were conducted by generalized multifactor dimensionality reduction (GMDR) (Lou et al., 2007). We have previously reported on the polymorphism information for ten single nucleotide polymorphisms (SNPs) on SLC6A4, BDNF, and MAOA genes (Meng et al., 2009; Meng, 2010).

RESULTS

We found that an offspring's reported parents' negative mood, subject's unwillingness to express emotion, being an only child, having an unfulfilling relationship with his/her mother, and increased activity and behavior restrictions by his/her father were social environment risk factors for internalizing disorders. No statistically significant interactions were found in the G×E analyses. G×G analyses found that subjects with mutational alleles concurrently on rs10835210 and rs2030324 (BDNF gene) were significantly likely to develop internalizing disorders. Those with one mutated allele on either SNPs were 1.761 times, and those with both mutated alleles were 3.353 times, more likely to develop internalizing disorders.

CONCLUSION

A negative family emotional environment was found to be associated with internalizing disorders. BDNF gene variants were also found to be similarly associated. Using conservative statistical analyses we did not find any G×E interactions.

The genetics of asthma and allergic disease: a 21st century perspective

Asthma and allergy are common conditions with complex etiologies involving both genetic and environmental contributions. Recent genome-wide association studies (GWAS) and meta-analyses of GWAS have begun to shed light on both common and distinct pathways that contribute to asthma and allergic diseases. Associations with variation in genes encoding the epithelial cell-derived cytokines, interleukin-33 (IL-33) and thymic stromal lymphopoietin (TSLP), and the IL1RL1 gene encoding the IL-33 receptor, ST2, highlight the central roles for innate immune response pathways that promote the activation and differentiation of T-helper 2 cells in the pathogenesis of both asthma and allergic diseases. In contrast, variation at the 17q21 asthma locus, encoding the ORMDL3 and GSDML genes, is specifically associated with risk for childhood onset asthma. These and other genetic findings are providing a list of well-validated asthma and allergy susceptibility genes that are expanding our understanding of the common and unique biological pathways that are dysregulated in these related conditions. Ongoing studies will continue to broaden our understanding of asthma and allergy and unravel the mechanisms for the development of these complex traits.

Ex-Vivo Modeling for Heritability Assessment and Genetic Mapping in Pharmacogenomics

The investigation of genetic factors that determine differential drug response is a key goal of pharmacogenomics (PGX), and relies on the often-untested assumption that differential response is heritable. While limitations in traditional study design often prohibit heritability (h²) estimates in PGX, new approaches may allow such estimates. We demonstrate an ex vivo model system to determine the h² of drug-induced cell killing and performed genome-wide analysis for gene mapping. The cytotoxic effect of 29 diverse chemotherapeutic agents on lymphoblastoid cell lines (LCLs) derived from family- and population-based cohorts was investigated. We used a high throughput format to determine cytotoxicity of the drugs on LCLs and developed a new evolutionary computation approach to fit response curves for each individual. Variance components analysis determined the h² for each drug response and a wide range of values was observed across drugs. Genome-wide analysis was performed using new analytical approaches. These results lay the groundwork for future studies to uncover genes influencing chemotherapeutic response and demonstrate a new computational framework for performing such analysis.

Convergent mechanisms of somatic mutations in polycythemia vera

Polycythemia vera (PV) is an acquired blood disorder, with variable increase of clonal myeloid cells (erythrocytes, granulocytes and platelets) and mostly normal polyclonal T lymphocytes. Most patients have a somatic V617F gain-of-function mutation in JAK2 associated with acquired uniparental disomy (UPD) on chromosome 9p. Yet, the JAK2 V617F mutation is not a PV-initiating event and the family clustering of PV suggests a contribution of inherited genetic events. Using whole-genome SNP arrays, we assayed 34 T-cells and 66 granulocytes (including 32 pairs from the same patients), and identified multiple SNPs around JAK2 that are associated with PV susceptibility (rs11999802, P=1.8E-8, OR=4.4). We also developed a quantitative measure of the fraction of somatic single nucleotide variants (SNVs) based on allele-specific PCR, and a quantitative measure of somatic UPD based on "fractional copy-neutral loss-of-heterozygosity (LOH)" on SNP arrays. Somatic genomic changes in granulocytes revealed strong genetic heterogeneity, including 9p UPD and chromosomal gain. The magnitude of somatic 9p UPD was strongly associated with V617F dosage (r2=0.74, P=4.8E-12), suggesting that UPD preferentially increases the V617F subclone. In granulocytes with heterozygous rs11999802 genotypes, UPD increased the relative fraction of germline risk alleles (P=0.03). Thus, germline risk variants at JAK2 predispose to somatic point mutations within JAK2, whose allelic dosage can be further increased by a serial subclonal expansion of allele-specific UPD or copy number alteration, contributing to PV pathogenesis. We argue that PV represents a unique disease model to study the interplay between germline risk variants and convergent mechanisms of somatic mutations.

