CiteULike: jyuh's gwa

Genome-wide association study for renal traits in the Framingham Heart and Atherosclerosis Risk in Communities Studies

Anna Kottgen — 2008-06-04T19:30:37-00:00

BMC Medical Genetics, Vol. 9 (03 June 2008), 49.

The positives, protocols, and perils of genome-wide association.

Benjamin M Neale — 2008-06-02T03:52:29-00:00

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics (23 May 2008)

Genome-wide association aims to comprehensively survey genetic variation for the purposes of disease and trait mapping. We provide a brief history of the development of genetic technology necessary to realize genome-wide association. From there we identify and review the publicly available resources for conducting such work including the molecular technologies, genomic databases, and analytic tools. Following on from the analytic tools, we highlight common analytic considerations, ranging from study design, quality control, and data cleaning to association analysis and replication. We conclude with a look toward future developments such as the analysis of copy number variation and integration of expression and epigenetic phenomenon into genome-wide association. (c) 2008 Wiley-Liss, Inc.

Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network.

Ivan Iossifov — 2008-05-25T13:27:49-00:00

Genome research (16 April 2008)

Common hereditary neurodevelopmental disorders such as autism, bipolar disorder, and schizophrenia are, most likely, both genetically multifactorial and heterogeneous. Because of these characteristics traditional methods for genetic analysis fail when applied to such diseases. To address the problem we propose a novel probabilistic framework that combines the standard genetic linkage formalism with whole-genome molecular-interaction data to predict pathways or networks of interacting genes that contribute to common heritable disorders. We apply the model to three large genotype-phenotype datasets, and identify a small number of highly-significant candidate genes for autism (24), bipolar disorder (21) and schizophrenia (25), and predict a number of gene targets likely to be shared among the disorders.

High-density association study and nomination of susceptibility genes for hypertension in the Japanese National Project.

N Kato — 2008-05-23T03:12:59-00:00

Human molecular genetics, Vol. 17, No. 4. (15 February 2008), pp. 617-627.

Essential hypertension is one of the most common, complex diseases, of which considerable efforts have been made to unravel the pathophysiological mechanisms. Over the last decade, multiple genome-wide linkage analyses have been conducted using 300-900 microsatellite markers but no single study has yielded definitive evidence for 'principal' hypertension susceptibility gene(s). Here, we performed a three-tiered, high-density association study of hypertension, which has been recently made possible. For tier 1, we genotyped 80 795 SNPs distributed throughout the genome in 188 male hypertensive subjects and two general population control groups (752 subjects per group). For tier 2 (752 hypertensive and 752 normotensive subjects), we genotyped a panel of 2676 SNPs selected (odds ratio >or= 1.4 and P <or= 0.015 in tier 1) and identified 75 SNPs that showed similar tendency of association in tier 1 and tier 2 samples (P <or= 0.05 for allele frequency and P <or= 0.01 for genotype distribution tests). For tier 3 (619 hypertensive and 1406 normotensive subjects), we genotyped the 75 SNPs and found nine SNPs from seven genomic loci to be associated with hypertension (P <or= 0.05). In three of these loci, the lowest P-values were observed for rs3755351 (P = 1.7 x 10(-5)) in ADD2, rs3794260 (P = 0.0001) in KIAA0789 and rs1805762 (P = 0.0003) in M6PR when case-control comparison was made in the combined data. An SNP (rs3755351) within ADD2 had the lowest P-value and its experiment-wide significance level is 0.13. Thus, these results have nominated several susceptibility genes for hypertension, and independent replication will clarify their etiological relevance.

Genetic analysis of recently identified type 2 diabetes loci in 1,638 unselected patients with type 2 diabetes and 1,858 control participants from a Norwegian population-based cohort (the HUNT study).

J Hertel — 2008-05-23T03:08:08-00:00

Diabetologia, Vol. 51, No. 6. (June 2008), pp. 971-977.

