Recent developments in genomewide association scans: a workshop summary and review

Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype

A key challenge in radiotherapy is to maximize radiation doses to cancer cells while minimizing damage to surrounding healthy tissue. As severe toxicity in a minority of patients limits the doses that can be safely given to the majority, there is interest in developing a test to measure an individual's radiosensitivity before treatment. Variation in sensitivity to radiation is an inherited genetic trait and recent progress in genotyping raises the possibility of genome-wide studies to characterize genetic profiles that predict patient response to radiotherapy.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations

INTRODUCTION

Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences.

METHODS

The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10-30%) and variations in allelic frequency for a Quantitative Trait (QT) (10-20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response.

CONCLUSION

The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

Phylogenetic dependency networks: inferring patterns of CTL escape and codon covariation in HIV-1 Gag

HIV avoids elimination by cytotoxic T-lymphocytes (CTLs) through the evolution of escape mutations. Although there is mounting evidence that these escape pathways are broadly consistent among individuals with similar human leukocyte antigen (HLA) class I alleles, previous population-based studies have been limited by the inability to simultaneously account for HIV codon covariation, linkage disequilibrium among HLA alleles, and the confounding effects of HIV phylogeny when attempting to identify HLA-associated viral evolution. We have developed a statistical model of evolution, called a phylogenetic dependency network, that accounts for these three sources of confounding and identifies the primary sources of selection pressure acting on each HIV codon. Using synthetic data, we demonstrate the utility of this approach for identifying sites of HLA-mediated selection pressure and codon evolution as well as the deleterious effects of failing to account for all three sources of confounding. We then apply our approach to a large, clinically-derived dataset of Gag p17 and p24 sequences from a multicenter cohort of 1144 HIV-infected individuals from British Columbia, Canada (predominantly HIV-1 clade B) and Durban, South Africa (predominantly HIV-1 clade C). The resulting phylogenetic dependency network is dense, containing 149 associations between HLA alleles and HIV codons and 1386 associations among HIV codons. These associations include the complete reconstruction of several recently defined escape and compensatory mutation pathways and agree with emerging data on patterns of epitope targeting. The phylogenetic dependency network adds to the growing body of literature suggesting that sites of escape, order of escape, and compensatory mutations are largely consistent even across different clades, although we also identify several differences between clades. As recent case studies have demonstrated, understanding both the complexity and the consistency of immune escape has important implications for CTL-based vaccine design. Phylogenetic dependency networks represent a major step toward systematically expanding our understanding of CTL escape to diverse populations and whole viral genes.

Calibration of credibility of agnostic genome-wide associations

Genome-wide testing platforms are increasingly used to promote "agnostic" approaches to the discovery of gene variants associated with the risk of many common diseases and quantitative traits. The early track record of genome-wide association (GWA) studies suggests that some proposed associations are replicated quite consistently with large-scale subsequent evidence from multiple studies, others have a more inconsistent replication record, some have failed to be replicated by independent investigators and many more early proposed associations await further replication. An important question is how to calibrate the credibility of these postulated associations. A simple Bayesian method is applied here to achieve such calibration. The variability of the estimated credibility is examined under different assumptions. Empirical examples are drawn from existing GWA studies. It is demonstrated that the credibility of different proposed associations can cover a very wide range. The credibility of specific associations usually remains relatively robust when different plausible assumptions are made (within a reasonable range) for the prior odds of an association being true, or the magnitude of the anticipated effect size for genetic associations. Heterogeneity and bias assumptions can have a more major impact on the credibility estimates and thus they need very careful consideration in each case. Credibility calibration may be used in conjunction with qualitative criteria for the appraisal of the cumulative evidence that take into consideration the amount, consistency, and protection from bias in the data.

Quantitative trait locus association scan of early reading disability and ability using pooled DNA and 100K SNP microarrays in a sample of 5760 children

Quantitative genetic research suggests that reading disability is the quantitative extreme of the same genetic and environmental factors responsible for normal variation in reading ability. This finding warrants a quantitative trait locus (QTL) strategy that compares low versus high extremes of the normal distribution of reading in the search for QTLs associated with variation throughout the distribution. A low reading ability group (N=755) and a high reading group (N=747) were selected from a representative UK sample of 7-year-olds assessed on two measures of reading that we have shown to be highly heritable and highly genetically correlated. The low and high reading ability groups were each divided into 10 independent DNA pools and the 20 pools were assayed on 100 K single nucleotide polymorphism (SNP) microarrays to screen for the largest allele frequency differences between the low and high reading ability groups. Seventy five of these nominated SNPs were individually genotyped in an independent sample of low (N=452) and high (N=452) reading ability children selected from a second sample of 4258 7-year-olds. Nine of the seventy-five SNPs were nominally significant (P<0.05) in the predicted direction. These 9 SNPs and 14 other SNPs showing low versus high allele frequency differences in the predicted direction were genotyped in the rest of the second sample to test the QTL hypothesis. Ten SNPs yielded nominally significant linear associations in the expected direction across the distribution of reading ability. However, none of these SNP associations accounted for more than 0.5% of the variance of reading ability, despite 99% power to detect them. We conclude that QTL effect sizes, even for highly heritable common disorders and quantitative traits such as early reading disability and ability, might be much smaller than previously considered.

