Testing for genetic heterogeneity in the genome search meta-analysis method

Methods for combining multiple genome-wide linkage studies

Cardiovascular disease, metabolic syndrome, schizophrenia, diabetes, bipolar disorder, and autism are a few of the numerous complex diseases for which researchers are trying to decipher the genetic composition. One interest of geneticists is to determine the quantitative trait loci (QTLs) that underlie the genetic portion of these diseases and their risk factors. The difficulty for researchers is that the QTLs underlying these diseases are likely to have small to medium effects which will necessitate having large studies in order to have adequate power. Combining information across multiple studies provides a way for researchers to potentially increase power while making the most of existing studies.Here, we will explore some of the methods that are currently being used by geneticists to combine information across multiple genome-wide linkage studies. There are two main types of meta-analyses: (1) those that yield a measure of significance, such as Fisher's p-value method along with its extensions/modifications and the genome search meta-analysis (GSMA) method, and (2) those that yield a measure of a common effect size and the corresponding standard error, such as model-based methods and Bayesian methods. Some of these methods allow for the assessment of heterogeneity. This chapter will conclude with a recommendation for usage.

Meta-analysis of 32 genome-wide linkage studies of schizophrenia

A genome scan meta-analysis (GSMA) was carried out on 32 independent genome-wide linkage scan analyses that included 3255 pedigrees with 7413 genotyped cases affected with schizophrenia (SCZ) or related disorders. The primary GSMA divided the autosomes into 120 bins, rank-ordered the bins within each study according to the most positive linkage result in each bin, summed these ranks (weighted for study size) for each bin across studies and determined the empirical probability of a given summed rank (P(SR)) by simulation. Suggestive evidence for linkage was observed in two single bins, on chromosomes 5q (142-168 Mb) and 2q (103-134 Mb). Genome-wide evidence for linkage was detected on chromosome 2q (119-152 Mb) when bin boundaries were shifted to the middle of the previous bins. The primary analysis met empirical criteria for 'aggregate' genome-wide significance, indicating that some or all of 10 bins are likely to contain loci linked to SCZ, including regions of chromosomes 1, 2q, 3q, 4q, 5q, 8p and 10q. In a secondary analysis of 22 studies of European-ancestry samples, suggestive evidence for linkage was observed on chromosome 8p (16-33 Mb). Although the newer genome-wide association methodology has greater power to detect weak associations to single common DNA sequence variants, linkage analysis can detect diverse genetic effects that segregate in families, including multiple rare variants within one locus or several weakly associated loci in the same region. Therefore, the regions supported by this meta-analysis deserve close attention in future studies.

Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder

Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.

Mapping a gene for rheumatoid arthritis on chromosome 18q21

Although single chi-square analysis of the North American Rheumatoid Arthritis Consortium (NARAC) data identifies many single-nucleotide polymorphisms (SNPs) with p-values less than 0.05, none remain significant after Bonferroni correction. In contrast, CHROMSCAN evades heavy Bonferroni correction and auto-correlation between SNPs by using composite likelihood to model association across all markers in a region and permutation to assess significance. Analysis by CHROMSCAN identifies a 36-kb interval that includes the most significant SNP (msSNP) observed in a 10-Mb target suggested by linkage. Unexpectedly, stratification by gender and age of onset shows that association evidence comes almost entirely from females with age of onset less than 40. Combining evidence from a meta-analysis of linkage studies and three subsets of the NARAC data provides significant evidence for a determinant of rheumatoid arthritis in a 36-kb interval and illustrates the principle that estimates of location and its information are more powerful than estimates of p-values alone.

Meta-analysis of genome-wide linkage studies in BMI and obesity

OBJECTIVE

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.

RESEARCH METHODS AND PROCEDURES

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.

RESULTS

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).

CONCLUSION

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.

Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND

The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. RESULTS from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis.

OBJECTIVES

In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1).

RESULTS

Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings.

CONCLUSIONS

The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.

Meta-analysis of genome-wide linkage studies for multiple sclerosis, using an extended GSMA method

Many genome-wide linkage studies in multiple sclerosis (MS) have been performed, but results are disappointing, with linkage confirmed only in the HLA region. We combined results from all available, non-overlapping genome-wide linkage studies in MS using the Genome Search Meta-Analysis method (GSMA). The GSMA is a rank-based analysis, which assesses the strongest evidence for linkage within bins of traditionally 30 cM width on the autosomes and X chromosome. Genome-wide evidence for linkage was confirmed on chromosome 6p (HLA region; P=0.00004). Suggestive evidence for linkage was found on chromosomes 10q (P=0.0077), 18p (P=0.0054) and 20p (P=0.0079). To explore how these results could be affected by bin definition, we analysed the data using different bin widths (20 and 40 cM) and using a shifted 30 cM bin by moving bin boundaries by 15 cM. Consistently significant results were obtained for the 6p region. The regions on 10q and 18p provided suggestive evidence for linkage in some analyses, and, interestingly, a region on 6q, that showed only nominal significance in the original analysis, yielded increased, suggestive significance in two of the additional analyses. These regions may provide targets to focus candidate gene studies or to prioritise results from genome-wide association studies.

Identification of chromosomal regions linked to premature myocardial infarction: a meta-analysis of whole-genome searches

Myocardial infarction (MI) is a complication of coronary artery disease and the leading cause of death in the Western world. MI is considered a distinct phenotype with an increased genetic component for its premature type. MI's exact inheritance pattern is still unknown. Genome searches for identifying susceptibility loci for premature MI produced inconclusive or inconsistent results. Thus, a genome search meta-analysis (GSMA) was applied to available genome search data on premature MI. GSMA is a non-parametric method to identify genetic regions that rank high, on average in terms of linkage statistics across genome searches unweighted or weighted by study size. The significance of each region's average and heterogeneity, unadjusted or adjusted by neighbouring average simulated ranks, was calculated using a Monte Carlo test. The meta-analysis involved five genome searches in Caucasians. Eight regions (6p22.3-6p21.1, 14p13-14q13.1, 13q33.1-13q34, 5p15.33-5p15.1, 8q13.2-8q22.2, 1p36.21-1p35.2, 12q24.31-12q24.33, 8q24.21-8q24.3) were found to have significant average rank by either unweighted or weighted analyses. In addition, region 8q24.21-8q24.3 produced significant low heterogeneity (P (unadjusted)=0.03 and P (adjusted)=0.05). Four regions (6p22.3-6p21.1, 14p13-14q13.1, 8q13.2-8q22.2, 8q24.21-8q24.3) were not identified by the individual studies. The meta-analysis suggests that these four regions should be further investigated for genes that confer susceptibility to MI.

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 (Prank< 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 (PQ< 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 (PQ= 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.