Pathway-based analysis of genetic susceptibility to cervical cancer in situ: HLA-DPB1 affects risk in Swedish women

We have conducted a pathway-based analysis of genome-wide single-nucleotide polymorphism (SNP) data in order to identify genetic susceptibility factors for cervical cancer in situ. Genotypes derived from Affymetrix 500k or 5.0 arrays for 1076 cases and 1426 controls were analyzed for association, and pathways with enriched signals were identified using the SNP ratio test. The most strongly associated KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were Asthma (empirical P=0.03), Folate biosynthesis (empirical P=0.04) and Graft-versus-host disease (empirical P=0.05). Among the 11 top-ranking pathways were 6 related to the immune response with the common denominator being genes in the major histocompatibility complex (MHC) region on chromosome 6. Further investigation of the MHC revealed a clear effect of HLA-DPB1 polymorphism on disease susceptibility. At a functional level, DPB1 alleles associated with risk and protection differ in key amino-acid residues affecting peptide-binding motifs in the extracellular domains. The results illustrate the value of pathway-based analysis to mine genome-wide data, and point to the importance of the MHC region and specifically the HLA-DPB1 locus for susceptibility to cervical cancer.

Enriching targeted sequencing experiments for rare disease alleles

MOTIVATION

Next-generation targeted resequencing of genome-wide association study (GWAS)-associated genomic regions is a common approach for follow-up of indirect association of common alleles. However, it is prohibitively expensive to sequence all the samples from a well-powered GWAS study with sufficient depth of coverage to accurately call rare genotypes. As a result, many studies may use next-generation sequencing for single nucleotide polymorphism (SNP) discovery in a smaller number of samples, with the intent to genotype candidate SNPs with rare alleles captured by resequencing. This approach is reasonable, but may be inefficient for rare alleles if samples are not carefully selected for the resequencing experiment.

RESULTS

We have developed a probability-based approach, SampleSeq, to select samples for a targeted resequencing experiment that increases the yield of rare disease alleles substantially over random sampling of cases or controls or sampling based on genotypes at associated SNPs from GWAS data. This technique allows for smaller sample sizes for resequencing experiments, or allows the capture of rarer risk alleles. When following up multiple regions, SampleSeq selects subjects with an even representation of all the regions. SampleSeq also can be used to calculate the sample size needed for the resequencing to increase the chance of successful capture of rare alleles of desired frequencies.

SOFTWARE

http://biostat.mc.vanderbilt.edu/SampleSeq

Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium

OBJECTIVE

White matter hyperintensities (WMHs) detectable by magnetic resonance imaging are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMHs are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified.

METHODS

We performed a meta-analysis of genome-wide association studies (GWASs) for WMH burden in 9,361 stroke-free individuals of European descent from 7 community-based cohorts. Significant findings were tested for replication in 3,024 individuals from 2 additional cohorts.

RESULTS

We identified 6 novel risk-associated single nucleotide polymorphisms (SNPs) in 1 locus on chromosome 17q25 encompassing 6 known genes including WBP2, TRIM65, TRIM47, MRPL38, FBF1, and ACOX1. The most significant association was for rs3744028 (p(discovery) = 4.0 × 10(-9) ; p(replication) = 1.3 × 10(-7) ; p(combined) = 4.0 × 10(-15) ). Other SNPs in this region also reaching genome-wide significance were rs9894383 (p = 5.3 × 10(-9) ), rs11869977 (p = 5.7 × 10(-9) ), rs936393 (p = 6.8 × 10(-9) ), rs3744017 (p = 7.3 × 10(-9) ), and rs1055129 (p = 4.1 × 10(-8) ). Variant alleles at these loci conferred a small increase in WMH burden (4-8% of the overall mean WMH burden in the sample).

