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Neurological Review |

The HapMap:  Charting a Course for Genetic Discovery in Neurological Diseases FREE

John Hardy, PhD; Andrew Singleton, PhD
[+] Author Affiliations

Author Affiliations:Department of Molecular Neuroscience and Reta Lila Weston Laboratories, Institute of Neurology, University College London, London, England (Dr Hardy); and Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland (Dr Singleton).


Section Editor: David E. Pleasure, MD

More Author Information
Arch Neurol. 2008;65(3):319-321. doi:10.1001/archneur.65.3.319.
Text Size: A A A
Published online

Whole-genome association analyses have begun to yield confirmed findings for genetic risk variants for complex disease. As the first reports of its application to neurological disease are described, we review this progress, explain the principles of the analysis, and discuss what the future is likely to be in this exciting area.

Figures in this Article

The human genome draft sequence released in 20011,2was a consensus sequence based on the stitching together of DNA sequences from clones derived from many individuals; at best, this corresponded to an imperfect sketch of the human sequence and certainly represented no one person. The immediate utility of the human draft DNA sequence was that it provided a map to allow scientists to localize genes that were mutated in mendelian disease. It did not directly help us to understand the more subtle differences between us, including predispositions to the many common diseases that afflict humans. These common diseases, which include most cases of neurological disease such as most amyotrophic lateral sclerosis, Parkinson disease, Alzheimer disease, stroke, brain tumors, many other cancers, most heart disease, and type 2 diabetes, have been believed to be predisposed to by many common variants across our genome. It has been believed that much human disease had its roots in individuals with unfortunate combinations of variants in different genes across their genome, perhaps with exposure to predisposing environmental factors and possibly a little bad luck, too.

Despite this pervasive belief, few of these common variants have been identified. Two approaches have been used during the last 15 years to find them: candidate gene association studies and affected family member (sibpair) linkage studies. While each has had limited successes, progress in general had been disappointing.

This last year, finally, the drought in genetic findings for complex diseases has ended and a deluge of clear disease associations has been reported. The reasons for this sudden change in fortune are both scientific and technological. The scientific change was the systematic identification of polymorphisms across the human genome. It has led to the discoveries that variability was not random in any population and that variability at one position could predict adjacent variability with reasonable accuracy (Figure). These realizations were systematized into a knowledge base of the human HapMap,3which cataloged those single-nucleotide polymorphisms that could be used to capture the majority of human variability and was used to choose approximately 500 000 single-nucleotide polymorphisms whose genetic analysis could be used to assess about 95% of genetic variability. The technological advance was the development of 2 competing platforms that allow the assessment of these tagging single-nucleotide polymorphisms (http://www.affymetrix.com/index.affxand http://www.illumina.com/). The competition between the 2 platforms has driven the price down from approximately $1000 per individual to cover 50% of the genome in mid 2005 to approximately $250 to cover 95% of the genome in mid 2007.

Place holder to copy figure label and caption
Figure.

In this theoretical example, there are 4 single-nucleotide polymorphisms (SNPs) (in capital) that are close to each other and each of which has allele frequencies of approximately 50%. This might suggest that each of the 4 combinations would occur one-sixteenth of the time—approximately 6%—but in practice this is not what we see, and in fact 2 variants, 1 and 12, predominate. This means that if you genotype just 1 of the SNPs (for example, SNP2), you can guess with reasonable accuracy (but not certainty) what the other SNPs would be in that individual. Thus, SNP2 captures or tags most of the genetic diversity at this locus.

Graphic Jump Location

The result of this technological progress has been that increasing numbers of diseases are being analyzed by this whole-genome technology and are yielding confirmed risk factor loci. Early success in age-related macular degeneration, where the identified locus conferred a strong genetic risk, has been swiftly followed by efforts that have revealed and confirmed moderate and minor risk loci in type 2 diabetes, heart disease, atrial fibrillation, prostate cancer, and breast cancer. Progress is now so rapid that this list grows each week; the initial effect of these studies is that they immediately reveal “low-hanging fruit,” risk loci that have effects substantial enough to allow detection at first pass; as one commentary has recently put it, this has been likened to drinking from a fire hose.4Increasing sample numbers will eventually allow the detection of very small effects and effects that are reliant on multigene or gene-environment interactions. Given this rapid progress, it is perhaps worth briefly reviewing the single example of type 2 diabetes as an illustration of the general principles involved and issues raised.

In 2006, using a linkage approach, the Decode group5reported that the transcriptional gene TCF7L2was a risk locus for this disorder. Sladek and colleagues6confirmed this finding in a whole-genome study and additionally reported that the insulin degrading enzyme locus (IDE) and the pancreatic β-cell transporter gene (SLC30A8) also met genomewide significance. Then, 3 other studies79confirmed these findings and, individually and through pooling of their data, identified CDKAL1, CDKN2A/CDKN2B, FTO, KCNJ1, and IGF2BP2as risk factor loci as well as confirmed PPARG(first identified as a candidate gene locus10because it is the site of action of rosiglitazone maleate11).