AIMS/HYPOTHESIS: Recent genome-wide association studies performed in selected patients and control participants have provided strong support for several new type 2 diabetes susceptibility loci. To get a better estimation of the true risk conferred by these novel loci, we tested a completely unselected population of type 2 diabetes patients from a Norwegian health survey (the HUNT study). METHODS: We genotyped single nucleotide polymorphisms (SNPs) in PKN2, IGFBP2, FLJ39370 (also known as C4ORF32), CDKAL1, SLC30A8, CDKN2B, HHEX and FTO using a Norwegian population-based sample of 1,638 patients with type 2 diabetes and 1,858 non-diabetic control participants (the HUNT Study), for all of whom data on BMI, WHR, cholesterol and triacylglycerol levels were available. We used diabetes, measures of obesity and lipid values as phenotypes in case-control and quantitative association study designs. RESULTS: We replicated the association with type 2 diabetes for rs10811661 in the vicinity of CDKN2B (OR 1.20, 95% CI: 1.06-1.37, p = 0.004), rs9939609 in FTO (OR 1.14, 95% CI: 1.04-1.25, p = 0.006) and rs13266634 in SLC30A8 (OR 1.20, 95% CI: 1.09-1.33, p = 3.9 x 10(-4)). We found borderline significant association for the IGFBP2 SNP rs4402960 (OR 1.10, 95% CI: 0.99-1.22). Results for the HHEX SNP (rs1111875) and the CDKAL1 SNP (rs7756992) were non-significant, but the magnitude of effect was similar to previous estimates. We found no support for an association with the less consistently replicated FLJ39370 or PKN2 SNPs. In agreement with previous studies, FTO was most strongly associated with BMI (p = 8.4 x 10(-4)). CONCLUSIONS/INTERPRETATION: Our data show that SNPs near IGFBP2, CDKAL1, SLC30A8, CDKN2B, HHEX and FTO are also associated with diabetes in non-selected patients with type 2 diabetes.

MAX-rank: a simple and robust genome-wide scan for case-control association studies.

Qizhai Li — 2008-05-23T03:08:14-00:00

Human genetics (20 May 2008)

In genome-wide association studies (GWAS), single-marker analysis is usually employed to identify the most significant single nucleotide polymorphisms (SNPs). The trend test has been proposed for analysis of case-control association. Three trend tests, optimal for the recessive, additive and dominant models respectively, are available. When the underlying genetic model is unknown, the maximum of the three trend test results (MAX) has been shown to be robust against genetic model misspecification. Since the asymptotic distribution of MAX depends on the allele frequency of the SNP, using the P-value of MAX for ranking may be different from using the MAX statistic. Calculating the P-value of MAX for 300,000 (300 K) or more SNPs is computationally intensive and the software and program to obtain the P-value of MAX are not widely available. On the other hand, the MAX statistic is very easy to calculate without complex computer programs. Thus, we study whether or not one could use the MAX statistic instead of its P-value to rank SNPs in GWAS. The approaches using the MAX and its P-value to rank SNPs are referred to as MAX-rank and P-rank. By applying MAX-rank and P-rank to simulated and four real datasets from GWAS, we found the ranks of SNPs with true association are very similar using both approaches. Thus, we recommend to use MAX-rank for genome-wide scans. After the top-ranked SNPs are identified, their P-values based on MAX can be calculated and compared with the significance level.

Replication of genome-wide association studies of type 2 diabetes susceptibility in Japan.

Yukio Horikawa — 2008-05-23T03:08:23-00:00

The Journal of clinical endocrinology and metabolism (13 May 2008)

Background: In Europeans and populations of European origin, several groups have recently identified novel type 2 diabetes susceptibility genes including FTO, SLC30A8, HHEX, CDKAL1, CDKN2B, and IGF2BP2, none of which were in the list of functional candidates. Objective and Design: The aim of this study was to replicate in a Japanese population previously identified associations of SNPs within ten candidate loci with type 2 diabetes using a relatively large sample size: 1921 subjects with type 2 diabetes and 1622 normal controls. Results: A total of fifteen SNPs were genotyped. Eight SNPs in five loci were found to be associated with type 2 diabetes: rs3802177 [OR=1.16 (95% CI 1.05-1.27), P=4.5 x 10(-3)] in SLC30A8, rs1111875 [OR=1.27 (95% CI 1.14-1.40), P =1.4 x 10(-5)] and rs7923837 [OR=1.27 (95% CI 1.13-1.43), P =1.0 x 10(-4)] in HHEX, rs10811661 [OR=1.27 (95% CI 1.15-1.40), P =1.9 x 10(-6)] in CDKN2B, rs4402960 [OR=1.23 (95% CI 1.11-1.36), P =8.1 x 10(-5)] and rs1470579 [OR=1.18 (95% CI 1.07-1.31), P =8.3 x 10(-4)] in IGF2BP2, and rs7754840 [OR=1.28 (95% CI 1.17-1.41), P =4.5 x 10(-7)] and rs7756992 [OR=1.27 (95% CI 1.15-1.40), P =9.8 x 10(-7)] in CDKAL1. The first and second strongest associations were found at variants in CDKAL1 and CDKN2B, both of which are involved in regenerative capacity of pancreatic beta-cells. Conclusions: Some of these variants represent common type 2 diabetes-susceptibility genes in both Japanese and Europeans.

