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Special Article |

The Quest for Genes Causing Complex Traits in Ocular Medicine Successes, Interpretations, and Challenges FREE

Sudha K. Iyengar, PhD
[+] Author Affiliations

Author Affiliations: Departments of Epidemiology and Biostatistics, Ophthalmology, and Genetics, Case Western Reserve University, Cleveland, Ohio.


Arch Ophthalmol. 2007;125(1):11-18. doi:10.1001/archopht.125.1.11.
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Published online

  Gene mapping and positional cloning have gained acceptance as state-of-the-art methods to identify molecules that cause common complex diseases. However, the use of specialized technology, varying study designs, and misconceptions about the role of novel findings in genetics has led to confusion among basic scientists and health care professionals alike regarding the importance of these findings in molecular diagnostics and individualized medicine. To alleviate this confusion, the successes achieved in the past few years in mapping of genes for complex traits such as age-related macular degeneration and glaucoma are interpreted in the context of the appropriate population biology framework. The current article veers away from propagating the overly simplistic belief of a linear relationship between a specific gene and age-related macular degeneration, particularly one that equates possession of a specific risk allele as the only precursor to end-stage disease. Ascribing predictive properties to a single gene without consideration of its network partners, timing of action, or environmental correlates argues for a static view of gene action. Modern viewpoints of the mechanisms of action of a gene are contextual and encompass more cohesive frameworks, ranging from the developmental timing of action, to the genomic and environmental milieu. In this regard, gene mapping studies that have been so immensely successful in the gene detection phase of a study provide biased perspectives on the importance of these genes and the corresponding risk alleles in the general population because of their limited sample size and constrained design. To move the field of gene-based diagnosis forward, it will be necessary to conduct additional cohort and longitudinal studies using the original gene finding studies as a knowledge base to develop predictive models. In summary, while we have achieved great successes in finding genes for complex traits, the application of these findings to clinical medicine is not straightforward. The key question of who will develop disease in the future remains.

The merger of 2 fields, traditional epidemiology, which focuses on the distribution and determinants of disease in human populations, and genetics, the study of inherited mechanisms of disease, led to the development of a broader allied field that draws on the strengths of both these disciplines. The goal of investigations in genetic epidemiology is to identify genes and to study their mechanisms of action in populations; other closely connected disciplines include molecular genetics and statistical genetics, neither of which has firm borders distinguishing it from genetic epidemiology. The theoretical basis for the field of genetic epidemiology was developed in the early 1900s by R. A. Fisher with the unification of theories from quantitative and qualitative modes of inheritance.1,2

Until recently, when the human genome was more fully characterized,3,4 the pace of the investigations and identification of disease genes remained slow. In the past, the most prominent successes were limited to disorders with mendelian inheritance patterns and strong familial risks (also described as high recurrence risks), where a single gene carried the bulk of the disease burden (eg, paired box 6 and aniridia type 2). In contrast, common complex disorders or multifactorial disorders are characterized by multiple genes and environmental factors contributing to their etiology. Two international, large-scale endeavors, the Human Genome Project (HGP)3,4 and the International HapMap Project,5,6 have accelerated the speed at which disease genes are being discovered for both rare and common disorders. The HGP was a large-scale enterprise to sequence the nuclear genome, find, and annotate all the possible genes in the genome. The project was completed in 2001 and a draft assembly of the human genome is available on a Web server (http://genome.ucsc.edu and www.ensembl.org).3,4 The HapMap Project took on the task of further characterizing the genome where the HGP left off. The goal of the HapMap Project is to determine all common genetic variation (both in genes and outside genes) in several different populations worldwide.5,6 These projects have been supported by technological and methodological advances that often require specialty knowledge to interpret results.

The goal of this review is to provide an explanation of the different approaches used in statistical genetics and genetic epidemiology for health care professionals and researchers studying eye disorders to assist in the interpretation of the rapidly changing and complex literature.