The nature of nurture: a genomewide association scan for family chaos

Widely used measures of the environment, especially the family environment of children, show genetic influence in dozens of twin and adoption studies. This phenomenon is known as gene-environment correlation in which genetically driven influences of individuals affect their environments. We conducted the first genome-wide association (GWA) analysis of an environmental measure. We used a measure called CHAOS which assesses 'environmental confusion' in the home, a measure that is more strongly associated with cognitive development in childhood than any other environmental measure. CHAOS was assessed by parental report when the children were 3 years and again when the children were 4 years; a composite CHAOS measure was constructed across the 2 years. We screened 490,041 autosomal single-nucleotide polymorphisms (SNPs) in a two-stage design in which children in low chaos families (N = 469) versus high chaos families (N = 369) from 3,000 families of 4-year-old twins were screened in Stage 1 using pooled DNA. In Stage 2, following SNP quality control procedures, 41 nominated SNPs were tested for association with family chaos by individual genotyping an independent representative sample of 3,529. Despite having 99% power to detect associations that account for more than 0.5% of the variance, none of the 41 nominated SNPs met conservative criteria for replication. Similar to GWA analyses of other complex traits, it is likely that most of the heritable variation in environmental measures such as family chaos is due to many genes of very small effect size.

Goldsurfer2 (Gs2): a comprehensive tool for the analysis and visualization of genome wide association studies

Background

Genome wide association (GWA) studies are now being widely undertaken aiming to find the link between genetic variations and common diseases. Ideally, a well-powered GWA study will involve the measurement of hundreds of thousands of single nucleotide polymorphisms (SNPs) in thousands of individuals. The sheer volume of data generated by these experiments creates very high analytical demands. There are a number of important steps during the analysis of such data, many of which may present severe bottlenecks. The data need to be imported and reviewed to perform initial quality control (QC) before proceeding to association testing. Evaluation of results may involve further statistical analysis, such as permutation testing, or further QC of associated markers, for example, reviewing raw genotyping intensities. Finally significant associations need to be prioritised using functional and biological interpretation methods, browsing available biological annotation, pathway information and patterns of linkage disequilibrium (LD).

Results

We have developed an interactive and user-friendly graphical application to be used in all steps in GWA projects from initial data QC and analysis to biological evaluation and validation of results. The program is implemented in Java and can be used on all platforms.

Conclusion

Very large data sets (e.g. 500 k markers and 5000 samples) can be quality assessed, rapidly analysed and integrated with genomic sequence information. Candidate SNPs can be selected and functionally evaluated.

Molecular genetic studies of gene identification for osteoporosis

This review comprehensively summarizes the most important and representative molecular genetics studies of gene identification for osteoporosis published up to the end of September 2007. It is intended to constitute a sequential update of our previously published reviews covering the available data up to the end of 2004. Evidence from candidate gene-association studies, genome-wide linkage and association studies, as well as functional genomic studies (including gene-expression microarray and proteomics) on osteogenesis and osteoporosis, are reviewed separately. Studies of transgenic and knockout mice models relevant to osteoporosis are summarized. The major results of all studies are tabulated for comparison and ease of reference. Comments are made on the most notable findings and representative studies for their potential influence and implications on our present understanding of genetics of osteoporosis. The format adopted by this review should be ideal for accommodating future new advances and studies.

Pharmacogenetics of the response to beta 2 agonist drugs: a systematic overview of the field

The response to beta2-agonist treatment shows large repeatability within individuals and may thus be determined by genetic influences. Here we present a systematic overview of the available genetic association and linkage data for beta2-agonist treatment response. Systematic searches identified 66 eligible articles, as of March 2007, pertaining either to B2AR gene polymorphisms and short-acting or long-acting beta2-agonists or to another 29 different genes. We systematize these study results according to gene, agent and type of outcomes addressed. The systematic review highlights major challenges in the field, including extreme multiplicity of analyses; lack of consensus for main phenotypes of interest; typically small sample sizes; and poor replicability of the proposed genetic variants. Future studies will benefit from standardization of analyses and outcomes, hypothesis-free genome-wide association testing platforms, potentially additional fine mapping around new discovered variants, and large-scale collaborative studies with prospective plans for replication among several teams, with transparent public recording of all data.

Multiple newly identified loci associated with prostate cancer susceptibility

Prostate cancer is the most common cancer affecting males in developed countries. It shows consistent evidence of familial aggregation, but the causes of this aggregation are mostly unknown. To identify common alleles associated with prostate cancer risk, we conducted a genome-wide association study (GWAS) using blood DNA samples from 1,854 individuals with clinically detected prostate cancer diagnosed at </=60 years or with a family history of disease, and 1,894 population-screened controls with a low prostate-specific antigen (PSA) concentration (<0.5 ng/ml). We analyzed these samples for 541,129 SNPs using the Illumina Infinium platform. Initial putative associations were confirmed using a further 3,268 cases and 3,366 controls. We identified seven loci associated with prostate cancer on chromosomes 3, 6, 7, 10, 11, 19 and X (P = 2.7 x 10(-8) to P = 8.7 x 10(-29)). We confirmed previous reports of common loci associated with prostate cancer at 8q24 and 17q. Moreover, we found that three of the newly identified loci contain candidate susceptibility genes: MSMB, LMTK2 and KLK3.