Strong association of common variants in the CDKN2A/CDKN2B region with type 2 diabetes in French Europids

Duesing — 2008-05-13T03:32:04-00:00

Diabetologia, Vol. 51, No. 5. (May 2008), pp. 821-826.

Genetics of Cardiovascular Diseases: From Single Mutations to the Whole Genome

Francois Cambien — 2008-05-23T03:01:53-00:00

Circulation, Vol. 116, No. 15. (9 October 2007), pp. 1714-1724.

10.1161/CIRCULATIONAHA.106.661751

Estimating odds ratios in genome scans: an approximate conditional likelihood approach.

A Ghosh — 2008-05-23T02:38:12-00:00

American journal of human genetics, Vol. 82, No. 5. (May 2008), pp. 1064-1074.

In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes.

Eleftheria Zeggini — 2008-04-02T08:50:44-00:00

Nature genetics (30 March 2008)

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

To what extent do scans of non-synonymous SNPs complement denser genome-wide association studies?

David Evans — 2008-01-17T18:08:22-00:00

European Journal of Human Genetics, Vol. aop, No. current.

Hypotheses in genome-wide association scans

Michael Nothnagel — 2008-05-14T10:47:27-00:00

European Journal of Human Genetics, Vol. aop, No. current.

WGAViewer: Software for genomic annotation of whole genome association studies

Dongliang Ge — 2008-05-05T18:41:31-00:00

Genome Res., Vol. 18, No. 4. (1 April 2008), pp. 640-643.

To meet the immediate need for a framework of post-whole genome association (WGA) annotation, we have developed WGAViewer, a suite of JAVA software tools that provides a user-friendly interface to automatically annotate, visualize, and interpret the set of P-values emerging from a WGA study. Most valuably, it can be used to highlight possible functional mechanisms in an automatic manner, for example, by directly or indirectly implicating a polymorphism with an apparent link to gene expression, and help to generate hypotheses concerning the possible biological bases of observed associations. The easily interpretable diagrams can then be used to identify the associations that seem most likely to be biologically relevant, and to select genomic regions that may need to be resequenced in a search for candidate causal variants. In this report, we used our recently completed study on host control of HIV-1 viral load during the asymptomatic set point period as an illustration for the heuristic annotation of this software and its contributive role in a successful WGA project. 10.1101/gr.071571.107

SNPLims: a data management system for genome wide association studies.

A Orro — 2008-05-07T06:44:59-00:00

BMC bioinformatics, Vol. 9 Suppl 2 (2008)

BACKGROUND: Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases.We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses. RESULTS: We have developed an information system mainly devoted to the storage and management of SNP genotype data produced by the Illumina platform from the raw outputs of genotyping into a relational database.The relational database can be accessed in order to import any existing data and export user-defined formats compatible with many different genetic analysis programs.After calculating family-based or case-control association study data, the results can be imported in SNPLims. One of the main features is to allow the user to rapidly identify and annotate statistically relevant polymorphisms from the large volume of data analyzed. Results can be easily visualized either graphically or creating ASCII comma separated format output files, which can be used as input to further analyses. CONCLUSIONS: The proposed infrastructure allows to manage a relatively large amount of genotypes for each sample and an arbitrary number of samples and phenotypes. Moreover, it enables the users to control the quality of the data and to perform the most common screening analyses and identify genes that become "candidate" for the disease under consideration.