INTERPRETATION

This large GWAS of WMH burden in community-based cohorts of individuals of European descent identifies a novel locus on chromosome 17. Further characterization of this locus may provide novel insights into the pathogenesis of cerebral WMH.

Impact of phenotype definition on genome-wide association signals: empirical evaluation in human immunodeficiency virus type 1 infection

Discussion on improving the power of genome-wide association studies to identify candidate variants and genes is generally centered on issues of maximizing sample size; less attention is given to the role of phenotype definition and ascertainment. The authors used genome-wide data from patients infected with human immunodeficiency virus type 1 (HIV-1) to assess whether differences in type of population (622 seroconverters vs. 636 seroprevalent subjects) or the number of measurements available for defining the phenotype resulted in differences in the effect sizes of associations between single nucleotide polymorphisms and the phenotype, HIV-1 viral load at set point. The effect estimate for the top 100 single nucleotide polymorphisms was 0.092 (95% confidence interval: 0.074, 0.110) log(10) viral load (log(10) copies of HIV-1 per mL of blood) greater in seroconverters than in seroprevalent subjects. The difference was even larger when the authors focused on chromosome 6 variants (0.153 log(10) viral load) or on variants that achieved genome-wide significance (0.232 log(10) viral load). The estimates of the genetic effects tended to be slightly larger when more viral load measurements were available, particularly among seroconverters and for variants that achieved genome-wide significance. Differences in phenotype definition and ascertainment may affect the estimated magnitude of genetic effects and should be considered in optimizing power for discovering new associations.

Construction of Japanese BAC library Yamato-2 (JY2): a set of 330K clone resources of damage-minimized DNA taken from a genetically established Japanese individual

A bacterial artificial chromosome (BAC) library referred to as Yamato-2 (JY2), was constructed from a Japanese individual and contained 330,000 clones. Library construction was based on 2 concepts: Japanese pedigree and non-immortalization. Genomic DNA was extracted from white blood cells from umbilical cord blood of a Japanese male individual. Four traits of the sample, (1) amelogenin DNA, (2) short tandem repeat (STR), (3) mitochondrial DNA (mtDNA), and (4) HLA-allele typing, were investigated to verify attribution of the donor. One of the samples with quite good Japanese characteristics was named JY2 and used as a resource for construction of a BAC library. Amelogenin DNA indicated male. STR indicated Mongoloid. MtDNA suggested haplogroup B, which is different from any other diploid whose sequence has been reported. The HLA gene was classified into east-Asian specific haplotype. These results revealed that JY2 was obtained from a Japanese male. We sequenced both ends of 185,012 BAC clones. By using the BLAST search, BAC end sequences (BESs) were mapped on the human reference sequence provided by NCBI. Inserts of individual BAC clones were mapped with both ends properly placed. As a result, 103,647 BAC clones were successfully mapped. The average insert size of BAC calculated from the mapping information was 130 kb. Coverage and redundancy of the reference sequence by successfully mapped BAC clones were 96.4% and 3.9-fold, respectively. This library will be especially suitable as a Japanese standard genome resource. The availability of an accurate library is indispensable for diagnostics or drug-design based on genome information, and JY2 will provide an accurate sequence of the Japanese genome as an important addition to the human genome.

Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study

Genome-wide association studies (GWAS) have become increasingly popular recently and contributed to the discovery of many susceptibility variants. However, a large proportion of the heritability still remained unexplained. This observation raises queries regarding the ability of GWAS to uncover the genetic basis of complex diseases. In this study, we propose a simple and fast statistical framework to estimate the total heritability explained by all true susceptibility variants in a GWAS. It is expected that many true risk variants will not be detected in a GWAS due to limited power. The proposed framework aims at recovering the "hidden" heritability. Importantly, only the summary z-statistics are required as input and no raw genotype data are needed. The strategy is to recover the true effect sizes from the observed z-statistics. The methodology does not rely on any distributional assumptions of the effect sizes of variants. Both binary and quantitative traits can be handled and covariates may be included. Population-based or family-based designs are allowed as long as the summary statistics are available. Simulations were conducted and showed satisfactory performance of the proposed approach. Application to real data (Crohn's disease, HDL, LDL, and triglycerides) reveals that at least around 10-20% of variance in liability or phenotype can be explained by GWAS panels. This translates to around 10-40% of the total heritability for the studied traits.

Genetic analysis in cardiovascular disease: a clinical perspective

Many forms of cardiovascular disease (CVD) demonstrate heritability and thus a genetic contribution is likely. This is most evident when considering the "simple" Mendelian traits such as hypertrophic cardiomyopathy. However, family history also influences our assessment of patients with complex traits such as coronary artery disease, hypertension, and common forms of hypercholesterolemia, as observed in clinical practice. Recent research has led to advances in our understanding of the genetic basis of both the simple and complex forms of CVD. This review presents the current state of knowledge regarding major gene disorders, as well as more common, complex forms of CVD such as coronary artery disease. It discusses the fundamental approaches being used to identify the genetic basis of the various disease states, as well as the practical implications of the discoveries to clinicians. It also focuses on our need to assess the extent by which genetic analysis can alter our calculation of an individual's risk of disease, and our ability to successfully target treatment that will modify this process.

Identification of Cd101 as a susceptibility gene for Novosphingobium aromaticivorans-induced liver autoimmunity

Environmental and genetic factors define the susceptibility of an individual to autoimmune disease. Although common genetic pathways affect general immunological tolerance mechanisms in autoimmunity, the effects of such genes could vary under distinct immune challenges within different tissues. In this study, we demonstrate this by observing that autoimmune type 1 diabetes-protective haplotypes at the insulin-dependent diabetes susceptibility region 10 (Idd10) introgressed from chromosome 3 of C57BL/6 (B6) and A/J mice onto the NOD background increase the severity of autoimmune primary biliary cirrhosis induced by infection with Novosphingobium aromaticivorans, a ubiquitous alphaproteobacterium, when compared with mice having the NOD and NOD.CAST Idd10 type 1 diabetes-susceptible haplotypes. Substantially increased liver pathology in mice having the B6 and A/J Idd10 haplotypes correlates with reduced expression of CD101 on dendritic cells, macrophages, and granulocytes following infection, delayed clearance of N. aromaticivorans, and the promotion of overzealous IFN-γ- and IL-17-dominated T cell responses essential for the adoptive transfer of liver lesions. CD101-knockout mice generated on the B6 background also exhibit substantially more severe N. aromaticivorans-induced liver disease correlating with increased IFN-γ and IL-17 responses compared with wild-type mice. These data strongly support the hypothesis that allelic variation of the Cd101 gene, located in the Idd10 region, alters the severity of liver autoimmunity induced by N. aromaticivorans.

Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening

Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individual's genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.

Genetic analysis of biological pathway data through genomic randomization

Genome Wide Association Studies (GWAS) are a standard approach for large-scale common variation characterization and for identification of single loci predisposing to disease. However, due to issues of moderate sample sizes and particularly multiple testing correction, many variants of smaller effect size are not detected within a single allele analysis framework. Thus, small main effects and potential epistatic effects are not consistently observed in GWAS using standard analytical approaches that consider only single SNP alleles. Here, we propose unique methodology that aggregates variants of interest (for example, genes in a biological pathway) using GWAS results. Multiple testing and type I error concerns are minimized using empirical genomic randomization to estimate significance. Randomization corrects for common pathway-based analysis biases, such as SNP coverage and density, linkage disequilibrium, gene size and pathway size. Pathway Analysis by Randomization Incorporating Structure (PARIS) applies this randomization and in doing so directly accounts for linkage disequilibrium effects. PARIS is independent of association analysis method and is thus applicable to GWAS datasets of all study designs. Using the KEGG database as an example, we apply PARIS to the publicly available Autism Genetic Resource Exchange GWAS dataset, revealing pathways with a significant enrichment of positive association results.

Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array

The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94% and over 98% of successful samples having SNP call rates exceeding 98%. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies.

Genetic determinants of susceptibility and severity in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) in most patients involves only simple hepatic steatosis; however, a minority develop progressive steatohepatitis. Family studies and inter-ethnic differences in susceptibility suggest that genetic factors may be important risk determinants for progressive disease. Polymorphisms in genes affecting lipid metabolism, cytokines, fibrotic mediators and oxidative stress may be associated with steatohepatitis and/or fibrosis, but most of these findings require replication. A recent finding that a nonsynonymous polymorphism in the PNPLA3 gene predicts the extent of steatosis in NAFLD has been replicated in at least eight studies, with several studies also demonstrating an association with fibrosis. A new genome-wide association study has identified several additional novel associations with NAFLD severity. Other disease genes may be identified by similar approaches in the future.

Contemporary human genetic strategies in aging research

Human aging is a complex, multifactorial process influenced by a number of genetic and non-genetic factors. This article first reviews genetic strategies for human aging research and considers the advantages and disadvantages of each. We then discuss the issue of phenotypic definition for genetic studies of aging, including longevity/life span, as well as disease-free survival and other endophenotypes. Finally, we argue that extensions of this area of research, including incorporation of gene×environment interactions, multivariate phenotypes, integration of functional genomic annotations, and exploitation of orthology - many of which are already initiated and ongoing - are critical to advancing this field.

Genetic factors in contact allergy--review and future goals

The genetics of contact allergy are still only partly understood, despite decades of research; this might be a consequence of inadequately defined phenotypes used in the past. A recommendation is to study an extreme phenotype, namely, polysensitization (sensitization to three or more unrelated allergens). Another approach to unravel the genetics of contact allergy is the study of candidate genes. In this review, we summarize studies on the associations between genetic variation (e.g. single-nucleotide polymorphisms) in certain candidate genes and contact allergy. Polymorphisms and mutations affecting the following proteins were studied: (i) filaggrin; (ii) N-acetyltransferase (NAT) 1 and 2; (iii) glutathione-S-transferase (GST) M and T; (iv) manganese superoxide dismutase; (v) angiotensin-converting enzyme (ACE); (vi) tumour necrosis factor (TNF); and (vii) interleukin-16 (IL-16). The polymorphisms of NAT1, NAT2, GSTM, GSTT, ACE, TNF and IL-16 were shown to be associated with an increased risk of contact allergy. In one of our studies, the increased risk conferred by the TNF and IL-16 polymorphisms was confined to polysensitized individuals. Other relevant candidate genes may be identified by studying diseases related to contact allergy in terms of clinical symptoms, a more general pathology (inflammation), and possibly an overlapping genetic background, such as irritant contact dermatitis.

Variable set enrichment analysis in genome-wide association studies

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.

Quantifying the underestimation of relative risks from genome-wide association studies

Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2–1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called “missing” heritability.

Genome-wide association studies (GWAS) exploit the correlation in genetic diversity along chromosomes in order to detect effects on disease risk without having to type causal loci directly. The inevitable downside of this approach is that, when the correlation between the marker and the causal variant is imperfect, the risk associated with carrying the predisposing allele is diluted and its effect is underestimated. Using simulations, where we know the true risk at the causal locus, we quantify the extent of this underestimation. We show that, for loci which have a modest effect on disease risk and are common in the population, the risk estimated from the most associated SNP is very close to the truth approximately two thirds of the time. Although the extent of the underestimation depends on assumptions about the frequency and strength of the risk allele, we predict that fine mapping of GWAS loci will, in rare cases, identify causal variants with considerably higher risk. Using three common diseases as examples, we investigate the expected cumulative effects of underestimation at multiple loci on our ability to stratify individuals by disease risk and to explain disease heritability.

GATES: a rapid and powerful gene-based association test using extended Simes procedure

The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.