Linear trend tests for case-control genetic association that incorporate random phenotype and genotype misclassification error

The purpose of this work is the development of linear trend tests that allow for error (LTT ae), specifically incorporating double-sampling information on phenotypes and/or genotypes. We use a likelihood framework. Misclassification errors are estimated via double sampling. Unbiased estimates of penetrances and genotype frequencies are determined through application of the Expectation-Maximization algorithm. We perform simulation studies to evaluate false-positive rates for various genotype classification weights (recessive, dominant, additive). We compare simulated power between the LTT ae and its genotypic test equivalent, the LRT ae, in the presence of phenotype and genotype misclassification, to evaluate power gains of the LTT ae for multi-locus haplotype association with a dominant mode of inheritance. Finally, we apply LTT ae and a method without double-sample information (LTT std) to double-sampled phenotype data for an actual Alzheimer's disease (AD) case-control study with ApoE genotypes. Simulation results suggest that the LTT ae maintains correct false-positive rates in the presence of misclassification. For power simulations, the LTT ae method is at least as powerful as LRT ae method, with a maximum power gain of 0.42 over the LRT ae method for certain parameter settings. For AD data, LTT ae provides more significant evidence for association (permutation p=0.0522) than LTT std (permutation p=0.1684). This is due to observed phenotype misclassification. The LTT ae statistic enables researchers to apply linear trend tests to case-control genetic data, increasing power to detect association in the presence of misclassification. If the disease MOI is known, LTT ae methods are usually more powerful due to the fact that the statistic has fewer degrees of freedom.

Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation

We describe a hierarchical regression modeling approach to selection of a subset of markers from the first stage of a genomewide association scan to carry forward to subsequent stages for testing on an independent set of subjects. Rather than simply selecting a subset of most significant marker-disease associations at some cutoff chosen to maximize the cost efficiency of a multistage design, we propose a prior model for the true noncentrality parameters of these associations composed of a large mass at zero and a continuous distribution of nonzero values. The prior probability of nonzero values and their prior means can be functions of various covariates characterizing each marker, such as their location relative to genes or evolutionary conserved regions, or prior linkage or association data. We propose to take the top ranked posterior expectations of the noncentrality parameters for confirmation in later stages of a genomewide scan. The statistical performance of this approach is compared with the traditional p-value ranking by simulation studies. We show that the ranking by posterior expectations performs better at selecting the true positive association than a simple ranking of p-values if at least some of the prior covariates have predictive value.

Detecting haplotype effects in genomewide association studies

The analysis of genomewide association studies requires methods that are both computationally feasible and statistically powerful. Given the large-scale collection of single nucleotide polymorphisms (SNPs), it is desirable to explore the information contained in their interrelationships. In particular, utilizing haplotypes rather than individual SNPs and accounting for correlations of polymorphisms in adjustment for multiple testing can lead to increased power. We present a statistically powerful and numerically efficient method based on sliding windows of adjacent SNPs to detect haplotype-disease association in genomewide studies. This method consists of an efficient algorithm to calculate a proper likelihood-ratio statistic for any given window of SNPs, along with an accurate and efficient Monte Carlo procedure to adjust for multiple testing. Simulation studies using the HapMap data showed that the proposed method performs well in realistic situations. We applied the new method to a case-control study on rheumatoid arthritis and identified several loci worthy of further investigations.

Analysis of genetic variation in Ashkenazi Jews by high density SNP genotyping

BACKGROUND

Genetic isolates such as the Ashkenazi Jews (AJ) potentially offer advantages in mapping novel loci in whole genome disease association studies. To analyze patterns of genetic variation in AJ, genotypes of 101 healthy individuals were determined using the Affymetrix EAv3 500 K SNP array and compared to 60 CEPH-derived HapMap (CEU) individuals. 435,632 SNPs overlapped and met annotation criteria in the two groups.

RESULTS

A small but significant global difference in allele frequencies between AJ and CEU was demonstrated by a mean FST of 0.009 (P < 0.001); large regions that differed were found on chromosomes 2 and 6. Haplotype blocks inferred from pairwise linkage disequilibrium (LD) statistics (Haploview) as well as by expectation-maximization haplotype phase inference (HAP) showed a greater number of haplotype blocks in AJ compared to CEU by Haploview (50,397 vs. 44,169) or by HAP (59,269 vs. 54,457). Average haplotype blocks were smaller in AJ compared to CEU (e.g., 36.8 kb vs. 40.5 kb HAP). Analysis of global patterns of local LD decay for closely-spaced SNPs in CEU demonstrated more LD, while for SNPs further apart, LD was slightly greater in the AJ. A likelihood ratio approach showed that runs of homozygous SNPs were approximately 20% longer in AJ. A principal components analysis was sufficient to completely resolve the CEU from the AJ.

CONCLUSION

LD in the AJ versus was lower than expected by some measures and higher by others. Any putative advantage in whole genome association mapping using the AJ population will be highly dependent on regional LD structure.

