Genome Scan Meta-analysis Research Articles

Combining linkage data sets for meta-analysis and mega-analysis: the GAW15 rheumatoid arthritis data set

We have used the genome-wide marker genotypes from Genetic Analysis Workshop 15 Problem 2 to explore joint evidence for genetic linkage to rheumatoid arthritis across several samples. The data consisted of four high-density genome scans on samples selected for rheumatoid arthritis. We cleaned the data, removed intermarker linkage disequilibrium, and assembled the samples onto a common genetic map using genome sequence positions as a reference for map interpolation. The individual studies were combined first at the genotype level (mega-analysis) prior to a multipoint linkage analysis on the combined sample, and second using the genome scan meta-analysis method after linkage analysis of each sample. The two approaches were compared, and give strong support to the HLA locus on chromosome 6 as a susceptibility locus. Other regions of interest include loci on chromosomes 11, 2, and 12.

Meta‐Analysis of Genome‐wide Linkage Studies in BMI and Obesity

The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1-qter and 12p11.21-q23 (p < 0.01). Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.

Meta-Analysis of 23 Type 2 Diabetes Linkage Studies from the International Type 2 Diabetes Linkage Analysis Consortium

Background: The International Type 2 Diabetes Linkage Analysis Consortium was formed to localize type 2 diabetes predisposing variants based on 23 autosomal linkage scans. Methods: We carried out meta-analysis using the genome scan meta-analysis (GSMA) method which divides the genome into bins of ∼30 cM, ranks the best linkage results in each bin for each sample, and then sums the ranks across samples. We repeated the meta-analysis using 2 cM bins, and/or replacing bin ranks with measures of linkage evidence: bin maximum LOD score or bin minimum p value for bins with p value <0.05 (truncated p value). We also carried out computer simulations to assess the empirical type I error rates of these meta-analysis methods. Results: Our analyses provided modest evidence for type 2 diabetes-predisposing variants on chromosomes 4, 10, and 14 (using LOD scores or truncated p values), or chromosome 10 and 16 (using ranks). Our simulation results suggested that uneven marker density across studies results in substantial variation in empirical type I error rates for all meta-analysis methods, but that 2 cM bins and scores that make more explicit use of linkage evidence, especially the truncated p values, reduce this problem. Conclusion: We identified regions modestly linked with type 2 diabetes by summarizing results from 23 autosomal genome scans.

Meta-analysis of genome-wide linkage studies of systemic lupus erythematosus

A genetic contribution to the development of systemic lupus erythematosus (SLE) is well established. Several genome-wide linkage scans have identified a number of putative susceptibility loci for SLE, some of which have been replicated in independent samples. This study aimed to identify the regions showing the most consistent evidence for linkage by applying the genome scan meta-analysis (GSMA) method. The study identified two genome-wide suggestive regions on 6p21.1-q15 and 20p11-q13.13 (P-value=0.0056 and P-value=0.0044, respectively) and a region with P-value<0.01 on 16p13-q12.2. The region on chromosome 6 contains the human leukocyte antigen cluster, and the chromosome 16 and 20 regions have been replicated in several cohorts. The potential importance of the identified genomic regions are also highlighted. These results, in conjunction with data emerging from dense single nucleotide polymorphism typing of specific regions or future genome-wide association studies will help guide efforts to identify the actual predisposing genetic variation contributing to this complex genetic disease.

Osteoarthritis susceptibility loci defined by genome scan meta-analysis

Genome scans for osteoarthritis (OA) have yielded inconsistent results. The absence of replication of linkage might be due to lack of power of individual studies. A meta-analysis of the published data was performed to assess evidence for linkage of OA across genome scan studies. Three OA whole-genome scans including 893 families with 3,000 affected individuals were used for genome scan meta-analysis (GSMA). A total of 5 bins lie above 95% confidence level (P = 0.05) and 1 bin is above 99% confidence level (P = 0.01) in OA GSMA; bins 7.6 (7q34-7q36.3, Psumrnk = 0.0035), 11.3 (11p12-11q13.4), 6.3 (6p21.1-6q15), 2.8 (2q31.1-2q34) and 15.3 (15q21.3-15q26.1). The highest summed rank was observed at bin 7.6. In conclusion, the OA GSMA has shown chromosome 7q34-7q36.3 with the highest summed rank and four additional loci with significant summed ranks across studies.