Very often, diseases are assumed to be the outcome of a singular event or a convergence of multiple events into a singular outcome, and individuals are classified as “with disease” or “without disease” using specific nosology. Thus, the presence or absence of the disease may aggregate in families as a binary trait. To quantify the extent of the familial aggregation, the recurrence risk ratio may be used as a measure.7 The recurrence risk ratio (λR) for a specific relative type is the ratio of the prevalence of the disease in the relatives of the index case to the prevalence of the disease in the general population; relationships must be specified because distant relatives share less of the genome than first-degree relatives. In the situation of many complex traits where extended relatives are hard to obtain, the sibling relative risk ratio is often used to determine if sufficient power is available to map disease genes.7,8 As an example, the recurrence risk ratios to siblings of cases with age-related macular degeneration (AMD) are projected to be 3- to 6-fold higher compared with the general population.9 Obtaining sibling recurrence risk ratios higher than 2 argues that a pattern of familial clustering over and above the risk in the general population is present. While genetic predisposition is certainly one explanation for familial aggregation, it is certainly not the only explanation for obtaining relative risk ratios higher than 2. Shared environment may also contribute to higher risks of disease. As an example, smoking increases the risk of not just the individual who smokes but may affect other members of the household, especially if they are also genetically susceptible. Furthermore, prevalence is difficult to estimate for many complex traits because obtaining an unbiased sample of the general population is quite often a diffi-cult endeavor. As described later, studies for gene mapping are biased toward collection of enriched families for disease or toward identification of cases and controls who may not ideally represent the frequencies in the general population. While these maneuvers are advantageous in finding disease genes, overlooking the ascertainment bias will skew the results when determining the population attributable risk (PAR) due to a gene.10 The PAR quantifies the proportion of the disease burden that can be eradicated or ameliorated if a risk factor is removed from the population. Thus, ascribing a PAR of 50% will suggest that 50% of the disease burden can be eliminated by taking away the risk factor. Ideally, to determine the PAR accurately, one would need a sufficiently large sample that is representative of the general population and is collected without regard to disease status.11 This brings the genetics question back to a population level and to investigations in epidemiology.

Assessment of a continuous measure, such as intraocular pressure or cholesterol levels, can be used to appraise the familial aggregation of a trait that may relate to the disease process. These measures, which correlate with the disease process but do not fully represent all facets, are sometimes called endophenotypes or intermediate traits.12 These traits are assumed to be more sensitive measures of salient aspects of the disease process and hence might be easier to map than the disease itself. Correlation coefficients can measure similarity in values of a continuous measure between 2 relatives. Similar to the concept of sibling relative risk, the variance in the phenotype for quantitative traits can be parsed into genetic and environmental components. These calculations of the heritability (the additive genetic component) would enable one to embark on molecular mapping studies.

Heritability is a population-specific concept and is often misinterpreted and misrepresented. Hypothetically, if every individual present in a population possessed 2 copies of a specific disease allele (homozygous for disease), the heritability for that gene in that population would be zero because there is no variation at that locus and it is not feasible to contrast individuals with variability in their genetic content at that locus. A measurable heritability (>15% conventionally) suggests that a gene for that disease is segregating in the population and that a sample from the population can be subject to mapping experiments. Interpretation of heritability estimates assumes an expert knowledge of the methods used in the calculations and the disease itself. There are a number of methods to calculate heritability, but each method uses slightly different pieces of information, specifically the information on additive and dominance portions of variance. Additive variance can be explained at the level of the allele.13,14 The DNA sequence is made up of 4 bases: A, G, T, and C. The order in which the bases occur in the natural sequence can be determined through a technique called sequencing, as was done in the HGP. The sequence is not identical in all individuals but is subject to variation. The variant form of any part of the sequence is generically called an allele. Alleles can be simple changes in a single base (eg, A vs G) or can be fairly complex involving large segments of the DNA. At a particular locus with 3 alleles, A1, A2, and A3, each allele has a specific value it would impart to the phenotype. As an example, if one considers systolic blood pressure, the A1 allele may correspond to a value of 118 mm Hg, whereas the A2 allele may correspond with a value of 140 mm Hg and the A3 allele may correspond with a value of 110 mm Hg. Thus, an individual with a genotype of A1A1 would have an average reading of 118 mm Hg, while an individual with the A1A2 genotype will have a higher reading of 129 mm Hg. This hypothetical example demonstrates that each allele acts on a linear scale and additively. Dominance variance characterizes the interaction of alleles when the joint action deviates from the simple linear relationship. For complex traits, both additive and dominance effects at a locus play a role in disease etiology.

