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(Arteriosclerosis, Thrombosis, and Vascular Biology. 2005;25:1541.)
© 2005 American Heart Association, Inc.

Special Article

Complex Trait Locus Linkage Mapping in Atherosclerosis

Time to Take a Step Back Before Moving Forward?

Rebecca L. Pollex; Robert A. Hegele

From the Robarts Research Institute, London, Ontario, Canada.

Correspondence to Robert A. Hegele, MD, FRCPC, FACP, FAHA, 406-100 Perth Dr, London, Ontario, Canada N6A 5K8. E-mail hegele{at}robarts.ca

	Introduction

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
Ever since the initial proposal to use polymorphic DNA markers to map genetic diseases,¹ linkage analysis (also called "positional cloning") has been used successfully to find the gene defects for hundreds of monogenic Mendelian traits.² Because monogenic diseases can serve as important models for understanding pathogenesis, especially if they point to novel biochemical and physiological pathways, linkage analysis has revolutionized biomedicine. A prime example of the success of linkage analysis in atherosclerosis was the discovery that ABCA1 was the causative gene for Tangier disease,³ which has created an exciting and thriving new subfield of research. The notable success in localizing the molecular defects in monogenic disorders follows from the simple disease pathogenesis model: a single mutated disease gene is necessary and sufficient to cause the observed trait. A recent search of the Online Mendelian Inheritance in Man (OMIM) human genetic disease database roughly quantifies the extent of this success: by entering the keywords "linkage analysis" AND "autosomal," 900 individual entries were returned. And this likely underestimates the number of monogenic diseases for which the molecular genetic basis was solved by linkage analysis.

	Applying Linkage Analysis to Complex Traits

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
Buoyed by the successful application of linkage analysis to discover the genetic basis of monogenic diseases, many investigators over the last decade turned their attention to the logical next frontier for human disease gene mapping: susceptibility genes for common complex traits. Clearly, this is an extremely worthy pursuit. Complex traits, such as the end points of common atherosclerosis, affect more people than monogenic diseases and result in an enormous burden of morbidity and mortality. Even incremental success in identifying meaningful genetic mutations for atherosclerosis would represent a major contribution, especially if new molecular targets could be identified.

However, the impediments to the genetic mapping of complex disease traits have been formidable. The goal of a mapping study is a significant linkage signal or "peak" (roughly a logarithm of odds [LOD] score of 3.0 [P=0.05] plotted on the ordinate when position along the chromosome is plotted on the abscissa). But susceptibility to complex traits is heterogeneous, involving multiple genetic and environmental risk factors, acting either independently or in concert. This complexity flattens and widens the linkage "peaks." Other confounding mechanisms, including variable penetrance, genomic imprinting, the effects of genetic background and parental allele origin, gene–gene and gene–environment interactions, and more recently, large-scale copy variations, further thwart the detection of genetic signals over background biological noise. The effects of such difficulties can be seen in the variable outcomes of genome-wide screens for cardiovascular disease susceptibility genes, from the discovery of no regions of significant linkage⁴ to suggestive broad linkage peaks^5,6 to significant linkage (although usually with no identification of the true genetic cause).^7,8

Many strategies have been proposed to circumvent the roadblocks to the mapping of complex traits. For instance, some complex disease phenotypes, such as familial combined hyperlipidemia (FCHL), are defined by threshold values applied to quantitative traits, such as serum concentrations of triglycerides, together with total or low-density lipoprotein cholesterol. Modified linkage strategies can be used to evaluate relationships between genetic markers and either the means or variances of quantitative traits, rather than the related discrete traits.^9,10 In these analyses, the statistically linked chromosomal regions are called quantitative trait loci (QTLs). Attempts to map QTLs using genome-wide linkage analysis have been referred to as "casting a wide-mesh net across the entire sea of genetic information."¹¹ And although you cannot catch fish without casting a net, has the catch been worth the effort?

	Determining the Yield From Linkage Analysis

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
The QTL approach has been successful at "catching" linkage peaks: a recent PubMed search using the terms "human" and either "quantitative trait locus" or "QTL" retrieved 1200 references, mostly from within the last 5 years. But the significant linkage peak is merely an initial step in the process; it specifies positional candidates for functional DNA changes that should ultimately explain the linkage. How often have significant QTLs led to discovery of a causative molecular genetic basis? A recent search of the OMIM human genetic disease database using the keywords "linkage analysis" AND "complex trait" returned only 20 individual entries. Has the time perhaps come to critically re-evaluate the utility and efficacy of this broad-based, resource-intensive approach for finding disease genes in atherosclerosis?