The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment

Metabolic response to the triglyceride (TG)-lowering drug, fenofibrate, is shaped by interactions between genetic and environmental factors, yet knowledge regarding the genetic determinants of this response is primarily limited to single-gene effects. Since very low-density lipoprotein (VLDL) is the central carrier of fasting TG, identifying factors that affect both total TG and VLDL-TG response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 688 individuals from 161 families were genotyped for 91 single-nucleotide polymorphisms (SNPs) in 25 genes known to be involved in lipoprotein metabolism. Using generalized estimating equations to control for family structure, we performed linear modeling to investigate whether single SNPs, single covariates, SNP-SNP interactions, and/or SNP-covariate interactions had a significant association with the change in total fasting TG and fasting VLDL-TG after 3 weeks of fenofibrate treatment. A 10-iteration fourfold cross-validation procedure was used to validate significant associations and quantify their predictive abilities. More than one-third of the significant, cross-validated SNP-SNP interactions predicting each outcome involved just five SNPs, showing that these SNPs are of key importance to fenofibrate response. Multiple variable models constructed using the top-ranked SNP--covariate interactions explained 11.9% more variation in the change in TG and 7.8% more variation in the change in VLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate response, which can be used to target fenofibrate therapy to individuals who are most likely to benefit from the drug.

Genome-wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays

General cognitive ability (g), which refers to what cognitive abilities have in common, is an important target for molecular genetic research because multivariate quantitative genetic analyses have shown that the same set of genes affects diverse cognitive abilities as well as learning disabilities. In this first autosomal genome-wide association scan of g, we used a two-stage quantitative trait locus (QTL) design with pooled DNA to screen more than 500 000 single nucleotide polymorphisms (SNPs) on microarrays, selecting from a sample of 7000 7-year-old children. In stage 1, we screened for allele frequency differences between groups pooled for low and high g. In stage 2, 47 SNPs nominated in stage 1 were tested by individually genotyping an independent sample of 3195 individuals, representative of the entire distribution of g scores in the full 7000 7-year-old children. Six SNPs yielded significant associations across the normal distribution of g, although only one SNP remained significant after a false discovery rate of 0.05 was imposed. However, none of these SNPs accounted for more than 0.4% of the variance of g, despite 95% power to detect associations of that size. It is likely that QTL effect sizes, even for highly heritable traits such as cognitive abilities and disabilities, are much smaller than previously assumed. Nonetheless, an aggregated ‘SNP set’ of the six SNPs correlated 0.11 (P < 0.00000003) with g. This shows that future SNP sets that will incorporate many more SNPs could be useful for predicting genetic risk and for investigating functional systems of effects from genes to brain to behavior.

A novel genetic marker for coronary spasm in women from a genome-wide single nucleotide polymorphism analysis

OBJECTIVE

Coronary spasm plays an important role in the pathogenesis of variant angina and also ischemic heart diseases in general, and it is more likely to occur in angiographically normal coronary arteries than in stenotic coronary arteries. We previously found a -786T/C polymorphism in the 5'-flanking region of the endothelial nitric oxide synthase (eNOS) gene and reported that this polymorphism is associated with coronary spasm. We report on an investigation of the genetic factor(s) associated with coronary spasm utilizing a genome-wide case-control study.

METHODS AND RESULTS

We recruited 411 consecutive Japanese women (201 with coronary spasm; 210 controls) who were all underwent an acetylcholine provocation test. For single nucleotide polymorphism analysis (SNP), 116,204 SNPs were genotyped for 100 women (50 with coronary spasm; 50 controls) utilizing the Affymetrix GeneChip 100 K Set. Case-control studies were performed with 311 women (151 with coronary spasm; 160 controls) using the 10 lowest permutation P value SNPs from the initial SNP analysis. Finally, we discovered SNP rs10498345, a genetic marker for coronary spasm in Japanese women (Odds ratio=0.43, P=9.48x10(-7)). Haplotype analysis showed that haplotype H2, the only haplotype containing the protective A allele at SNP rs10498345, was most strongly associated with coronary spasm (permutation P value <1x10(-4)). SNP rs10498345 was strongly associated with the vasoconstrictor response to acetylcholine. Northern blot analysis revealed a novel 4.7 kb RNA transcript, which lacked poly (A), nearby SNP rs10498345.

CONCLUSIONS

SNP rs10498345 was strongly associated with coronary spasm in Japanese women utilizing genome-wide SNP analysis.

From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions

A brief history of human genetics and genomics is provided, comparing recent progress in those fields with that in pharmacogenetics and pharmacogenomics, which are subsets of genetics and genomics, respectively. Sequencing of the entire human genome, the mapping of common haplotypes of single-nucleotide polymorphisms (SNPs), and cost-effective genotyping technologies leading to genome-wide association (GWA) studies - have combined convincingly in the past several years to demonstrate the requirements needed to separate true associations from the plethora of false positives. While research in human genetics has moved from monogenic to oligogenic to complex diseases, its pharmacogenetics branch has followed, usually a few years behind. The continuous discoveries, even today, of new surprises about our genome cause us to question reviews declaring that "personalized medicine is almost here" or that "individualized drug therapy will soon be a reality." As summarized herein, numerous reasons exist to show that an "unequivocal genotype" or even an "unequivocal phenotype" is virtually impossible to achieve in current limited-size studies of human populations. This problem (of insufficiently stringent criteria) leads to a decrease in statistical power and, consequently, equivocal interpretation of most genotype-phenotype association studies. It remains unclear whether personalized medicine or individualized drug therapy will ever be achievable by means of DNA testing alone.

Joint modeling of linkage and association using affected sib-pair data

There has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT.