This story illustrates several points. First, large studies can find real associations (each study had on the order of 1500 cases and controls). Second, replication leads to confidence. Third, pooling of data from the studies led to extra findings (3 studies pooled their data, and now, all of the groups who have published their findings are also pooling their data), leading fourth to the expectation that when the data represent approximately 15 000 cases and 15 000 controls, other findings will be made. Fifth, this approach can lead to directly “druggable” targets. As 2 examples, PPARGis the site of action of the major drug class for type 2 diabetes11and zinc supplementation had already been considered as a therapy for diabetes.12

To date, no confirmed findings to our knowledge have been reported for neurological diseases except the confirmation that the method picks up APOEin Alzheimer disease13and the MAPTlocus in progressive supranuclear palsy.14However, there have been initial reports for Alzheimer disease,15amyotrophic lateral sclerosis,16,17Parkinson disease,18and ischemic stroke.19Most of these studies15,1719have publicly released their data, facilitating replication. No doubt, definitive findings will be made in the next period.

However, perhaps as exciting as this identification of single-nucleotide polymorphism associations has been the realization that this technology can pick up unexpected homozygosity20and thus be used to clone recessive disease loci almost instantly.21,22Finally, this technology also rapidly identifies the newly recognized large insertion, deletion, and inversion polymorphisms in the human genome20,23that also have clear implications for the dissection of the pathogenesis of neurological disease.24,25

Despite the phenomenal progress these findings represent, they are raising as many questions as they promise to answer. Many of the confirmed “hits” are not near genes: what do they represent? Distant control elements or RNA regulatory transcripts are possibilities. Most hits in genes do not alter the amino acid sequence, and this must represent variability that alters expression or splicing as we have recently shown is relevant for MAPTelements.26Surprisingly, most of the new associations do not explain previous genetic linkage results, perhaps suggesting that much rare variability awaits to be found as is certainly true for cholesterol metabolism27and may also be true in Alzheimer disease.28Two other daunting tasks await geneticists: first, developing an understanding of gene-gene and gene-environment interactions, and second, developing the technologies (both technical and analytical) to generate and interpret the whole-genome medical sequencing that will be possible within the next 5 years. The progress this last year has been truly astounding. Much will be achieved in the next 2 years, but perhaps as important, this progress means that the even more difficult goals needed to fully understand disease pathogenesis now seem within reach. To attain these goals, we will need the resolve and self-confidence to collaborate and pool data, even with our competitors. We will need to recognize that these efforts require massive clinical and laboratory investments; thus, we must ensure that academic rewards and incentives are assured for all involved. Lastly, we will need the continued commitment of funding agencies because these experiments are both large scale and expensive; however, they promise to supply unequivocal answers to questions we have been asking for a long time and thus provide genuine value for money.

Correspondence:John Hardy, PhD, Department of Molecular Neuroscience and Reta Lila Weston Laboratories, Institute of Neurology, University College London, Queen Square, London WC1 3BG, England (j.hardy@ion.ucl.ac.uk).

Accepted for Publication:August 27, 2007.

Author Contributions:Study concept and design: Hardy and Singleton. Drafting of the manuscript: Hardy. Critical revision of the manuscript for important intellectual content: Singleton.

Financial Disclosure:None reported.