Both binary and quantitative traits have been used in ocular genetics, but the earlier focus has been predominantly on binary traits. This trend is now shifting to examine quantitative traits. While not often described extensively, ordinal traits that measure disease progression or severity via juxtaposition of multiple traits in a specific series can also be used as a proxy for a direct quantitative measure. Here, caution needs to be heeded when performing disease mapping experiments because the assumption is that all steps in the model are equal, unless the steps are weighted to reflect the underlying pathophysiology. So for a 5-step scale, the change from 0 to 1 is the same as the change from 1 to 2 and so on. As long as each increase reflects minute changes in the natural history of the disease, the method of using the contrived scale is valid. In fact, using a binary indicator in a quantitative trait linkage analysis (1 = presence of disease, 0 = absence of disease) is a specific case of this method.

Calculations of relative recurrence risks, heritability, or segregation analysis are traditional methods used to establish the feasibility of a genetic mechanism causing the disease and are the prima facie evidence that precedes gene mapping studies. Segregation analysis, a method for formal model fitting using phenotypic data in pedigrees, has been used sparingly since the advent of advanced molecular methods because it is time-consuming and prone to many uncertainties. The majority of these studies make no allusions to any specific genes, nor do they require molecular genetic data.

Linkage analysis has been extensively used in the mapping of genes for ocular disorders1520 and has led to the identification of genes for both rare and complex ocular traits. Linkage has customarily been used for the mapping of rare disorders through the collection of larger families, with many individuals being “affected.” The minimum requirement is a single pair of affected siblings but can include more extended families, with second- and third-degree relatives contributing to the overall evidence for or against a particular gene or chromosomal region. The clinical data on affection status are contrasted between individuals who are affected and unaffected. In the same statistical test, the molecular contrasts are provided by markers (sequence variants) that can trace inheritance patterns in chromosomes between members of the same family. These markers can interrogate a single gene, an entire chromosome or a specific chromosomal location, or the entire nuclear genome. The mitochondrial genome has a maternal mode of inheritance and its own set of markers. The expectation is that if a gene for the disorder exists, then individuals in a family who were affected would preferentially inherit the affected portion of a chromosome from their parent(s).Chromosomes or pieces of chromosomes that were inherited equally by affected and unaffected members of a family (ie, segregating randomly according to the laws of Mendel) are not associated with disease.

The summary statistic describing evidence for or against linkage between a disease locus and a marker was developed by Morton21 and is called the LOD (logarithm of the backward odds) score statistic and can be described as the ratio of the likelihood for the data at a particular recombination fraction over the likelihood of the data under no linkage,

 Image not available.

where θ is the recombination fraction.13,14

The original method and many of its adaptations relied on assumption of specific modes of inheritance (eg, autosomal dominant, autosomal recessive) to test the hypotheses of linkage, the so-called model-based methods.21 Subsequently, model-independent methods were developed that used allele sharing between members of a family without making assumptions regarding the mode of inheritance.2226 A LOD score threshold of 3 using the traditional model and 3.6 when performing a genomewide scan using model-independent methods is considered sufficient evidence to conclude that linkage exists between a chromosomal segment and disease.27 For multifactorial diseases, very often the burden of a LOD score of 3.6 is not met after performing a genome scan, resulting in reservations that genes for complex diseases can be mapped. This is exemplified by examining all the linkage scans for AMD.2835 Each individually did not reach statistical significance (ie, a LOD score of 3.6 on 1q31), but a meta-analysis36 showed that the LOD score on 1q31 was the second strongest signal across studies; the strongest was the locus on 10q. When the linkage scans were originally published, the use of a variety of linkage methods and the corresponding statistics (eg, LOD score, maximum LOD score statistic, nonparametric LOD score)37 that were used to achieve the same goal were often confusing to readers unfamiliar with the methods because the statistics reported did not appear to be directly comparable. However, most can be converted to a familiar P value through use of specific referent distributions.