Osteoarthritis susceptibility loci defined by genome scan meta-analysis

Genome scans for osteoarthritis (OA) have yielded inconsistent results. The absence of replication of linkage might be due to the lack of power of individual studies. A meta-analysis of published data was performed to assess evidence for linkage of OA across genome scan studies. Three OA whole-genome scans including 893 families with 3,000 affected individuals were used for genome scan meta-analysis (GSMA). A total of five bins lie above 95% confidence level (P=0.05) and one bin is above 99% confidence level (P=0.01) in OA GSMA; bins 7.6 (7q34-7q36.3, P (sumrnk) =0.0035), 11.3 (11p12-11q13.4), 6.3 (6p21.1-6q15), 2.8 (2q31.1-2q34) and 15.3 (15q21.3-15q26.1). The highest summed bin was bins 7.6. In conclusion, the OA GSMA has shown that chromosome 7q34-7q36.3 have the highest summed rank and four additional loci with significant summed ranks across studies.

Meta-analysis of genome-wide linkage studies for bone mineral density

Genome-wide linkage studies have shown several chromosome loci that may harbor genes that regulate bone mineral density (BMD), but results have been inconsistent. A meta-analysis was performed to assess evidence for linkage of BMD across whole genome scan studies. Eleven whole-genome scans of BMD or osteoporosis containing 3,097 families with 12,685 individuals were included in this genome scan meta-analysis (GSMA). For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted and summed across all studies. The summed rank for each bin was assessed empirically for significance using permutation methods. A total of seven bins lie above the 95% confidence level (P=0.05) and one bin was above the 99% confidence level (P=0.01) in the GSMA of eleven linkage studies: bins 16.1 (16pter-16p12.3, Psumrnk <0.01), 3.3 (3p22.2-3p14.1), 1.1 (1pter-1p36.22), 18.2 (18p11.23-18q12.2), 6.3 (6p21.1-6q15), 20.1 (20pter-20p12.3), and 18.1 (18pter-18p11.23). GSMA was performed with seven studies with linkage scores of LOD >1-1.85 for sensitivity test, confirming the linkage on chromosome 16p and 3p and revealing evidence of new linkage in bins 10.2 (10p14-10q11.21) and 22.2 (22q12.3-22pter). In conclusion, the meta-analysis of whole-genome linkage studies of BMD has shown chromosome 16pter-16p12.3 to have the greatest evidence of linkage as well as revealing evidence of linkage in chromosomes 1p, 3p, 6, 10, 18, 20p, and 22q across studies. This data may provide a basis with which to carry out targeted linkage and candidate gene studies particularly in these regions.

An Updated Meta-Analysis of Genome Scans for Hypertension and Blood Pressure in the NHLBI Family Blood Pressure Program (FBPP)

A meta-analysis of the results from a multicenter genome-wide linkage study for hypertension and blood pressure (BP) based on an initial sample of 6,245 individuals was published in 2003. We report here a combined linkage analysis of hypertension and BP using the complete Family Blood Pressure Program (FBPP) dataset, which includes a total of 12,028 genotyped individuals. Genome-wide linkage analyses for hypertension and BP were first performed in each of the studied ethnic group within each network and the results were combined with a meta-analysis using a modified Fisher's method of combining P values. Our meta-analysis of genome scans for the latest FBPP dataset reveals suggestive linkage for hypertension and BP at several regions on the human genome. Strong evidence for linkage at two of these regions, 2p14 and 3p14.1, have also been published in previous meta-analyses, making them good candidate locations for susceptibility variants.

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations.

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6–7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160–167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287–294 cM, including D2), chromosome 5 (p-value < 0.001 for 0–7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7–14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.

Genome scan meta-analysis of rheumatoid arthritis

Genome scans for rheumatoid arthritis (RA) have yielded inconsistent results. The absence of replication of linkage might be due to lack of power of individual studies. We performed a genome scan meta-analysis of published data to increase statistical power and to assess evidence for linkage of RA across genome scan studies. Four RA whole-genome scans containing 767 families with 964 sibling pairs were included for the genome scan meta-analysis (GSMA). The GSMA method was applied to pool the results obtained from four genome scans. For each study, 120 genomic bins of approximately 30 centimorgans were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted and summed across all studies. The summed rank for each bin was assessed empirically for significance using permutation methods. A total of nine bins lay above the 95% confidence level (P=0.05) and four bins were above the 99% confidence level (P=0.01) in the RA GSMA, suggesting that these bins contain RA-linked loci: bins 6.2, 6.4, 8.1, 18.3, 12.3, 12.2, 1.5, 6.3 and 16.2. The strongest evidence for linkage occurred on chromosome 6p22.3-6p21.1 (bin 6.2), containing the HLA region (P(sumrnk)=0.0000008). This RA GSMA confirmed the evidence for HLA loci as the greatest susceptibility factor to RA and showed evidence for linkage at non-HLA loci, such as chromosomes 1p, 6, 8p, 12, 16 and 18q, across studies. These data may provide a basis to carry out targeted linkage and candidate gene studies, particularly in the regions.