A genome-wide search for linkage to chronic kidney disease in a community-based sample: the SAFHS

Nedal Arar — 2008-05-01T02:23:39-00:00

Nephrol. Dial. Transplant. (28 April 2008), gfn215.

Background. Chronic kidney disease (CKD) phenotypes such as albuminuria measured by urinary albumin creatinine ratio (ACR), elevated serum creatinine (SrCr) and/or decreased creatinine clearance (CrCl) and glomerular filtration rate (eGFR) are major risk factors for renal and cardiovascular diseases. Epidemiological studies have reported that CKD phenotypes cluster in families suggesting a genetic predisposition. However, studies reporting chromosomal regions influencing CKD are very limited. Therefore, the purpose of this study is to identify susceptible chromosomal regions for CKD phenotypes in Mexican American families enrolled in the San Antonio Family Heart Study (SAFHS). Methods. We used the variance components decomposition approach (implemented in the software package SOLAR) to perform linkage analysis on 848 participants from 26 families. A total of 417 microsatellite markers were genotyped at an average interval of 10 cM spanning 22 autosomal chromosomes. Results. All phenotypes were measured by standard procedures. Mean +/- SD values of ACR, SrCr, CrCl and eGFR were 0.06 +/- 0.38, 0.85 +/- 0.72 mg/dl, 129.85 +/- 50.37 ml/min and 99.18 +/- 25.69 ml/min/1.73 m2 body surface area, respectively. All four CKD phenotypes exhibited significant heritabilities (P < 0.0001). A genome-wide scan showed linkage on chromosome 2p25 for SrCr, CrCl and eGFR. Significant linkage was also detected on chromosome 9q21 for eGFR [logarithm of the odds (LOD) score = 3.87, P = 0.00005] and SrCr (LOD score = 2.6, P = 0.00026). ACR revealed suggestive evidence for linkage to a region on chromosome 20q12 (LOD score = 2.93, P = 0.00020). Conclusion. Findings indicate that chromosomal regions 2p25, 9q21 and 20q12 may have functional relevance to CKD phenotypes in Mexican Americans. 10.1093/ndt/gfn215

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

Mark Mccarthy — 2008-04-18T17:25:58-00:00

Nature Reviews Genetics, Vol. 9, No. 5., pp. 356-369.

How to interpret a genome-wide association study.

TA Pearson — 2008-03-29T01:12:45-00:00

JAMA, Vol. 299, No. 11. (19 March 2008), pp. 1335-1344.

Genome-wide association (GWA) studies use high-throughput genotyping technologies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) and relate them to clinical conditions and measurable traits. Since 2005, nearly 100 loci for as many as 40 common diseases and traits have been identified and replicated in GWA studies, many in genes not previously suspected of having a role in the disease under study, and some in genomic regions containing no known genes. GWA studies are an important advance in discovering genetic variants influencing disease but also have important limitations, including their potential for false-positive and false-negative results and for biases related to selection of study participants and genotyping errors. Although these studies are clearly many steps removed from actual clinical use, and specific applications of GWA findings in prevention and treatment are actively being pursued, at present these studies mainly represent a valuable discovery tool for examining genomic function and clarifying pathophysiologic mechanisms. This article describes the design, interpretation, application, and limitations of GWA studies for clinicians and scientists for whom this evolving science may have great relevance.

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

Fredrik Pettersson — 2008-03-04T17:29:04-00:00

BMC Bioinformatics, Vol. 9 (04 March 2008), 138.

Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies.

Hua Zhong — 2008-03-29T01:13:41-00:00

Biostatistics (28 February 2008)

Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.

Efficient Approximation of P-value of the Maximum of Correlated Tests, with Applications to Genome-Wide Association Studies.