Venter  JCAdams  MDMyers  EW  et al.  The sequence of the human genome. Science 2001;291 (5507) 1304- 1351[published correction appears in Science. 2001;292(5523):1838].
PubMed
Lander  ESLinton  LMBirren  B  et al. International Human Genome Sequencing Consortium, Initial sequencing and analysis of the human genome. Nature 2001;409 (6822) 860- 922[published corrections appear in Nature. 2001;412(6846):565 and Nature. 2001;411(6838):720].
PubMed
International HapMap Consortium, A haplotype map of the human genome. Nature 2005;437 (7063) 1299- 1320
PubMed
Hunter  DJKraft  P Drinking from the fire hose: statistical issues in genomewide association studies. N Engl J Med 2007;357 (5) 436- 439
PubMed
Grant  SFThorleifsson  GReynisdottir  I  et al.  Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 2006;38 (3) 320- 323
PubMed
Sladek  RRocheleau  GRung  J  et al.  A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445 (7130) 881- 885
PubMed
Saxena  RVoight  BFLyssenko  V  et al. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT; Lund University; Novartis Institutes of BioMedical Research, Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007;316 (5829) 1331- 1336
PubMed
Zeggini  EWeedon  MNLindgren  CM  et al.  Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 2007;316 (5829) 1336- 1341
PubMed
Scott  LJMohlke  KLBonnycastle  LL  et al.  A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007;316 (5829) 1341- 1345
PubMed
Deeb  SSFajas  LNemoto  M  et al.  A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet 1998;20 (3) 284- 287
PubMed
Saltiel  AROlefsky  JM Thiazolidinediones in the treatment of insulin resistance and type II diabetes. Diabetes 1996;45 (12) 1661- 1669
PubMed
Beletate  VEl Dib  RPAtallah  AN Zinc supplementation for the prevention of type 2 diabetes mellitus. Cochrane Database Syst Rev 2007; (1) CD005525
PubMed
Coon  KDMyers  AJCraig  DW  et al.  A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease. J Clin Psychiatry 2007;68 (4) 613- 618
PubMed
Melquist  SCraig  DWHuentelman  MJ  et al.  Identification of a novel risk locus for progressive supranuclear palsy by a pooled genomewide scan of 500 288 single-nucleotide polymorphisms. Am J Hum Genet 2007;80 (4) 769- 778
PubMed
Reiman  EMWebster  JAMyers  AJ  et al.  GAB2 alleles modify Alzheimer's risk in APOE epsilon4 carriers. Neuron 2007;54 (5) 713- 720
PubMed
Dunckley  THuentelman  MJCraig  DW  et al.  Whole-genome analysis of sporadic amyotrophic lateral sclerosis [published online ahead of print August 1, 2007]. N Engl J Med 2007;357 (8) 775- 788
PubMed
Schymick  JCScholz  SWFung  HC  et al.  Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 2007;6 (4) 322- 328
PubMed
Fung  HCScholz  SMatarin  M  et al.  Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 2006;5 (11) 911- 916
PubMed
Matarín  MBrown  WMScholz  S  et al.  A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol 2007;6 (5) 414- 420
PubMed
Simon-Sanchez  JScholz  SFung  HC  et al.  Genome-wide SNP assay reveals structural genomic variation, extended homozygosity and cell-line induced alterations in normal individuals. Hum Mol Genet 2007;16 (1) 1- 14
PubMed
Simon-Sanchez  JScholz  SDel Mar Matarin  M  et al.  Genomewide SNP assay reveals mutations underlying Parkinson disease [published online ahead of print November 9, 2007]. Hum Mutat 2007; (2)
PubMed
Camargos  SScholz  SSimon-Sanchez  J  et al.  DYT16, a novel young-onset dystonia-parkinsonism disorder: identification of a segregating mutation in the stress response protein prkra. Lancet Neurol 2008;7 (3) 207- 215
PubMed
McCarroll  SAHadnott  TNPerry  GH  et al. International HapMap Consortium, Common deletion polymorphisms in the human genome. Nat Genet 2006;38 (1) 86- 92
PubMed
van de Leemput  JChandran  JKnight  MA  et al.  Deletion at ITPR1 underlies ataxia in mice and spinocerebellar ataxia 15 in humans. PLoS Genet 2007;3 (6) e108
PubMed10.1371/journal.pgen.0030108
Lupski  JR Structural variation in the human genome. N Engl J Med 2007;356 (11) 1169- 1171
PubMed
Myers  AJPittman  AMZhao  AS  et al.  The MAPT H1c risk haplotype is associated with increased expression of tau and especially of 4 repeat containing transcripts. Neurobiol Dis 2007;25 (3) 561- 570
PubMed
Cohen  JCKiss  RSPertsemlidis  AMarcel  YLMcPherson  RHobbs  HH Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 2004;305 (5685) 869- 872
PubMed
Kauwe  JSJacquart  SChakraverty  S  et al.  Extreme cerebrospinal fluid amyloid beta levels identify family with late-onset Alzheimer's disease presenilin 1 mutation. Ann Neurol 2007;61 (5) 446- 453
PubMed

Figures

Place holder to copy figure label and caption
Figure.

In this theoretical example, there are 4 single-nucleotide polymorphisms (SNPs) (in capital) that are close to each other and each of which has allele frequencies of approximately 50%. This might suggest that each of the 4 combinations would occur one-sixteenth of the time—approximately 6%—but in practice this is not what we see, and in fact 2 variants, 1 and 12, predominate. This means that if you genotype just 1 of the SNPs (for example, SNP2), you can guess with reasonable accuracy (but not certainty) what the other SNPs would be in that individual. Thus, SNP2 captures or tags most of the genetic diversity at this locus.