Prior to the popularization of single nucleotide polymorphisms (SNPs) as the markers that are common, cosmopolitan, and easy to genotype, microsatellites spaced at even intervals on each of 22 autosomes and on the X chromosome were used for genomewide scans. Such scans have been performed for both monogenic ocular disorders and complex ocular traits3842 and ranged in cost, density of coverage, and markers genotyped. The current SNP scans consist of approximately 6000 or more markers, with an average intermarker distance of about 0.64 megabase (Mb) (1 Mb = 1 million bases).43 Microsatellite scans were less dense (about ≥400 genomewide) with an average intermarker distance of 10 centimorgans or roughly 10 Mb. The replacement of microsatellites with SNPs enabled greater automation and reduced genotyping error, but microsatellites, the majority of which were commonly used for genetic studies, had more than 2 alleles and carried more information than most SNPs. The increase in the density of SNP to microsatellite coverage is to partially compensate for the loss of information because the majority of SNPs are biallelic. The current maps are increasing in density as more SNPs are publicly made available through government-funded5,6 and commercial ventures.44,45

A requirement for linkage analysis is a family unit with at least 2 or more affected members. Study designs have varied from collection of large families with many affected individuals (families with the greatest genetic load), to modestly sized families, to a single affected sib pair, to distant relatives in isolated populations. In some cases, homozygosity mapping for recessive diseases using pooling techniques has also been useful,4649 but this method is unlikely to gain popularity for multifactorial traits because the assumptions being made when pooling samples are quite vital to the success of the experiment. Affected parent-offspring relationships were included in the traditional model-based methods for linkage but do not provide linkage information via model-free linkage methods. These relatives have other uses, such as relationship testing and fine mapping, and should be collected whenever feasible. Strategies for enrichment of disease-bearing individuals vary with the complexity of the disorder and the availability of family members for participation in the study. For example, if a disease has a late onset, then finding surviving relatives who reside in the same geographic region may prove to be a difficult task, especially in larger metropolitan cities in the United States. For a late-onset disease, obtaining truly unaffected individuals may also be difficult, especially for diseases where the possession of the disease allele is not commensurate with developing the disorder (variable penetrance).

One method to alleviate concerns of misclassification in affection status is to use an ordinal or quantitative measure that captures additional information beyond a simple description of affected and unaffected in families. Further, this clinical (phenotypic) information should be captured uniformly within and across the families, using epidemiologic principles of objective measurement and standardized techniques that have been validated, whenever feasible. For example, using intraocular pressure as an intermediate trait for glaucoma may provide additional information during the gene mapping process, while recognizing that normotensive glaucoma may also segregate in the family. Therefore, by mapping genes for only this feature, the full extent of the genetic variation that causes glaucoma will not be characterized. Difficulties in collection of enriched families owing to variable penetrance, complex modes of inheritance, and changes in the disease status with age are often cited as reasons why alternate methods to map disease genes (eg, association studies described later) are selected in lieu of linkage mapping. In these cases, collection of endophenotypes as surrogates for disease may provide some necessary information.

In summary, while quite successful for traits showing mendelian inheritance patterns, linkage analysis had not proven very tractable for most multifactorial diseases, and many ocular disorders and traits lag in gene identification. The difficulties in mapping these disease genes have been owing to genetic heterogeneity (different loci segregating in different samples), modest sample sizes that have not been sufficiently large to extract the linkage signal (ie, small effect size of the disease gene), phenotypic heterogeneity, and epistasis (interaction between genes). Thus, gene discovery for most ocular traits is still in the early phase, the exception being AMD, which has had successful breakthroughs in disease gene mapping despite its complexity. Two loci, complement factor H and LOC387715, were identified as major candidate genes underneath linkage peaks on chromosomes 1q5052 and 10q,53,54 following linkage experiments. More convincingly, the majority of the studies published have shown some support for both loci. Further, the linkage scans for AMD have been surprisingly concordant in identification of multiple regions likely to harbor a candidate gene beyond the loci on chromosomes 1 and 10, which showed the best evidence for linkage in a meta-analysis for AMD.55 Similarly, there seems to be a convergence of evidence on chromosomes 5q and 14q for glaucoma susceptibility,15,56 although a disease locus has not yet been identified. Therefore, the prospects of using family-based methods to map disease genes for ocular diseases are quite favorable.