Qizhai Li — 2008-03-28T09:44:55-00:00

Ann Hum Genet (3 March 2008)

Genome-wide association study (GWAS), typically involving 100,000 to 500,000 single-nucleotide polymorphisms (SNPs), is a powerful approach to identify disease susceptibility loci. In a GWAS, single-marker analysis, which tests one SNP at a time, is usually used as the first stage to screen SNPs across the genome in order to identify a small fraction of promising SNPs with relatively low p-values for further and more focused studies. For single-marker analysis, the trend test derived for an additive genetic model is often used. This may not be robust when the additive assumption is not appropriate for the true underlying disease model. A robust test, MAX, based on the maximum of three trend test statistics derived for recessive, additive, and dominant models, has been proposed recently for GWAS. But its p-value has to be evaluated through a resampling-based procedure, which is computationally challenging for the analysis of GWAS. Obtaining the p-value for MAX with adjustment for the covariates can be even more time-consuming. In this article, we provide a simple approximation for the p-value of the MAX test with or without adjusting for the covariates. The new method avoids resampling steps and thus makes the MAX test readily applicable to GWAS. We use simulation studies as well as real datasets on 17 confirmed disease-associated SNPs to assess the accuracy of the proposed method. We also apply the method to the GWAS of coronary artery disease.

Reporting and interpretation in genome-wide association studies.

Jon Wakefield — 2008-03-28T09:45:08-00:00

Int J Epidemiol (11 February 2008)

BACKGROUND: In the context of genome-wide association studies we critique a number of methods that have been suggested for flagging associations for further investigation. METHODS: The P-value is by far the most commonly used measure, but requires careful calibration when the a priori probability of an association is small, and discards information by not considering the power associated with each test. The q-value is a frequentist method by which the false discovery rate (FDR) may be controlled. RESULTS: We advocate the use of the Bayes factor as a summary of the information in the data with respect to the comparison of the null and alternative hypotheses, and describe a recently-proposed approach to the calculation of the Bayes factor that is easily implemented. The combination of data across studies is straightforward using the Bayes factor approach, as are power calculations. CONCLUSIONS: The Bayes factor and the q-value provide complementary information and when used in addition to the P-value may be used to reduce the number of reported findings that are subsequently not reproduced.

Examining the statistical properties of fine-scale mapping in large-scale association studies.

Steven Wiltshire — 2008-03-19T09:24:16-00:00

Genet Epidemiol (6 December 2007)

Interpretation of dense single nucleotide polymorphism (SNP) follow-up of genome-wide association or linkage scan signals can be facilitated by establishing expectation for the behaviour of primary mapping signals upon fine-mapping, under both null and alternative hypotheses. We examined the inferences that can be made regarding the posterior probability of a real genetic effect and considered different disease-mapping strategies and prior probabilities of association. We investigated the impact of the extent of linkage disequilibrium between the disease SNP and the primary analysis signal and the extent to which the disease gene can be physically localised under these scenarios. We found that large increases in significance (>2 orders of magnitude) appear in the exclusive domain of genuine genetic effects, especially in the follow-up of genome-wide association scans or consensus regions from multiple linkage scans. Fine-mapping significant association signals that reside directly under linkage peaks yield little improvement in an already high posterior probability of a real effect. Following fine-mapping, those signals that increase in significance also demonstrate improved localisation. We found local linkage disequiliptium patterns around the primary analysis signal(s) and tagging efficacy of typed markers to play an important role in determining a suitable interval for fine-mapping. Our findings help inform the interpretation and design of dense SNP-mapping follow-up studies, thus facilitating discrimination between a genuine genetic effect and chance fluctuation (false positive). Genet. Epidemiol. 2007. (c) 2007 Wiley-Liss, Inc.

Optimal two-stage testing for family-based genome-wide association studies.

S Macgregor — 2008-03-09T00:46:24-00:00

Am J Hum Genet, Vol. 82, No. 3. (March 2008)

Universal false discovery rate estimation methodology for genome-wide association studies.

K Forner — 2008-03-01T00:02:23-00:00

Hum Hered, Vol. 65, No. 4. (2008), pp. 183-194.

Genome-wide case-control association studies aim at identifying significant differential markers between sick and healthy populations. With the development of large-scale technologies allowing the genotyping of thousands of single nucleotide polymorphisms (SNPs) comes the multiple testing problem and the practical issue of selecting the most probable set of associated markers. Several False Discovery Rate (FDR) estimation methods have been developed and tuned mainly for differential gene expression studies. However they are based on hypotheses and designs that are not necessarily relevant in genetic association studies. In this article we present a universal methodology to estimate the FDR of genome-wide association results. It uses a single global probability value per SNP and is applicable in practice for any study design, using any statistic. We have benchmarked this algorithm on simulated data and shown that it outperforms previous methods in cases requiring non-parametric estimation. We exemplified the usefulness of the method by applying it to the analysis of experimental genotyping data of three Multiple Sclerosis case-control association studies.