SNPTransformer: a lightweight toolkit for genome-wide association studies

High-throughput genotyping chips have produced huge datasets for genome-wide association studies (GWAS) that have contributed greatly to discovering susceptibility genes for complex diseases. There are two strategies for performing data analysis for GWAS. One strategy is to use open-source or commercial packages that are designed for GWAS. The other is to take advantage of classic genetic programs with specific functions, such as linkage disequilibrium mapping, haplotype inference and transmission disequilibrium tests. However, most classic programs that are available are not suitable for analyzing chip data directly and require custom-made input, which results in the inconvenience of converting raw genotyping files into various data formats. We developed a powerful, user-friendly, lightweight program named SNPTransformer for GWAS that includes five major modules (Transformer, Operator, Previewer, Coder and Simulator). The toolkit not only works for transforming the genotyping files into ten input formats for use with classic genetics packages, but also carries out useful functions such as relational operations on IDs, previewing data files, recoding data formats and simulating marker files, among other functions. It bridges upstream raw genotyping data with downstream genetic programs, and can act as an in-hand toolkit for human geneticists, especially for non-programmers. SNPTransformer is freely available at http://snptransformer.sourceforge.net.

Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of "missing heritability" has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of "missing heritability" for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp.

Genomics for disease treatment and prevention

The enormous advances in genetics and genomics of the past decade have the potential to revolutionize health care, including mental health care, and bring about a system predominantly characterized by the practice of genomic and personalized medicine. This article briefly reviews the history of genetics and genomics and assesses the extent to which the results of genetic and genomic studies are currently being leveraged clinically for disease treatment and prevention. Relevant social, economic, and policy issues relevant to genomic medicine are also reviewed, and priority research areas in which further work is needed are identified.

Using ancestry matching to combine family-based and unrelated samples for genome-wide association studies

We propose a method to analyze family-based samples together with unrelated cases and controls. The method builds on the idea of matched case-control analysis using conditional logistic regression (CLR). For each trio within the family, a case (the proband) and matched pseudo-controls are constructed, based upon the transmitted and untransmitted alleles. Unrelated controls, matched by genetic ancestry, supplement the sample of pseudo-controls; likewise unrelated cases are also paired with genetically matched controls. Within each matched stratum, the case genotype is contrasted with control/pseudo-control genotypes via CLR, using a method we call matched-CLR (mCLR). Eigenanalysis of numerous SNP genotypes provides a tool for mapping genetic ancestry. The result of such an analysis can be thought of as a multidimensional map, or eigenmap, in which the relative genetic similarities and differences amongst individuals is encoded in the map. Once constructed, new individuals can be projected onto the ancestry map based on their genotypes. Successful differentiation of individuals of distinct ancestry depends on having a diverse, yet representative sample from which to construct the ancestry map. Once samples are well-matched, mCLR yields comparable power to competing methods while ensuring excellent control over Type I error.

Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis

Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.

Variations of six transmembrane epithelial antigen of prostate 4 (STEAP4) gene are associated with metabolic syndrome in a female Uygur general population

BACKGROUND AND AIMS

Metabolic syndrome (MetS) is linked with visceral obesity and is associated with a clustering of abnormalities (including impaired glucose tolerance, insulin resistance, dyslipidemia and hypertension). Six transmembrane epithelial antigen of prostate 4 (STEAP4) was associated with human obesity. STEAP4 gene represents a strong biological and positional candidate for a susceptibility factor for MetS. Uygur Chinese is a relatively isolated population with a relatively homogeneous environment and a high prevalence of MetS. We undertook this study to investigate the relationship between STEAP4 gene variations and MetS in a Uygur general population.

METHODS

The functional regions of STEAP4 gene were sequenced in Uygur patients with MetS. Four representative variations, rs1981529, rs34741656, rs8122 and 6031T/G (unsuccessfully genotyped), selected with a r² cutoff of 0.8 and minor allele frequency of >5%, were genotyped in 858 MetS and 687 non-MetS controls.