Heritability of alternative splicing in the human genome

Alternative pre-mRNA splicing increases proteomic diversity and provides a potential mechanism underlying both phenotypic diversity and susceptibility to genetic disorders in human populations. To investigate the variation in splicing among humans on a genome-wide scale, we use a comprehensive exon-targeted microarray to examine alternative splicing in lymphoblastoid cell lines (LCLs) derived from the CEPH HapMap population. We show the identification of transcripts containing sequence verified exon skipping, intron retention, and cryptic splice site usage that are specific between individuals. A number of novel alternative splicing events with no previous annotations in either the RefSeq and EST databases were identified, indicating that we are able to discover de novo splicing events. Using family-based linkage analysis, we demonstrate Mendelian inheritance and segregation of specific splice isoforms with regulatory haplotypes for three genes: OAS1, CAST, and CRTAP. Allelic association was further used to identify individual SNPs or regulatory haplotype blocks linked to the alternative splicing event, taking advantage of the high-resolution genotype information from the CEPH HapMap population. In one candidate, we identified a regulatory polymorphism that disrupts a 5' splice site of an exon in the CAST gene, resulting in its exclusion in the mutant allele. This report illustrates that our approach can detect both annotated and novel alternatively spliced variants, and that such variation among individuals is heritable and genetically controlled.

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.

Successful genome-wide scan in paired blood and buccal samples

Interest in genome-wide association studies to identify susceptibility alleles for cancer is growing, and several are currently planned or under way. Although the feasibility of collecting buccal cell samples as an alternative to venous blood samples as a source of genomic DNA has been shown, the validity of using DNA from buccal cells for genome-wide scans has not been assessed. We used 46 paired buffy coat and buccal cell samples to test the feasibility of using DNA from buccal cells for genotyping with the HumanHap300 Bead Chip (v.1.0.0) on the Illumina Infinium II platform. Genotyping was successful in every sample, regardless of DNA yield or sample type. Of the 317,502 genotypes attempted, 315,314 (99.3%) were successfully called. Completion rates were similar for buffy coat and buccal cell samples (99.63% and 99.44%, respectively; P = 0.15). Completion rates <99% were observed in only four samples and did not differ by specimen type. The paired samples showed exceptionally high concordance (99.96%). These results show that buccal cell samples collected and processed under optimal conditions can be used for genome-wide association studies with results comparable to those obtained from DNA extracted from buffy coat.

Fine mapping versus replication in whole-genome association studies

Association replication studies have a poor track record and, even when successful, often claim association with different markers, alleles, and phenotypes than those reported in the primary study. It is unknown whether these outcomes reflect genuine associations or false-positive results. A greater understanding of these observations is essential for genomewide association (GWA) studies, since they have the potential to identify multiple new associations that that will require external validation. Theoretically, a repeat association with precisely the same variant in an independent sample is the gold standard for replication, but testing additional variants is commonplace in replication studies. Finding different associated SNPs within the same gene or region as that originally identified is often reported as confirmatory evidence. Here, we compare the probability of replicating a gene or region under two commonly used marker-selection strategies: an "exact" approach that involves only the originally significant markers and a "local" approach that involves both the originally significant markers and others in the same region. When a region of high intermarker linkage disequilibrium is tested to replicate an initial finding that is only weak association with disease, the local approach is a good strategy. Otherwise, the most powerful and efficient strategy for replication involves testing only the initially identified variants. Association with a marker other than that originally identified can occur frequently, even in the presence of real effects in a low-powered replication study, and instances of such association increase as the number of included variants increases. Our results provide a basis for the design and interpretation of GWA replication studies and point to the importance of a clear distinction between fine mapping and replication after GWA.

Mechanisms of disease: The genetic basis of coronary heart disease

Since completion of the human genome sequence, considerable progress has been made in determining the genetic basis of human diseases. Understanding the genetic basis of coronary heart disease (CHD), the leading cause of mortality in developed countries, is a priority. Here we provide an update on the genetic basis of CHD, focusing mainly on the clinical manifestations rather than the risk factors, most of which are heritable and also influenced by genetic factors. The challenges faced when identifying clinically relevant genetic determinants of CHD include phenotypic and genetic heterogeneity, and gene-gene and gene-environment interactions. In addition, the etiologic spectrum includes common genetic variants with small effects, as well as rare genetic variants with large effects. Advances such as the cataloging of human genetic variation, new statistical approaches for analyzing massive amounts of genetic data, and the development of high-throughput single-nucleotide polymorphism genotyping platforms, will increase the likelihood of success in the search for genetic determinants of CHD. Such knowledge could refine cardiovascular risk stratification and facilitate the development of new therapies.

Simulating association studies: a data-based resampling method for candidate regions or whole genome scans

MOTIVATION

Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realistic patterns of linkage disequilibrium and allele frequencies for typed SNPs.

METHODS

We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project.

RESULTS

Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.

Multistage Sampling for Genetic Studies

In the past, to study Mendelian diseases, segregating families have been carefully ascertained for segregation analysis, followed by collecting extended multiplex families for linkage analysis. This would then be followed by association studies, using independent case-control samples and/or additional family data. Recently, for complex diseases, the initial sampling has been for a genome-wide linkage analysis, often using independent sib-pairs or nuclear families, to identify candidate regions for follow-up with association studies, again using case-control samples and/or additional family data. We now have the ability to conduct genome-wide association studies using 100,000-500,000 diallelic genetic markers. For such studies we focus especially on efficient two-stage association sampling designs, which can retain nearly optimal statistical power at about half the genotyping cost. Similarly, beginning an association study by genotyping pooled samples may also be a viable option if the cost of accurately pooling DNA samples outweighs genotyping costs. Finally, we note that the sampling of family data for linkage analysis is not a practice that should be automatically discontinued.