Combined Analysis from Eleven Linkage Studies of Bipolar Disorder Provides Strong Evidence of Susceptibility Loci on Chromosomes 6q and 8q

Several independent studies and meta-analyses aimed at identifying genomic regions linked to bipolar disorder (BP) have failed to find clear and consistent evidence of linkage regions. Our hypothesis is that combining the original genotype data provides benefits of increased power and control over sources of heterogeneity that outweigh the difficulty and potential pitfalls of the implementation. We conducted a combined analysis using the original genotype data from 11 BP genomewide linkage scans comprising 5,179 individuals from 1,067 families. Heterogeneity among studies was minimized in our analyses by using uniform methods of analysis and a common, standardized marker map and was assessed using novel methods developed for meta-analysis of genome scans. To date, this collaboration is the largest and most comprehensive analysis of linkage samples involving a psychiatric disorder. We demonstrate that combining original genome-scan data is a powerful approach for the elucidation of linkage regions underlying complex disease. Our results establish genomewide significant linkage to BP on chromosomes 6q and 8q, which provides solid information to guide future gene-finding efforts that rely on fine-mapping and association approaches.

Meta-analysis of genome scans of age-related macular degeneration

A genetic contribution to the development of age-related macular degeneration (AMD) is well established. Several genome-wide linkage studies have identified a number of putative susceptibility loci for AMD but only a few of these regions have been replicated in independent studies. Here, we perform a meta-analysis of six AMD genome screens using the genome-scan meta-analysis method, which allows linkage results from several studies to be combined, providing greater power to identify regions that show only weak evidence for linkage in individual studies. Results from non-parametric analysis for a broad AMD clinical phenotype (including two studies with quantitative traits) were extracted. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted according to study size and summed across all studies; the summed rank (SR) for each bin was assessed empirically for significance using permutation methods. A high SR indicates a region with consistent evidence for linkage across studies. The strongest evidence for an AMD susceptibility locus was found on chromosome 10q26 where genome-wide significant linkage was observed (P=0.00025). Several other regions met the empirical significance criteria for bins likely to contain linked loci including adjacent pairs of bins on chromosomes 1q, 2p, 3p and 16. Several of the regions identified here showed only weak evidence for linkage in the individual studies. These results will help prioritize regions for future positional and functional candidate gene studies in AMD.

HEGESMA: genome search meta-analysis and heterogeneity testing

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. http://biomath.med.uth.gr.

Genetics and genomics of depression

Depressive disorders are among the most common psychiatric diseases, with prevalence estimates ranging from 5% to a maximum of 20%. Despite their high prevalence and socioeconomic impact, little is known about their etiology. Heritability estimates demonstrate up to a 50% genetic component based on family aggregation and contrasting monozygotic and dizygotic twin studies. The low relative risk to siblings ( λsib <1.5) makes the search for their genetic determinants very tedious. Gene-environment interaction has been recognized for a long time in the pathophysiology of depression, and its best biological substratum at present is represented by the serotonin transporter (5-HTT) gene, where several copies of its short allele culminate in depression and suicide in response to lifelong stress events. Many total genome scans have been performed with variable results, the most authoritative being the one of Utah pedigrees with a strong family history of major depression. It identified a locus on chromosome 12 encompassing a gene cluster and sex-specific predisposition. Nevertheless, recent genome scan meta-analysis yielded somewhat disappointing conclusions with a relatively low significance for quantitative trait loci on chromosomes 9, 10, 14, and 18. Studies on animal models have contributed to the chromosomal mapping of many behavioral traits, including anxiety, the stress response, and depression. Although F2 crosses constitute a classical approach, novel models of recombinant inbred strain and recombinant congenic strain animals allow for a rapid initial localization of any traits. This type of analysis has led us to uncover significant loci for the stress response and anxiety in rats and mice.

Meta-analysis in psychiatric genetics

The article reviews literature on methods for meta-analysis of genetic linkage and association studies, and summarizes and comments on specific meta-analysis findings for psychiatric disorders. The Genome Scan Meta-Analysis and Multiple Scan Probability methods assess the evidence for linkage across studies. Multiple Scan Probability analysis suggested linkage of two chromosomal regions (13q and 22q) to schizophrenia and bipolar disorder, whereas Genome Scan Meta-Analysis on a larger sample identified at least 10 schizophrenia linkage regions, but none for bipolar disorder. Meta-analyses of pooled ORs support association of schizophrenia to the Ser311Cys polymorphism in DRD2 and the T102C polymorphism in HTR2A, and of attention deficit hyperactivity disorder to the 48-bp repeat in DRD4. The 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4) may contribute to the risk of bipolar disorder, suicidal behavior, and neuroticism, but association to the lifetime risk of major depression has not been shown. Meta-analyses support linkage of schizophrenia to regions where replicable associations to candidate genes have been identified through positional cloning methods. There are additional supported regions where susceptibility genes are likely to be identified. Linkage meta-analysis has had less clear success for bipolar disorder based on a smaller dataset. Meta-analysis can guide the prioritization of regions for study, but proof of association requires biological confirmation of hypotheses about gene actions. Elucidation of causal mechanisms will require more comprehensive study of sequence variation in candidate genes, better statistical and meta-analytic methods to take all variation into account, and biological strategies for testing etiologic hypotheses.