RESULTS

Fourteen novel and six known single nucleotide polymorphisms (SNPs) including 2 nonsynonymous SNPs in the STEAP4 gene were identified. SNPs rs8122 and rs1981529 were significantly associated with MetS phenotype in females [additive p = 0.032 and p = 0.011; ORs (95% CI) adjusted for age 0.772 (0.625-0.954) and 0.740 (0.582-0.941), respectively]. Two common haplotypes 1 (rs8122/rs1981529/rs34741656, G-A-G) and 2 (A-G-G) had significantly higher (permutation p = 0.044) and lower (permutation p = 0.009) frequency in MetS than that in controls in females. Multiple linear regression analysis revealed a significant association of the SNPs rs8122 and rs1981529 with HDL-c level in MetS cases (p = 0.001 and 0.024) and in a combined sample (p = 0.004 and 0.009).

CONCLUSIONS

STEAP4 genetic variations are likely to be associated with metabolic syndrome in a female Uygur general population.

Individualized medicine 2010

Molecular and cell biology have revolutionized not only diagnosis, therapy and prevention of human diseases but also greatly contributed to the understanding of their pathogenesis. Based on modern molecular and biochemical methods it is possible to identify on the one hand point mutations and single nucleotide polymorphisms. On the other hand, using high throughput array technologies, it is possible to analyse thousands of genes or gene products simultaneously, resulting in an individual gene or gene expression profile (signature). These data increasingly allow to define the individual risk for a given disease and to predict the individual prognosis of a disease as well as the efficacy of therapeutic strategies (individualized medicine). In the following sections some of the recent advances of predictive medicine and their clinical relevance will be addressed.

Copy number variants on the X chromosome in women with primary ovarian insufficiency

OBJECTIVE

To investigate whether submicroscopic copy number variants (CNVs) on the X chromosome can be identified in women with primary ovarian insufficiency (POI), defined as spontaneous secondary amenorrhea before 40 years of age accompanied by follicle-stimulating hormone levels above 40 IU/L on at least two occasions.

DESIGN

Analysis of intensity data of single nucleotide polymorphism (SNP) probes generated by genomewide Illumina 370k CNV BeadChips, followed by the validation of identified loci using a custom designed ultra-high-density comparative genomic hybridization array containing 48,325 probes evenly distributed over the X chromosome.

SETTING

Multicenter genetic cohort study in the Netherlands.

PATIENT(S)

108 Dutch Caucasian women with POI, 97 of whom passed quality control, who had a normal karyogram and absent fragile X premutation, and 235 healthy Dutch Caucasian women as controls.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Amount and locus of X chromosomal microdeletions or duplications.

RESULT(S)

Intensity differences between SNP probes identify microdeletions and duplications. The initial analysis identified an overrepresentation of deletions in POI patients. Moreover, CNVs in two genes on the Xq21.3 locus (i.e., PCDH11X and TGIF2LX) were statistically significantly associated with the POI phenotype. Mean size of identified CNVs was 262 kb. However, in the validation study the identified putative Xq21.3 deletions samples did not show deviations in intensities in consecutive probes.

CONCLUSION(S)

X chromosomal submicroscopic CNVs do not play a major role in Caucasian POI patients. We provide guidelines on how submicroscopic cytogenetic POI research should be conducted.

Novel therapies in childhood heart failure: today and tomorrow

Heart failure is an important cause of morbidity and mortality in individuals of all ages. The many-faceted nature of the clinical heart failure syndrome has historically frustrated attempts to develop an overarching explanative theory. However, much useful information has been gained by basic and clinical investigation, even though a comprehensive understanding of heart failure has been elusive. Heart failure is a growing problem, in both adult and pediatric populations, for which standard medical therapy, as of 2010, can have positive effects, but these are usually limited and progressively diminish with time in most patients. If we want curative or near-curative therapy that will return patients to a normal state of health at a feasible cost, much better diagnostic and therapeutic technologies need to be developed. This review addresses the vexing group of heart failure etiologies that include cardiomyopathies and other ventricular dysfunctions of various types, for which current therapy is only modestly effective. Although there are many unique aspects to heart failure in patients with pediatric and congenital heart disease, many of the innovative approaches that are being developed for the care of adults with heart failure will be applicable to heart failure in childhood.

