GSMA: software implementation of the genome search meta-analysis method

Meta-analysis can be used to pool results of genome-wide linkage scans. This is of great value in complex diseases, where replication of linked regions occurs infrequently. The genome search meta-analysis (GSMA) method is widely used for this analysis, and a computer program is now available to implement the GSMA.     

Heterogeneity-based genome search meta-analysis for preeclampsia

Preeclampsia is a pregnancy-related disorder that causes maternal and fetal morbidity and mortality. Its exact inheritance pattern is still unknown, and genome searches for identifying susceptibility loci for preeclampsia have thus far produced inconclusive or inconsistent results. We performed a heterogeneity-based genome search meta-analysis (HEGESMA) that synthesized the available genome scan data on preeclampsia. HEGESMA identifies genetic regions (bins) that rank highly on average in terms of linkage statistics across genome scans (searches). The significance of each bin’s average rank and heterogeneity across scans was calculated using Monte Carlo tests. The meta-analysis involved four genome-scans on general preeclampsia and five scans on severe preeclampsia. In general preeclampsia, 13 bins had significantly high average rank (P _rank < 0.05) by either unweighted or weighted analyses, while four of them (2p11.2–2q21.1, 9q21.32–9q31.2, 2p15–2p11.2, 2q32.1–2q35) were formally significant by both analyses. Heterogeneity of bin 2.8 (2q32.1–2q35) was significantly low in both unweighted and weighted analysis (P _Q < 0.01). In severe preeclampsia, 10 bins had significantly high average rank by either unweighted or weighted analyses and five of them (3q11.1–3q21.2, 2q37.1–2q37.3, 18p11.32–18p11.22, 2p15–2p11.2, 7q34–7q36.3) were significant by both analyses. Bin 2q37.1–2q37.3 showed marginal low heterogeneity in unweighted and weighted analysis (P _Q = 0.06). Results should be interpreted with caution as the p values were modest. Further investigation of these regions by genotyping with additional markers and families may help to direct the identification of candidate genes for preeclampsia.     

The meta-analysis of genome-wide association studies

The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review distinguishes between the use of meta-analysis to validate previously reported genetic associations and its use for gene discovery, and advocates viewing gene discovery as an exploratory screen that requires independent replication instead of treating it as the application of hundreds of thousands of statistical tests. The review considers the use of fixed and random effects meta-analyses, the investigation of between-study heterogeneity, adjustment for confounding, assessing the combined evidence and genomic control, and comments on alternative approaches that have been used in the literature.     

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

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.     

GLIDERS - A web-based search engine for genome-wide linkage disequilibrium between HapMap SNPs

Background  

A number of tools for the examination of linkage disequilibrium (LD) patterns between nearby alleles exist, but none are available for quickly and easily investigating LD at longer ranges (>500 kb). We have developed a web-based query tool (GLIDERS: Genome-wide LInkage DisEquilibrium Repository and Search engine) that enables the retrieval of pairwise associations with r² ≥ 0.3 across the human genome for any SNP genotyped within HapMap phase 2 and 3, regardless of distance between the markers.     

HEGESMA: genome search meta-analysis and heterogeneity testing

SUMMARY: Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and weighted analysis, with the weights for each study as specified by the user. Furthermore, the program performs heterogeneity analyses restricted to the bins with similar average ranks. AVAILABILITY: http://biomath.med.uth.gr.     

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association

For the identification of susceptibility loci in complex diseases the choice of the target phenotype is very important. We compared results of genome-wide searches for linkage or for association related to three phenotypes for alcohol use disorder. These are a behavioral score BQ, based on a 12-item questionnaire about drinking behavior and the subject's report of drinking-related health problems, and ERP pattern and ERP magnitude, both derived from the eyes closed resting ERP measures to quantify brain activity. Overall, we were able to identify 11 candidate regions for linkage. Only two regions were found to be related to both BQ and one of the ERP phenotypes. The genome-wide search for association using single-nucleotide polymorphisms did not yield interesting leads.     

The impact of imputation on meta-analysis of genome-wide association studies

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ～25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary.     

RS-SNP: a random-set method for genome-wide association studies

Background

Common carp is one of the most important aquaculture teleost fish in the world. Common carp and other closely related Cyprinidae species provide over 30% aquaculture production in the world. However, common carp genomic resources are still relatively underdeveloped. BAC end sequences (BES) are important resources for genome research on BAC-anchored genetic marker development, linkage map and physical map integration, and whole genome sequence assembling and scaffolding.

Result

To develop such valuable resources in common carp (Cyprinus carpio), a total of 40,224 BAC clones were sequenced on both ends, generating 65,720 clean BES with an average read length of 647 bp after sequence processing, representing 42,522,168 bp or 2.5% of common carp genome. The first survey of common carp genome was conducted with various bioinformatics tools. The common carp genome contains over 17.3% of repetitive elements with GC content of 36.8% and 518 transposon ORFs. To identify and develop BAC-anchored microsatellite markers, a total of 13,581 microsatellites were detected from 10,355 BES. The coding region of 7,127 genes were recognized from 9,443 BES on 7,453 BACs, with 1,990 BACs have genes on both ends. To evaluate the similarity to the genome of closely related zebrafish, BES of common carp were aligned against zebrafish genome. A total of 39,335 BES of common carp have conserved homologs on zebrafish genome which demonstrated the high similarity between zebrafish and common carp genomes, indicating the feasibility of comparative mapping between zebrafish and common carp once we have physical map of common carp.