A note on generalized Genome Scan Meta-Analysis statistics

BackgroundWise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.ResultsWe provide an Edgeworth approximation to the null distribution of the weighted GSMA statistic, and, we examine the limiting distribution of the GSMA statistics under the order statistic formulation, and quantify the relevance of the pairwise correlations of the GSMA statistics across different bins on this limiting distribution. We also remark on aggregate criteria and multiple testing for determining significance of GSMA results.ConclusionTheoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic.

Genome scan meta-analysis for hypertension

Genome scans for hypertension have yielded inconsistent results. The non-replication of significant or suggestive linkage might be due to lack of power of individual studies. Here, we conducted a genome scan meta-analysis for hypertension in an attempt to increase statistical power and to enhance evidence of linkage. A newly developed Genome Search Meta-analysis (GSMA) method was applied to pool the results obtained from six scans reported in five papers. Our analysis did not find any regions with genome-wide significant linkage to hypertension. We did identify several regions with suggestive linkage, including 2p, 5q, 6q, 8p, 9p, 9q, and 11q. It seems that no region has a uniformly large impact on hypertension and that susceptibility genes for hypertension may be very difficult to detect.

Support for involvement of neuregulin 1 in schizophrenia pathophysiology

Schizophrenia is a common, multigenic psychiatric disorder. Linkage studies, including a recent meta-analysis of genome scans, have repeatedly implicated chromosome 8p12-p23.1 in schizophrenia susceptibility. More recently, significant association with a candidate gene on 8p12, neuregulin 1 (NRG1), has been reported in several European and Chinese samples. We investigated NRG1 for association in schizophrenia patients of Portuguese descent to determine whether this gene is a risk factor in this population. We tested NRG1 markers and haplotypes for association in 111 parent-proband trios, 321 unrelated cases, and 242 control individuals. Associations were found with a haplotype that overlaps the risk haplotype originally reported in the Icelandic population ("Hap(ICE)"), and two haplotypes located in the 3' end of NRG1 (all P<0.05). However, association was not detected with Hap(ICE) itself. Comparison of NRG1 transcript expression in peripheral leukocytes from schizophrenia patients and unaffected siblings identified 3.8-fold higher levels of the SMDF variant in patients (P=0.039). Significant positive correlations (P<0.001) were found between SMDF and HRG-beta 2 expression and between HRG-gamma and ndf43 expression, suggesting common transcriptional regulation of NRG1 variants. In summary, our results suggest that haplotypes across NRG1 and multiple NRG1 variants are involved in schizophrenia.

A note on the genome scan meta-analysis statistic.

Wise and colleagues (Ann. Hum. Genet. (1999) 63: 263-72) introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. We provide an alternative derivation of the null distribution of the GSMA statistic, with extensions, and we suggest approximations to the distribution of the GSMA statistic that may be useful in applications.

Inflammatory bowel disease susceptibility loci defined by genome scan meta-analysis of 1952 affected relative pairs

Crohn's disease and ulcerative colitis (the inflammatory bowel diseases) have a strong genetic component. Although over 20 putative susceptibility loci have been identified by individual genome scans, the majority of these loci have not been replicated. Many individual studies are at the lower limit of acceptable power for complex disease linkage analysis. Genome scan meta-analysis (GSMA), by use of sample sizes an order of magnitude greater than individual linkage studies, has increased power to detect novel loci, may confirm or refute regions detected in smaller individual studies, and enables regions to be prioritized for further gene identification efforts. Genome scan data (markers, significance scores) were obtained from 10 separate studies and meta-analysis was performed using the GSMA method. These studies comprised 1952 inflammatory bowel disease, 1068 Crohn's disease and 457 ulcerative colitis affected relative pairs. Study results were divided into 34 cM chromosomal bins, ranked, weighted by study size, summed across studies and bin-by-bin significance obtained by simulation. A region on chromosome 6p (containing the HLA) met genome wide significance for inflammatory bowel disease. Loci meeting suggestive significance for inflammatory bowel disease were 2q, 3q, 5q, 7q and 16 (NOD2/CARD15 region); Crohn's disease, 2q, 3q, 6p, 16 (NOD2/CARD15 region), 17q, 19p; and ulcerative colitis, 2q. Clustering of adjacent bins was observed for chromosomes 6p, 16, 19p. The meta-analysis has identified novel loci and prioritized genomic regions for further gene identification studies.