Graphic Jump Location

Tables

References

Venter  JCAdams  MDMyers  EW  et al.  The sequence of the human genome. Science 2001;291 (5507) 1304- 1351[published correction appears in Science. 2001;292(5523):1838].
PubMed
Lander  ESLinton  LMBirren  B  et al. International Human Genome Sequencing Consortium, Initial sequencing and analysis of the human genome. Nature 2001;409 (6822) 860- 922[published corrections appear in Nature. 2001;412(6846):565 and Nature. 2001;411(6838):720].
PubMed
International HapMap Consortium, A haplotype map of the human genome. Nature 2005;437 (7063) 1299- 1320
PubMed
Hunter  DJKraft  P Drinking from the fire hose: statistical issues in genomewide association studies. N Engl J Med 2007;357 (5) 436- 439
PubMed
Grant  SFThorleifsson  GReynisdottir  I  et al.  Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 2006;38 (3) 320- 323
PubMed
Sladek  RRocheleau  GRung  J  et al.  A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445 (7130) 881- 885
PubMed
Saxena  RVoight  BFLyssenko  V  et al. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT; Lund University; Novartis Institutes of BioMedical Research, Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007;316 (5829) 1331- 1336
PubMed
Zeggini  EWeedon  MNLindgren  CM  et al.  Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 2007;316 (5829) 1336- 1341
PubMed
Scott  LJMohlke  KLBonnycastle  LL  et al.  A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007;316 (5829) 1341- 1345
PubMed
Deeb  SSFajas  LNemoto  M  et al.  A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet 1998;20 (3) 284- 287
PubMed
Saltiel  AROlefsky  JM Thiazolidinediones in the treatment of insulin resistance and type II diabetes. Diabetes 1996;45 (12) 1661- 1669
PubMed
Beletate  VEl Dib  RPAtallah  AN Zinc supplementation for the prevention of type 2 diabetes mellitus. Cochrane Database Syst Rev 2007; (1) CD005525
PubMed
Coon  KDMyers  AJCraig  DW  et al.  A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease. J Clin Psychiatry 2007;68 (4) 613- 618
PubMed
Melquist  SCraig  DWHuentelman  MJ  et al.  Identification of a novel risk locus for progressive supranuclear palsy by a pooled genomewide scan of 500 288 single-nucleotide polymorphisms. Am J Hum Genet 2007;80 (4) 769- 778
PubMed
Reiman  EMWebster  JAMyers  AJ  et al.  GAB2 alleles modify Alzheimer's risk in APOE epsilon4 carriers. Neuron 2007;54 (5) 713- 720
PubMed
Dunckley  THuentelman  MJCraig  DW  et al.  Whole-genome analysis of sporadic amyotrophic lateral sclerosis [published online ahead of print August 1, 2007]. N Engl J Med 2007;357 (8) 775- 788
PubMed
Schymick  JCScholz  SWFung  HC  et al.  Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 2007;6 (4) 322- 328
PubMed
Fung  HCScholz  SMatarin  M  et al.  Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 2006;5 (11) 911- 916
PubMed
Matarín  MBrown  WMScholz  S  et al.  A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol 2007;6 (5) 414- 420
PubMed
Simon-Sanchez  JScholz  SFung  HC  et al.  Genome-wide SNP assay reveals structural genomic variation, extended homozygosity and cell-line induced alterations in normal individuals. Hum Mol Genet 2007;16 (1) 1- 14
PubMed
Simon-Sanchez  JScholz  SDel Mar Matarin  M  et al.  Genomewide SNP assay reveals mutations underlying Parkinson disease [published online ahead of print November 9, 2007]. Hum Mutat 2007; (2)
PubMed
Camargos  SScholz  SSimon-Sanchez  J  et al.  DYT16, a novel young-onset dystonia-parkinsonism disorder: identification of a segregating mutation in the stress response protein prkra. Lancet Neurol 2008;7 (3) 207- 215
PubMed
McCarroll  SAHadnott  TNPerry  GH  et al. International HapMap Consortium, Common deletion polymorphisms in the human genome. Nat Genet 2006;38 (1) 86- 92
PubMed
van de Leemput  JChandran  JKnight  MA  et al.  Deletion at ITPR1 underlies ataxia in mice and spinocerebellar ataxia 15 in humans. PLoS Genet 2007;3 (6) e108
PubMed10.1371/journal.pgen.0030108
Lupski  JR Structural variation in the human genome. N Engl J Med 2007;356 (11) 1169- 1171
PubMed
Myers  AJPittman  AMZhao  AS  et al.  The MAPT H1c risk haplotype is associated with increased expression of tau and especially of 4 repeat containing transcripts. Neurobiol Dis 2007;25 (3) 561- 570
PubMed
Cohen  JCKiss  RSPertsemlidis  AMarcel  YLMcPherson  RHobbs  HH Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 2004;305 (5685) 869- 872
PubMed
Kauwe  JSJacquart  SChakraverty  S  et al.  Extreme cerebrospinal fluid amyloid beta levels identify family with late-onset Alzheimer's disease presenilin 1 mutation. Ann Neurol 2007;61 (5) 446- 453
PubMed

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