With the advent of high-density genome scans for association, many skeptics of linkage scans have advocated abandoning linkage studies altogether. The rationale for the diminished enthusiasm for linkage is as follows. (1) Linkage studies require an investment in families of individuals; for every index case collected, another 2 to 3 family members are required, making this design quite expensive during the data collection phase. (2) Optimism is high regarding the success of association scans, which can be constrained to a case-control design. (3) Linkage restricts the region bearing the disease gene to between 10 and 15 Mb; further fine mapping and mutation hunting are necessary to find the actual causative variant. (4) Linkage mapping relies solely on information within families; the LOD score and other model-free statistics sum up results across families. This results in loss of power for loci that may have a modest effect or control only a fraction of the families (genetic heterogeneity).

However, certain modes of inheritance (eg, parent of origin effects or imprinting) can only be studied under a family-based design, be it linkage or association. Additionally, many weaknesses of the linkage design can be mediated by specialized methods. As an example, loss of power due to locus heterogeneity can be alleviated by using covariates to rank families and boost linkage signals.5760 Technological development has made assessment of markers cheaper, which has enabled scientists to increase the coverage of the genome from coarse scans to tighter scans. The main benefit of this advance is that additional information has been gained regarding inheritance from the increased coverage of the genome or chromosomal segment. Therefore, finding genes for disease using a linkage signal as a first step has become more feasible.

Association extracts genetic (allelic) information at the level of the population and theoretically compares tiny fragments of chromosomes between individuals with and without disease to localize disease susceptibility.13,61 In contrast to linkage studies, methods to test association can use a variety of study designs. Although traditionally case-control studies have been advocated for association testing, other designs, such as cohort studies and trios of families consisting of an affected individual and his or her parents, are also suitable for association testing. The family-based association design was developed by Spielman and colleagues6264 to guard against population stratification. The latter method cleverly uses information within and across families to construct pseudocontrols from untransmitted chromosomes in a statistical test called the transmission disequilibrium test. It also retains information from linkage and is the ideal middle ground between strictly family-based linkage and case-control methods.

Association testing can either determine if a gene is involved in disease through interrogation of specific variants, or SNPs can be used as surrogates in an indirect test when the gene or mutation is unknown. Consequently, it is feasible to target specific hypotheses, such as if a sequence variant (eg, the Y402H polymorphism at complement factor H for AMD or specific variants in optineurin65,66 or myocilin6772 for glaucoma) is responsible for the disease burden in a particular population or sample. Recently, this framework of scanning candidate genes has been broadened to genomewide association tests,7378 whereby the entire genome is inspected at predefined intervals to determine if association with a genomic fragment can be established. The method to capture the association ranges from the standard fare in epidemiology, such as χ2 tests and logistic regression modeling, to more sophisticated tests. As an example, a general test that takes advantage of some of the useful properties of family-based association designs has been proposed7981; it uses moving windows to examine consecutive SNPs genomewide. The family-based association testing methods have other advantages as well. A wider range of hypotheses can be tested, including parent-of-origin effects, skewing of sex ratios, and other complex modes of inheritance.

Many companies are offering genotyping products that interrogate 100 000 to 500 000 SNPs simultaneously, and denser scans are in production stages.8290 This phenomenon, coupled with the cost reduction for genotyping, has led to a paradigm shift from linkage studies to association studies. Association studies are beguilingly portrayed as the panacea to gene mapping woes, but similar to linkage studies, association studies also have their own weaknesses. First, isolation of a genetic variant(s) that shows stark frequency contrasts between cases and controls does not provide sufficient grounds to assume a causative role for the variant. The variant may not be causal but simply in linkage disequilibrium (assort in the population on the same chromosomal segment) with the disease variant.74 Second, a more grievous error can occur when cases and controls are unintentionally drawn from genetically different populations or even subpopulations with distinct properties, such that a false-positive signal is generated because of confounding when conducting association tests. As described earlier, the transmission disequilibrium test was proposed as a safeguard against such false positives, but with a large number of statistical tests being conducted using dense genome scans, a proportion of such false positives is expected. Methods to protect case-control studies from confounding related to population stratification have also been proposed.9193

However, the best defense against false positives is replication of the experiment in a second independent sample. For most diseases, collection of a second sample is generally prohibitively expensive because, typically, collection of clinical and demographic data is more expensive. In an effort to alleviate some of these problems, efforts to support community collaborations are ongoing, with public availability of data sets gaining popularity,94,95 despite ethical dilemmas posed by the exposure of individual genetic data.6