Successful aging: from phenotype to genotype

Despite worldwide interest in the increasing human "healthspan," inadequate experimental attention has been dedicated to identifying genetic influences on successful aging beyond those that influence longevity alone. Although it is an under-studied topic, some promising leads have emerged from the existing genetic studies of successful aging. Here we describe the results of a systematic review of published family, twin, linkage, and association studies of successful aging that evaluated at least one other characteristic of healthy aging in addition to longevity. We identified 29 studies that met our criteria. Although methodological inconsistencies in sampling and phenotypes were frequent, we found evidence for a substantial genetic contribution to successful aging, including several specific genes (APOE, GSTT1, IL6, IL10, PON1, and SIRT3) that are promising candidates for future molecular genetic research. In addition to reviewing this literature, we provide recommendations for advancing our understanding of the genetic basis of successful aging.

Entropy-based joint analysis for two-stage genome-wide association studies

Genome-wide association studies (GWAS) are being conducted to identify common genetic variants that predispose to human diseases to unravel the genetic etiology of complex human diseases now. Because of genotyping cost constraints, it often follows a two-stage design, in which a large number of markers are identified in a proportion of the available samples in stage 1, and then the markers identified in stage 1 are examined in all the samples in stage 2. In this paper, we introduce a nonlinear entropy-based statistic for joint analysis for two-stage genome-wide association studies. Type I error rates and power of the entropy-based statistic for association tests are validated using simulation studies in single-locus test. The power of entropy-based joint analysis is investigated by simulations. And the results suggest that entropy-based joint analysis is always more powerful than linear joint analysis that uses a linear function of risk allele frequencies in cases and controls when detecting rare genetic variants; the powers of these two joint analyses are comparable when detecting common genetic variants. Furthermore, when the false discovery rate is controlled, entropy-based joint analysis is more powerful and needs fewer samples than linear joint analysis that uses a linear function of risk allele frequencies in cases and controls. So, we recommend we should use entropy-based strategy for two-stage genome-wide association studies to detect the rare and common genetic variants with moderate to large genetic effect underlying a complex disease.

Genome-wide association study identifies novel breast cancer susceptibility loci

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2 > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10(-7)). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

A Bayesian measure of the probability of false discovery in genetic epidemiology studies

In light of the vast amounts of genomic data that are now being generated, we propose a new measure, the Bayesian false-discovery probability (BFDP), for assessing the noteworthiness of an observed association. BFDP shares the ease of calculation of the recently proposed false-positive report probability (FPRP) but uses more information, has a noteworthy threshold defined naturally in terms of the costs of false discovery and nondiscovery, and has a sound methodological foundation. In addition, in a multiple-testing situation, it is straightforward to estimate the expected numbers of false discoveries and false nondiscoveries. We provide an in-depth discussion of FPRP, including a comparison with the q value, and examine the empirical behavior of these measures, along with BFDP, via simulation. Finally, we use BFDP to assess the association between 131 single-nucleotide polymorphisms and lung cancer in a case-control study.

Polymorphisms in 33 inflammatory genes and risk of myocardial infarction--a system genetics approach

The hypothesis of a causal link between inflammation and atherosclerosis would be strengthened if variants of inflammatory genes were associated with disease. Polymorphisms of 33 genes encoding inflammatory molecules were tested for association with myocardial infarction (MI). Patients with MI and a parental history of MI (n = 312) and controls from the UK (n = 317) were genotyped for 162 polymorphisms. Thirteen polymorphisms were associated with MI (P values ranging from 0.003 to 0.041). For three genes, ITGB1, SELP, and TNFRSF1B haplotype frequencies differed between patients and controls (P values < 0.01). We further assessed the simultaneous contribution of all polymorphisms and relevant covariates to MI using a two-step strategy of data mining relying on Random Forest and DICE algorithms. In a replication study involving two independent samples from the UK (n = 649) and France (n = 706), one interaction between the ITGA4/R898Q polymorphism and current smoking status was replicated. This study illustrates a strategy for assessing the joint effect of a large number of polymorphisms on a phenotype that may provide information that single locus or single gene analysis may fail to uncover. Overall, there was weak evidence for an implication of inflammatory polymorphisms on susceptibility to MI.

Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease

Background

Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable.

Results

Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty.

Conclusion

Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required.

A genetic association analysis of cognitive ability and cognitive ageing using 325 markers for 109 genes associated with oxidative stress or cognition

Background

Non-pathological cognitive ageing is a distressing condition affecting an increasing number of people in our 'ageing society'. Oxidative stress is hypothesised to have a major role in cellular ageing, including brain ageing.

Results

Associations between cognitive ageing and 325 single nucleotide polymorphisms (SNPs), located in 109 genes implicated in oxidative stress and/or cognition, were examined in a unique cohort of relatively healthy older people, on whom we have cognitive ability scores at ages 11 and 79 years (LBC1921). SNPs showing a significant positive association were then genotyped in a second cohort for whom we have cognitive ability scores at the ages of 11 and 64 years (ABC1936). An intronic SNP in the APP gene (rs2830102) was significantly associated with cognitive ageing in both LBC1921 and a combined LBC1921/ABC1936 analysis (p < 0.01), but not in ABC1936 alone.