Conclusion

BAC end sequences are great resources for the first genome wide survey of common carp. The repetitive DNA was estimated to be approximate 28% of common carp genome, indicating the higher complexity of the genome. Comparative analysis had mapped around 40,000 BES to zebrafish genome and established over 3,100 microsyntenies, covering over 50% of the zebrafish genome. BES of common carp are tremendous tools for comparative mapping between the two closely related species, zebrafish and common carp, which should facilitate both structural and functional genome analysis in common carp.     

Critical reasoning on causal inference in genome-wide linkage and association studies

Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.     

A robust method for testing association in genome-wide association studies

In genetic association studies, due to the varying underlying genetic models, no single statistical test can be the most powerful test under all situations. Current studies show that if the underlying genetic models are known, trend-based tests, which outperform the classical Pearson χ2 test, can be constructed. However, when the underlying genetic models are unknown, the χ2 test is usually more robust than trend-based tests. In this paper, we propose a new association test based on a generalized genetic model, namely the generalized order-restricted relative risks model. Through a Monte Carlo simulation study, we show that the proposed association test is generally more powerful than the χ2 test, and more robust than those trend-based tests. The proposed methodologies are also illustrated by some real SNP datasets.     

GWAMA: software for genome-wide association meta-analysis

Background  

Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies.     

Selection strategies for linkage studies using twins.

Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.     

Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.     

FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium

Background  

Human genome contains millions of common single nucleotide polymorphisms (SNPs) and these SNPs play an important role in understanding the association between genetic variations and human diseases. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), thus it is not necessary to genotype all SNPs for association study. Many algorithms have been developed to find a small subset of SNPs called tag SNPs that are sufficient to infer all the other SNPs. Algorithms based on the r ² LD statistic have gained popularity because r ² is directly related to statistical power to detect disease associations. Most of existing r ² based algorithms use pairwise LD. Recent studies show that multi-marker LD can help further reduce the number of tag SNPs. However, existing tag SNP selection algorithms based on multi-marker LD are both time-consuming and memory-consuming. They cannot work on chromosomes containing more than 100 k SNPs using length-3 tagging rules.     

An efficient comprehensive search algorithm for tagSNP selection using linkage disequilibrium criteria

MOTIVATION: Selecting SNP markers for genome-wide association studies is an important and challenging task. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously maximizing the information content provided by selected markers. RESULTS: We devised an improved algorithm for tagSNP selection using the pairwise r(2) criterion. We first break down large marker sets into disjoint pieces, where more exhaustive searches can replace the greedy algorithm for tagSNP selection. These exhaustive searches lead to smaller tagSNP sets being generated. In addition, our method evaluates multiple solutions that are equivalent according to the linkage disequilibrium criteria to accommodate additional constraints. Its performance was assessed using HapMap data. AVAILABILITY: A computer program named FESTA has been developed based on this algorithm. The program is freely available and can be downloaded at http://www.sph.umich.edu/csg/qin/FESTA/     

Applying novel genome-wide linkage strategies to search for loci influencing type 2 diabetes and adult height in American Samoa

Type 2 diabetes mellitus (T2DM) is a common complex phenotype that by the year 2010 is predicted to affect 221 million people globally. In the present study we performed a genome-wide linkage scan using the allele-sharing statistic Sall implemented in Allegro and a novel two-dimensional genome-wide strategy implemented in Merloc that searches for pairwise interaction between genetic markers located on different chromosomes linked to T2DM. In addition, we used a robust score statistic from the newly developed QTL-ALL software to search for linkage to variation in adult height. The strategies were applied to a study sample consisting of 238 sib-pairs affected with T2DM from American Samoa. We did not detect any genome-wide significant susceptibility loci for T2DM. However, our two-dimensional linkage investigation detected several loci pairs of interest, including 11q22 and 21q21, 9q21 and 11q22, 1p22-p21 and 4p15, and 4p15 and 15q11-q14, with a two-loci maximum LOD score (MLS) greater than 2.00. Most detected individual loci have previously been identified as susceptibility loci for diabetes-related traits. Our two-dimensional linkage results may facilitate the selection of potential candidate genes and molecular pathways for further diabetes studies because these results, besides providing candidate loci, also demonstrate that polygenic effects may play an important role in T2DM. Linkage was detected (p value of 0.005) for variation in adult height on chromosome 9q31, which was reported previously in other populations. Our finding suggests that the 9q31 region may be a strong quantitative trait locus for adult height, which is likely to be of importance across populations.     

A genome-wide search for genes predisposing to familial psoriasis by using a stratification approach

We have performed a genome scan, using markers spaced by 10 cM, in the search for psoriasis-susceptibility loci. The family material of 134 affected sibling pairs was ascertained on the basis of a population genetic study in which 65% of the probands had two healthy parents. Genotyping results were analyzed for non-random excessive allele-sharing between sib pairs by using GENEHUNTER ver 1.1. A stratification approach was applied to increase the homogeneity of the material by means of an operational definition of joint complaints among affected individuals. Significant linkage to the human leukocyte antigen region on chromosome 6p in a cohort including 42 families without joint complaints (nonparametric linkage score of 2.83, P=0.002) strongly supported the validity of this operational definition as it replicated results from an earlier linkage report with similar stratification criteria. New candidate regions on chromosomes 3 and 15 were identified. The highest non-parametric linkage values in this study, 2.96 (P=0.0017) and 2.89 (P=0.0020), were reached on chromosome 15 in a subgroup with joint complaints and on chromosome 3 in a subgroup without joint complaints. In addition, confirmation of previously reported loci was established on chromosomes 4q, 6p, and 17q. This study indicates that distinct disease loci might be involved in psoriasis etiology for various phenotypes.