Convenience sampling of cases and controls from clinical practices can also have an effect on the ability to identify disease genes through association testing because of biased representation of individuals from the population. When a variant associated with disease is identified, this result may not be portable to other populations or even to other samples derived from the same population. Finally, the foundation for genomewide association studies is built on the assumptions proposed in a population genetic theory of dispersal of human populations more than 100 000 years ago. This theory is called the common disease common variant hypothesis, and its assumptions, advantages, and shortcomings have been reviewed previously.9698 Its greatest weakness is that it may not have sufficient power to identify rare causative variants for disease. In this instance, family-based methods will prove more effective if the right types of families are identified.

As previously described, the environment also plays a significant role in complex diseases. Embracing this comprehensive view, the National Institute of Environmental Health Sciences has launched a project similar in scope to the HapMap project, the Genes and Environment Initiative.94 The goal of the initiative is to understand how the genome and environmental parameters interact to cause disease. In the past, gene and environmental investigations were limited to a few genes or exposures. The scope of this project broadens the paradigm to genomewide investigations. In this scenario, epidemiologic studies (eg, cohort studies) have a very important role if biological samples are available for assessment of DNA variation. These can form the knowledge base of prospective cohort studies.99

An important issue germane to the discussion of genomewide linkage and association tests is that of multiple testing. The genome has so much variability at the level of the population that 100 000 to 1 million or more markers may be required to cover it adequately. Each of these markers is then assessed for its correlation with disease, creating a problem with the large number of statistical tests being conducted. Although, more methods with more liberal thresholds have been proposed to deal with the multiple testing problem,100 the larger problem of discriminating signal from noise still remains, especially in smaller data sets.

Interpretation of the results of linkage and association scans is not an easy endeavor particularly when investigators arrive at discordant results. Guidelines suggested for contextual interpretation of these studies (eg, the Lander and Kruglyak guidelines27 for interpreting linkage studies) have been misinterpreted as the gold standard and good studies abandoned when the experiment merely suggests that additional data were needed to meet the burden of proof. In the case where the results from several experiments (ie, multiple genome scans or association studies of a candidate gene) are not always concordant, the actual variants or markers, the samples, the population from which the sample derives, and the statistical and molecular methods used need to be carefully compared for similarity and dissimilarity. As in the case of calpain 10 and diabetes mellitus, an apparent controversy in the results across studies can be reconciled through joint analysis and meta-analysis.101 Calpain 10 is a gene associated with type 2 diabetes and was discovered through a linkage scan for type 2 diabetes in the Mexican American population. This gene was shown to be of biological importance, but the genetic data were not supported by replication studies. However, aggregation of data across multiple samples showed that there was soft evidence in multiple samples that did not meet statistical significance individually but cumulatively met the threshold for significance. Replication is a very important attribute of epidemiologic studies, and meta-analyses may be viewed as the mechanism to combine data across heterogenous samples when collection of a new cohort of sufficiently large size is not feasible.

The current state of the art is multistaged designs that encompass data from linkage, association, and molecular experiments when feasible. Neither linkage nor association mapping require a priori knowledge of the function of the gene prior to embarking on gene mapping investigations, but some general knowledge about the potential function of an unknown gene is certainly an advantage. In the future, ocular genetic studies may rely on multicenter and community collaborations for mapping experiments.

Despite the optimistic forecast for gene mapping, many unresolved issues remain. Some of these issues are formulated into simple questions that deserve further scientific thought. Are most ocular diseases caused by a few common variants that can be used to predict disease status? Should variants that show association signals in non–gene bearing chromosomal regions be ignored? Will interactions between genes and between genes and environment mediate the bulk of the modifiable risk for ocular disease? The field of comparative genomics for complex ocular disorders is in its infancy. Do we anticipate that the allelic spectrum of mutations will encompass copy number variants? And most importantly, can the discoveries made at the bench be translated into therapies that cure inherited ocular disorders?

Correspondence: Sudha K. Iyengar, PhD, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Bldg 1315, 10900 Euclid Ave, Cleveland, OH 44106-7281 (ski@case.edu).

Submitted for Publication: September 6, 2006; final revision received September 28, 2006; accepted September 28, 2006.