Conclusion

This study suggests a possible role for APP in normal cognitive ageing, in addition to its role in Alzheimer's disease.

A grid-search algorithm for optimal allocation of sample size in two-stage association studies

Multiple testing occurs commonly in genome-wide association studies with dense SNPs map. With numerous SNPs, not only the genotyping cost and time increase dramatically, many family wise error rate (FWER) controlling methods may fail for being too conservative and of less power when detecting SNPs associated with disease is of interest. Recently, several powerful two-stage strategies for multiple testing have received great attention. In this paper, we propose a grid-search algorithm for an optimal design of sample size allocation for these two-stage procedures. Two types of constraints are considered, one is the fixed overall cost and the other is the limited sample size. With the proposed optimal allocation of sample size, bearable false-positive results and larger power can be achieved to meet the limitations. The simulations indicate, as a general rule, allocating at least 80% of the total cost in stage one provides maximum power, as opposed to other methods. If per-genotyping cost in stage two differs from that in stage one, downward proportion of the total cost in earlier stage maintains good power. For limited total sample size, evaluating all the markers on 55% of the subjects in the first stage provides the maximum power while the cost reduction is approximately 43%.

Candidate gene association study of type 2 diabetes in a nested case-control study of the EPIC-Potsdam cohort - role of fat assimilation

To search for common variants etiological for type 2 diabetes, we screened 15 genes involved in fat assimilation for sequence variants. Approximately 55 kb in promoter and coding regions, and intron/splice sites were sequenced by cycle sequencing. In the set of 15 genes, 71 single nucleotide polymorphisms (SNPs) were detected. 33 SNPs were presumed to be functionally significant and were genotyped in 192 incident type 2 diabetes subjects and 384 matched controls from the European Prospective Investigation into Cancer and Nutrition-Potsdam cohort. A total of 27 SNPs out of 15 genes showed no statistical association with type 2 diabetes in our study. Six SNPs demonstrated nominal association with type 2 diabetes, with the most significant marker (FABP6 Thr79Met) having an adjusted odds ratio of 0.45 (95% CI 0.22-0.92) in homozygous Met allele carriers. Evidence for an association with disease status was also found for a novel Arg109Cys (g.2129C > T) variant of colipase, 5'UTR (rs2084202) and Met71Val (rs8192506) variants of diazepam-binding inhibitor, Arg298His (rs13283456) of PTGES2, and a novel promoter variant (g.-1324G > A) of SLC27A5. The results presented here provide preliminary evidence for the association of common variants in genes involved in fat assimilation with the genetic susceptibility of type 2 diabetes. However, they definitely need further verification.

[A review of power and sample size estimation in genomewide association studies]

Power and sample size estimation is one of the crucially important steps in planning a genetic association study to achieve the ultimate goal, identifying candidate genes for disease susceptibility, by designing the study in such a way as to maximize the success possibility and minimize the cost. Here we review the optimal two-stage genotyping designs for genomewide association studies recently investigated by Wang et al(2006). We review two mathematical frameworks most commonly used to compute power in genetic association studies prior to the main study: Monte-Carlo and non-central chi-square estimates. Statistical powers are computed by these two approaches for case-control genotypic tests under one-stage direct association study design. Then we discuss how the linkage disequilibrium strength affects power and sample size, and how to use empirically-derived distributions of important parameters for power calculations. We provide useful information on publicly available software developed to compute power and sample size for various study designs.

Non-Replication and Inconsistency in the Genome-Wide Association Setting

Non-replication and inconsistency had been common features in the search for common variants of candidate genes affecting the risk of complex diseases. They may continue to require attention in the current era, when massive hypothesis-free testing of genetic variants is feasible. An empirical evaluation of the early experience with genome-wide association (GWA) studies suggests several examples where proposed associations have failed to be replicated by subsequent investigations. Non-replication and inconsistency is defined here in the framework of cumulative meta-analysis. Ideally, associations exist, GWA finds them, and subsequent investigations should replicate them. However, a number of other possibilities need to be considered. No common genetic variants may associate with the phenotype of interest and GWA may find nothing; or associations may exist, but GWA may miss them. Associations that do not exist may be falsely selected by the GWA and subsequent studies may appropriately refute them or falsely replicate them. Finally, GWA may find true associations that are nevertheless falsely non-replicated in the subsequent studies; or associations may be genuinely inconsistent across study populations. A list of options is presented for consideration in each of these scenarios.