	FCHL: A Case Report

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
The history of linkage analysis applied to FCHL is illustrative. FHCL is a common genetically determined trait that is associated with a high risk of atherosclerosis. The complex genetic nature of FCHL was shown in 1973.¹² After seeking causative genes using candidate gene association studies in the 1980s, a landmark article in 1992 reported linkage of FCHL to the APOA1/C3/A4 gene cluster on chromosome 11.¹³ Because this was a relatively small region genetically, only a short time was felt to be required before sequence analysis would reveal the actual DNA variant that was the molecular basis of the linkage with FCHL. However, 13 years later, there is little compelling evidence for a functional DNA change that explains the linkage of FCHL to chromosome 11. Recently, interest in the APOA1/C3/A4 locus has been re-energized by the bioinformatic discovery of another closely linked gene, APOA5, which might harbor variants that contribute to FHCL or its component phenotypes.¹⁴ Furthermore, the list of linked markers for the discrete FHCL traits and its related quantitative traits has meanwhile grown, with evidence for linkage between 24 additional regions on 13 different chromosomes (Table). As with the chromosome 11 loci, most of these have not yielded candidate mutations or variants that would mechanistically explain the linkage. One important exception might be the chromosome 1 FCHL locus, with variants in TNFRSF1B having been reported,¹⁵ and recently variants in USF gene.¹⁶

View this table: [in this window] [in a new window]   Summary of Results From FCHL Linkage Studies, 1998 to Present

	Complex Trait Mapping: How to Judge Success?

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
Other examples of complex trait genes accepted as being causative or rendering susceptibility, but with as yet incomplete mechanistic understanding, are PDE4D and ALOX5AP in atherosclerosis,^17,18 and calpain-2 in type 2 diabetes.¹⁹ But what of peaks that have not panned out? One example of collecting linkage peaks that have not yet translated into causative variants is seen with linkage mapping for obesity-related phenotypes. To date, all chromosomes, except chromosome 21, have been implicated as possible locations for obesity genes, harboring 296 linkage peaks.²⁰ Yet, to the best of our knowledge, mutations in just 1 gene identified this way, GAD2, appear to underlie susceptibility for common obesity.²¹ Therefore, without independent evidence for causation, should more circumspection be applied when considering the potential value of adding more peaks to the linkage map given their record of leading to isolation of causative or susceptibility mutations?

Yet, even with evidence for causation, success is not necessarily assured, as shown in the search for a gene underlying coronary artery disease and myocardial infarction.²² Here, the researchers appeared to follow all the right steps: the identification of a region with a significant LOD score; the discovery of a 21-bp deletion in exon 11 of MEF2A; a strong candidate gene within the region, which cosegregated with affected family members; and finally, the demonstration of functional in vitro evidence of altered transactivation ability for the MEF2A mutant protein.²³ Yet, despite all this evidence, the validity of this claim has been brought recently into question because the 21-bp deletion has been also observed in multiple unaffected individuals.²⁴ This example underscores the difficulties of complex gene discovery, stressing the importance of replication and multiplicity of mutations, samples, model systems, and methods to validate preliminary observations.

	Conclusions

Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 
Although we have focused on linkage studies, it is clear that other genetic approaches, such as association analysis, also have limitations.²⁵ With due respect, we wonder whether it is time to reconsider the appropriateness of reporting "suggestive" linkage peaks for complex traits derived from genome-wide scans in small samples. We certainly do not propose that linkage analysis should be retired as a strategy to identify genes in complex traits. Having performed such studies ourselves, we appreciate that they can be logistically challenging: time- and resource-intensive studies that require integrated team efforts involving creative, skilled, and innovative scientists. There can be no argument that specifying a linkage peak is a necessary first step toward finding the causative molecular etiology. But on its own, is a "suggestive" chromosomal position linked to a complex atherosclerosis-related trait sufficient to report to a general readership? We suggest that in the future, such studies should not be published in Arteriosclerosis, Thrombosis, and Vascular Biology.

Perhaps the results of genome-wide linkage analyses for complex traits could be accompanied more routinely at an earlier stage by complementary evidence to increase confidence that a molecular etiology will be disclosed ultimately. Concurrent reporting of replications in independent samples would increase the interest in and importance of an individual suggestive or even significant QTL. Also, linkage research should build on existing cooperative efforts between labs, as has been done for the Family Blood Pressure Program, will which allow for earlier meta-analyses with larger sample sizes and increased power. New online tools will aid in these endeavors for multicenter trial database development²⁶ and analysis.²⁷ Furthermore, the wide availability of complete human genome data should enable immediate prioritization among all positional candidate genes through the routine use of in silico methods. Linkage data could be interpreted in light of corroborative complementary experimental data from different technologies, such as expression evidence from microarrays,²⁸ databases, or experiments in cell lines or animal models.²⁹ Genomic DNA sequences within the linked region could be screened for markers or potential mutations and the results (positive and negative) reported together with the initial linkage data, along with a detailed description of the population studied. Or alternatively, genome-wide association studies using high-density single-nucleotide polymorphism maps may be a realistic alternative approach to complex trait gene discovery.³⁰ New approaches, attitudes and tools, together with time to reflect and converse, might help to improve the yield of lasting, replicable molecular etiologies for complex diseases that can be found by casting genome-wide nets into the genetic ocean.

	Acknowledgments

 
R.L.P. is supported by a Natural Sciences and Engineering Research Council of Canada graduate scholarship. R.A.H. is supported by the Edith Schulich Vinet Canada research chair (Tier I) in human genetics, a career investigator award from the Heart and Stroke Foundation of Ontario (CI 4380), and operating grants from the Canadian Institutes for Health Research, the Ontario Research and Development Challenge Fund, and the Blackburn Group.

Received February 3, 2005; accepted May 27, 2005.

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Top Introduction Applying Linkage Analysis to... Determining the Yield From... FCHL: A Case Report Complex Trait Mapping: How... Conclusions References

 

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