Biostatistical aspects of genome-wide association studies.

A Ziegler — 2008-02-29T15:43:52-00:00

Biom J, Vol. 50, No. 1. (February 2008), pp. 8-28.

To search the entire human genome for association is a novel and promising approach to unravelling the genetic basis of complex genetic diseases. In these genome-wide association studies (GWAs), several hundreds of thousands of single nucleotide polymorphisms (SNPs) are analyzed at the same time, posing substantial biostatistical and computational challenges. In this paper, we discuss a number of biostatistical aspects of GWAs in detail. We specifically consider quality control issues and show that signal intensity plots are a sine qua condition non in today's GWAs. Approaches to detect and adjust for population stratification are briefly examined. We discuss different strategies aimed at tackling the problem of multiple testing, including adjustment of p -values, the false positive report probability and the false discovery rate. Another aspect of GWAs requiring special attention is the search for gene-gene and gene-environment interactions. We finally describe multistage approaches to GWAs.

Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays.

Stuart Macgregor — 2008-02-27T15:57:09-00:00

Nucleic Acids Res (14 February 2008)

Genome-wide association (GWA) studies to map genes for complex traits are powerful yet costly. DNA-pooling strategies have the potential to dramatically reduce the cost of GWA studies. Pooling using Affymetrix arrays has been proposed and used but the efficiency of these arrays has not been quantified. We compared and contrasted Affymetrix Genechip HindIII and Illumina HumanHap300 arrays on the same DNA pools and showed that the HumanHap300 arrays are substantially more efficient. In terms of effective sample size, HumanHap300-based pooling extracts >80% of the information available with individual genotyping (IG). In contrast, Genechip HindIII-based pooling only extracts approximately 30% of the available information. With HumanHap300 arrays concordance with IG data is excellent. Guidance is given on best study design and it is shown that even after taking into account pooling error, one stage scans can be performed for >100-fold reduced cost compared with IG. With appropriately designed two stage studies, IG can provide confirmation of pooling results whilst still providing approximately 20-fold reduction in total cost compared with IG-based alternatives. The large cost savings with Illumina HumanHap300-based pooling imply that future studies need only be limited by the availability of samples and not cost.

LDL-cholesterol concentrations: a genome-wide association study.

MS Sandhu — 2008-02-18T03:23:32-00:00

Lancet, Vol. 371, No. 9611. (9 February 2008), pp. 483-491.

BACKGROUND: LDL cholesterol has a causal role in the development of cardiovascular disease. Improved understanding of the biological mechanisms that underlie the metabolism and regulation of LDL cholesterol might help to identify novel therapeutic targets. We therefore did a genome-wide association study of LDL-cholesterol concentrations. METHODS: We used genome-wide association data from up to 11,685 participants with measures of circulating LDL-cholesterol concentrations across five studies, including data for 293 461 autosomal single nucleotide polymorphisms (SNPs) with a minor allele frequency of 5% or more that passed our quality control criteria. We also used data from a second genome-wide array in up to 4337 participants from three of these five studies, with data for 290,140 SNPs. We did replication studies in two independent populations consisting of up to 4979 participants. Statistical approaches, including meta-analysis and linkage disequilibrium plots, were used to refine association signals; we analysed pooled data from all seven populations to determine the effect of each SNP on variations in circulating LDL-cholesterol concentrations. FINDINGS: In our initial scan, we found two SNPs (rs599839 [p=1.7x10(-15)] and rs4970834 [p=3.0x10(-11)]) that showed genome-wide statistical association with LDL cholesterol at chromosomal locus 1p13.3. The second genome screen found a third statistically associated SNP at the same locus (rs646776 [p=4.3x10(-9)]). Meta-analysis of data from all studies showed an association of SNPs rs599839 (combined p=1.2x10(-33)) and rs646776 (p=4.8x10(-20)) with LDL-cholesterol concentrations. SNPs rs599839 and rs646776 both explained around 1% of the variation in circulating LDL-cholesterol concentrations and were associated with about 15% of an SD change in LDL cholesterol per allele, assuming an SD of 1 mmol/L. INTERPRETATION: We found evidence for a novel locus for LDL cholesterol on chromosome 1p13.3. These results potentially provide insight into the biological mechanisms that underlie the regulation of LDL cholesterol and might help in the discovery of novel therapeutic targets for cardiovascular disease.