Tagging single nucleotide polymorphisms in cell cycle control genes and susceptibility to invasive epithelial ovarian cancer

High-risk susceptibility genes explain <40% of the excess risk of familial ovarian cancer. Therefore, other ovarian cancer susceptibility genes are likely to exist. We have used a single nucleotide polymorphism (SNP)-tagging approach to evaluate common variants in 13 genes involved in cell cycle control-CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, and CDKN2D-and risk of invasive epithelial ovarian cancer. We used a two-stage, multicenter, case-control study. In stage 1, 88 SNPs that tag common variation in these genes were genotyped in three studies from the United Kingdom, United States, and Denmark ( approximately 1,500 cases and 2,500 controls). Genotype frequencies in cases and controls were compared using logistic regression. In stage 2, eight other studies from Australia, Poland, and the United States ( approximately 2,000 cases and approximately 3,200 controls) were genotyped for the five most significant SNPs from stage 1. No SNP was significant in the stage 2 data alone. Using the combined stages 1 and 2 data set, CDKN2A rs3731257 and CDKN1B rs2066827 were associated with disease risk (unadjusted P trend = 0.008 and 0.036, respectively), but these were not significant after adjusting for multiple testing. Carrying the minor allele of these SNPs was found to be associated with reduced risk [OR, 0.91 (0.85-0.98) for rs3731257; and OR, 0.93 (0.87-0.995) for rs2066827]. In conclusion, we have found evidence that a single tagged SNP in both the CDKN2A and CDKN1B genes may be associated with reduced ovarian cancer risk. This study highlights the need for multicenter collaborations for genetic association studies.

Identifying the genotype behind the phenotype: a role model found in VKORC1 and its association with warfarin dosing

Genotype-phenotype studies in pharmacogenomics promise to identify the genetic factors that contribute substantially to variation in individual drug response. While most genetic association studies have failed to deliver this promise, several recent examples serve as a reminder that these associations do exist and can be identified when investigated using well-designed studies. Here, we describe the path taken to identify the association between common vitamin K epoxide reductase complex subunit 1 genetic variation and warfarin dosing in patients. We also describe the key elements that led the way, such as definition of the phenotype, confirmation of a genetic component, determination of biological plausibility and selection of genetic polymorphisms. We also describe several avenues that are yet to be explored for the specific vitamin K epoxide reductase complex subunit 1 warfarin example that can also be generalized as future directions for many genetic association studies in pharmacogenomics. These future avenues will be best explored using diverse approaches encompassing clinical, statistical and genomic methods currently being developed for genotype-phenotype studies in human populations.

Microarrays

Microarrays are revolutionizing genetics by making it possible to genotype hundreds of thousands of DNA markers and to assess the expression (RNA transcripts) of all of the genes in the genome. Microarrays are slides the size of a postage stamp that contain millions of DNA sequences to which single-stranded DNA or RNA can hybridize. This miniaturization requires little DNA or RNA and makes the method fast and inexpensive; multiple assays of each target make the method highly accurate. DNA microarrays with hundreds of thousands of DNA markers have made it possible to conduct systematic scans of the entire genome to identify genetic associations with complex disorders or dimensions likely to be influenced by many genes of small effect size. RNA microarrays can provide snapshots of gene expression across all of the genes in the genome at any time in any tissue, which has far-reaching applications such as structural and functional 'genetic neuroimaging' and providing a biological basis for understanding environmental influence.

Adaptive two-stage analysis of genetic association in case-control designs

We study a two-stage analysis of genetic association for case-control studies. In the first stage, we compare Hardy-Weinberg disequilibrium coefficients between cases and controls and, in the second stage, we apply the Cochran- Armitage trend test. The two analyses are statistically independent when Hardy-Weinberg equilibrium holds in the population, so all the samples are used in both stages. The significance level in the first stage is adaptively determined based on its conditional power. Given the level in the first stage, the level for the second stage analysis is determined with the overall Type I error being asymptotically controlled. For finite sample sizes, a parametric bootstrap method is used to control the overall Type I error rate. This two-stage analysis is often more powerful than the Cochran-Armitage trend test alone for a large association study. The new approach is applied to SNPs from a real study.

Methods for linkage disequilibrium mapping in crops

Linkage disequilibrium (LD) mapping in plants detects and locates quantitative trait loci (QTL) by the strength of the correlation between a trait and a marker. It offers greater precision in QTL location than family-based linkage analysis and should therefore lead to more efficient marker-assisted selection, facilitate gene discovery and help to meet the challenge of connecting sequence diversity with heritable phenotypic differences. Unlike family-based linkage analysis, LD mapping does not require family or pedigree information and can be applied to a range of experimental and non-experimental populations. However, care must be taken during analysis to control for the increased rate of false positive results arising from population structure and variety interrelationships. In this review, we discuss how suitable the recently developed alternative methods of LD mapping are for crops.

Gene modifiers of lung disease

PURPOSE OF REVIEW

Cystic fibrosis is a recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, but there is great heterogeneity of lung-disease severity. If we could understand non-CFTR genetic factors (modifier genes) that contribute to the severity of lung disease, we could develop novel therapies. Early studies were small and/or phenotyping methodologies were limited; consequently, most findings have not been replicated.

RECENT FINDINGS

Several large gene-modifier studies have been established. These studies are complementary in terms of design and the types of patient, and employ specialized approaches to quantitate pulmonary disease severity. Emerging data indicate that non-CFTR genetic variants contribute to at least half the variability in pulmonary disease severity, and genetic variation in transforming growth factor beta1 clearly modifies the severity of cystic fibrosis lung disease.

SUMMARY

The cystic fibrosis community is working to identify the most important gene modifiers for lung disease. Candidate genes are currently being tested, and high-resolution, whole-genome scans are now affordable. For cystic fibrosis, several hundred thousand genetic markers (single-nucleotide polymorphisms) will identify key chromosomal regions and genes. If successful, these studies will provide the opportunity for novel approaches and therapies for cystic fibrosis lung disease.