Overview of personalized medicine in the disease genomic era

Sir William Osler (1849-1919) recognized that "variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions we know as disease". Accordingly, the traditional methods of medicine are not always best for all patients. Over the last decade, the study of genomes and their derivatives (RNA, protein and metabolite) has rapidly advanced to the point that genomic research now serves as the basis for many medical decisions and public health initiatives. Genomic tools such as sequence variation, transcription and, more recently, personal genome sequencing enable the precise prediction and treatment of disease. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. In order to make personalized medicine effective, these genomic techniques must be standardized and integrated into health systems and clinical workflow. In addition, full application of personalized or genomic medicine requires dramatic changes in regulatory and reimbursement policies as well as legislative protection related to privacy. This review aims to provide a general overview of these topics in the field of personalized medicine.

Novel loci for major depression identified by genome-wide association study of Sequenced Treatment Alternatives to Relieve Depression and meta-analysis of three studies

We report a genome-wide association study (GWAS) of major depressive disorder (MDD) in 1221 cases from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study and 1636 screened controls. No genome-wide evidence for association was detected. We also carried out a meta-analysis of three European-ancestry MDD GWAS data sets: STAR*D, Genetics of Recurrent Early-onset Depression and the publicly available Genetic Association Information Network-MDD data set. These data sets, totaling 3957 cases and 3428 controls, were genotyped using four different platforms (Affymetrix 6.0, 5.0 and 500 K, and Perlegen). For each of 2.4 million HapMap II single-nucleotide polymorphisms (SNPs), using genotyped data where available and imputed data otherwise, single-SNP association tests were carried out in each sample with correction for ancestry-informative principal components. The strongest evidence for association in the meta-analysis was observed for intronic SNPs in ATP6V1B2 (P=6.78 x 10⁻⁷), SP4 (P=7.68 x 10⁻⁷) and GRM7 (P=1.11 x 10⁻⁶). Additional exploratory analyses were carried out for a narrower phenotype (recurrent MDD with onset before age 31, N=2191 cases), and separately for males and females. Several of the best findings were supported primarily by evidence from narrow cases or from either males or females. On the basis of previous biological evidence, we consider GRM7 a strong MDD candidate gene. Larger samples will be required to determine whether any common SNPs are significantly associated with MDD.

Genome-wide association study of recurrent early-onset major depressive disorder

A genome-wide association study was carried out in 1020 case subjects with recurrent early-onset major depressive disorder (MDD) (onset before age 31) and 1636 control subjects screened to exclude lifetime MDD. Subjects were genotyped with the Affymetrix 6.0 platform. After extensive quality control procedures, 671 424 autosomal single nucleotide polymorphisms (SNPs) and 25 068 X chromosome SNPs with minor allele frequency greater than 1% were available for analysis. An additional 1 892 186 HapMap II SNPs were analyzed based on imputed genotypic data. Single-SNP logistic regression trend tests were computed, with correction for ancestry-informative principal component scores. No genome-wide significant evidence for association was observed, assuming that nominal P<5 × 10(-8) approximates a 5% genome-wide significance threshold. The strongest evidence for association was observed on chromosome 18q22.1 (rs17077540, P=1.83 × 10(-7)) in a region that has produced some evidence for linkage to bipolar-I or -II disorder in several studies, within an mRNA detected in human brain tissue (BC053410) and approximately 75 kb upstream of DSEL. Comparing these results with those of a meta-analysis of three MDD GWAS data sets reported in a companion article, we note that among the strongest signals observed in the GenRED sample, the meta-analysis provided the greatest support (although not at a genome-wide significant level) for association of MDD to SNPs within SP4, a brain-specific transcription factor. Larger samples will be required to confirm the hypothesis of association between MDD (and particularly the recurrent early-onset subtype) and common SNPs.

Long intergenic noncoding RNAs: new links in cancer progression

The process of cancer metastasis involves a series of sequential and complex steps. Here we give a perspective on recent results regarding noncoding transcription in cancer progression, focusing on the emerging role of long intergenic noncoding RNAs (lincRNAs). LincRNAs target chromatin modification complexes or RNA-binding proteins to alter gene expression programs. Similarly to miRNAs, lincRNAs exhibit distinct gene expression patterns in primary tumors and metastases. We discuss how lincRNAs can be used for cancer diagnosis and prognosis and serve as potential therapeutic targets.