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Daniel Shriner — 2008-02-11T09:56:30-00:00

Nucl. Acids Res. (7 February 2008), gkn007.

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO. 10.1093/nar/gkn007

Power analysis for genome-wide association studies

Robert Klein — 2007-08-29T05:42:08-00:00

BMC Genetics, Vol. 8 (28 August 2007), 58.

A new multipoint method for genome-wide association studies by imputation of genotypes.

Jonathan Marchini — 2007-06-23T07:42:01-00:00

Nat Genet (17 June 2007)

Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.

Statistical significance for genomewide studies.

JD Storey — 2005-06-30T17:29:52-00:00

Proc Natl Acad Sci U S A, Vol. 100, No. 16. (5 August 2003), pp. 9440-9445.

With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

Genome-wide scan for type 1 diabetic nephropathy in the Finnish population reveals suggestive linkage to a single locus on chromosome 3q.

AM Osterholm — 2007-06-23T03:46:45-00:00

Kidney Int, Vol. 71, No. 2. (January 2007), pp. 140-145.

Diabetic nephropathy (DN) is the primary cause of morbidity and mortality in patients with type 1 as well as type 2 diabetes, and accounts for 40% of end-stage renal disease in the Western world. Familial clustering of DN suggests importance of genetic factors in the development of the disease. In the present study, we performed a two-stage genome-wide scan to search for chromosomal loci containing susceptibility genes for nephropathy in patients with type 1 diabetes. In total, 83 discordant sib pairs (DSPs), sibs concordant for type 1 diabetes but discordant for nephropathy, were collected from Finland, a homogeneous population with one of the highest incidences of type 1 diabetes. To map loci for DN, we applied DSP analysis to detect linkage. In the initial scan, 73 DSPs were typed using 900 markers with an average intermarker distance of approximately 4 cM. Multipoint DSP analysis identified five chromosome regions (3q, 4p, 9q, 16q, and 22p) with maximum logarithm of odds (LOD) score (MLS) >or=1.0 (corresponding to a nominal P-value <or=0.015). In the second stage, additional 43 markers flanking these five loci were genotyped in all 83 DSPs. Using simulations, we determined the empirical threshold with LOD score of 1.76 and 3.12 for suggestive and significant linkage, respectively. No locus reached the genome-wide significance of 5%. However, one locus on 3q reached suggestive linkage with MLS of 2.67 (P=4.4 x 10(-4)). These results, together with data from others, suggest that the locus on 3q most likely has a susceptibility gene for DN.

A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes.

AS Krolewski — 2007-06-23T03:28:56-00:00

Kidney Int, Vol. 69, No. 1. (January 2006), pp. 129-136.

The main hallmark of diabetic nephropathy is elevation in urinary albumin excretion. We performed a genome-wide linkage scan in 63 extended families with multiple members with type II diabetes. Urinary albumin excretion, measured as the albumin-to-creatinine ratio (ACR), was determined in 426 diabetic and 431 nondiabetic relatives who were genotyped for 383 markers. The data were analyzed using variance components linkage analysis. Heritability (h2) of ACR was significant in diabetic (h2=0.23, P=0.0007), and nondiabetic (h2=0.39, P=0.0001) relatives. There was no significant difference in genetic variance of ACR between diabetic and nondiabetic relatives (P=0.16), and the genetic correlation (rG=0.64) for ACR between these two groups was not different from 1 (P=0.12). These results suggested that similar genes contribute to variation in ACR in diabetic and nondiabetic relatives. This hypothesis was supported further by the linkage results. Support for linkage to ACR was suggestive in diabetic relatives and became significant in all relatives for chromosome 22q (logarithm of odds, LOD=3.7) and chromosome 7q (LOD=3.1). When analyses were restricted to 59 Caucasian families, support for linkage in all relatives increased and became significant for 5q (LOD=3.4). In conclusion, genes on chromosomes 22q, 5q and 7q may contribute to variation in urinary albumin excretion in diabetic and nondiabetic individuals.

A genome-wide search for linkage to renal function phenotypes in West Africans with type 2 diabetes.

G Chen — 2007-06-23T02:18:44-00:00

Am J Kidney Dis, Vol. 49, No. 3. (March 2007), pp. 394-400.

BACKGROUND: Reduced renal function often is a major consequence of diabetes and hypertension. Although several indices of renal function (eg, creatinine clearance) are clearly heritable and show linkage to several genomic regions, the specific underlying genetic determinants are still being sought. The purpose of this study is to conduct a genome-wide search for regions linked to 3 renal function phenotypes, serum creatinine, creatinine clearance, and glomerular filtration rate (GFR), in persons with type 2 diabetes. METHODS: A genome-wide panel of 372 autosomal short tandem repeat markers at an average spacing of 9 centimorgan were typed in 691 patients with type 2 diabetes (321 sib pairs and 36 half-sib pairs) in an affected sib pair study in West Africa. Linkage analysis was conducted with the 3 phenotypes by using a multipoint variance components linkage method. RESULTS: Creatinine clearance showed higher logarithm of odds (LOD) score than the other 2 phenotypes. Linkage to creatinine clearance was observed on chromosomes 16 (marker D16S539, LOD score of 3.56, empirical P = 0.0001), 17 (D17S1298, LOD score of 2.08, empirical P = 0.0018), and 7 (D7S1818, LOD score of 1.84, nominal P = 0.00181, empirical P = 0.0022). Maximum LOD scores for serum creatinine were observed on chromosomes 10 (D10S1432, LOD score of 2.53, empirical P = 0.0001) and 3 (D3S2418, LOD score of 2.21, empirical P = 0.0003) and for GFR on chromosomes 6 (D6S1040, LOD score of 2.08, empirical P = 0.0001) and 8 (D8S256, LOD score of 1.80, empirical P = 0.0001). Several of these results are replications of significant findings from other genome scans. CONCLUSION: A genome-wide scan for serum creatinine, creatinine clearance, and GFR in a West African sample showed linkage regions that may harbor genes influencing variation in these phenotypes. Potential candidate genes in these regions that have been implicated in diabetic nephropathy and/or renal damage in models of hypertension include CYBA (or P22PHOX) (16q24), NOX1 (10q22), and NOX3 (6q25.1-q26).

Genome-wide scans for diabetic nephropathy and albuminuria in multiethnic populations: the family investigation of nephropathy and diabetes (FIND).

SK Iyengar — 2007-06-23T02:16:50-00:00

Diabetes, Vol. 56, No. 6. (June 2007), pp. 1577-1585.

The Family Investigation of Nephropathy and Diabetes (FIND) was initiated to map genes underlying susceptibility to diabetic nephropathy. A total of 11 centers participated under a single collection protocol to recruit large numbers of diabetic sibling pairs concordant and discordant for diabetic nephropathy. We report the findings from the first-phase genetic analyses in 1,227 participants from 378 pedigrees of European-American, African-American, Mexican-American, and American Indian descent recruited from eight centers. Model-free linkage analyses, using a dichotomous definition for diabetic nephropathy in 397 sibling pairs, as well as the quantitative trait urinary albumin-to-creatinine ratio (ACR), were performed using the Haseman-Elston linkage test on 404 microsatellite markers. The strongest evidence of linkage to the diabetic nephropathy trait was on chromosomes 7q21.3, 10p15.3, 14q23.1, and 18q22.3. In ACR (883 diabetic sibling pairs), the strongest linkage signals were on chromosomes 2q14.1, 7q21.1, and 15q26.3. These results confirm regions of linkage to diabetic nephropathy on chromosomes 7q, 10p, and 18q from prior reports, making it important that genes underlying these peaks be evaluated for their contribution to nephropathy susceptibility. Large family collections consisting of multiple members with diabetes and advanced nephropathy are likely to accelerate the identification of genes causing diabetic nephropathy, a life-threatening complication of diabetes.