Financial Disclosure: None reported.

Funding/Support: Dr Iyengar is supported by grants EY015814 (Fine Mapping of Genes for Age-Related Maculopathy) and EY016482 (A Multicenter Study to Map Genes for Fuchs Dystrophy) from the National Eye Institute.

Elston  RCRao  DC Statistical modeling and analysis in human genetics. Annu Rev Biophys Bioeng 1978;7253- 286
PubMed Link to Article
Bailer  AJPiegorsch  WW Estimating integrals using quadrature methods with an application in pharmacokinetics. Biometrics 1990;461201- 1211
PubMed Link to Article
Lander  ESLinton  LMBirren  B  et al.  Initial sequencing and analysis of the human genome. Nature 2001;409860- 921
PubMed Link to Article
Venter  JCAdams  MDMyers  EW  et al.  The sequence of the human genome. Science 2001;2911304- 1351
PubMed Link to Article
The International HapMap Consortium, The International HapMap Project. Nature 2003;426789- 796
PubMed Link to Article
International HapMap Consortium, Integrating ethics and science in the International HapMap Project. Nat Rev Genet 2004;5467- 475
PubMed Link to Article
Risch  N Linkage strategies for genetically complex traits, I: multilocus models. Am J Hum Genet 1990;46222- 228
PubMed
James  JW Frequency in relatives for an all-or-none trait. Ann Hum Genet 1971;3547- 49
PubMed Link to Article
Maller  JGeorge  SPurcell  S  et al.  Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat Genet 2006;381055- 1059
PubMed Link to Article
Guo  SW Inflation of sibling recurrence-risk ratio, due to ascertainment bias and/or overreporting. Am J Hum Genet 1998;63252- 258
PubMed Link to Article
Elston  RC 'Twixt cup and lip: how intractable is the ascertainment problem? Am J Hum Genet 1995;5615- 17
PubMed
Duggirala  RWilliams  JTWilliams-Blangero  SBlangero  J A variance component approach to dichotomous trait linkage analysis using a threshold model. Genet Epidemiol 1997;14987- 992
PubMed Link to Article
Strachan  TRead  AP Human Molecular Genetics 2. 2nd New York, NY Garland Science 1999;
Thomas  DC Statistical Methods in Genetic Epidemiology.  New York, NY Oxford University Press 2004;
Wiggs  JL Genes associated with human glaucoma. Ophthalmol Clin North Am 2005;18335- 343
PubMed Link to Article
Francis  PJ Genetics of inherited retinal disease. J R Soc Med 2006;99189- 191
PubMed Link to Article
Kelly  JMaumenee  IH Hereditary macular diseases. Int Ophthalmol Clin 1999;3983- 115
PubMed Link to Article
Chung  MLotery  AJ Genetics update of macular diseases. Ophthalmol Clin North Am 2002;15459- 465
PubMed Link to Article
Hewitt  AWCraig  JEMackey  DA Complex genetics of complex traits: the case of primary open-angle glaucoma. Clin Experiment Ophthalmol 2006;34472- 484
PubMed Link to Article
Libby  RTGould  DBAnderson  MGJohn  SW Complex genetics of glaucoma susceptibility. Annu Rev Genomics Hum Genet 2005;615- 44
PubMed Link to Article
Morton  NE Genetic tests under incomplete ascertainment. Am J Hum Genet 1959;111- 16
PubMed
Elston  RC The genetic dissection of multifactorial traits. Clin Exp Allergy 1995;25 ((suppl 2)) 103- 106
PubMed Link to Article
Haseman  JKElston  RC The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 1972;23- 19
PubMed Link to Article
Brown  DLGorin  MBWeeks  DE Efficient strategies for genomic searching using the affected-pedigree-member method of linkage analysis. Am J Hum Genet 1994;54544- 552
PubMed
Goldgar  DE Multipoint analysis of human quantitative genetic variation. Am J Hum Genet 1990;47957- 967
PubMed
Amos  CI Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 1994;54535- 543
PubMed
Lander  EKruglyak  L Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 1995;11241- 247
PubMed Link to Article
Abecasis  GRYashar  BMZhao  Y  et al.  Age-related macular degeneration: a high-resolution genome scan for susceptibility loci in a population enriched for late-stage disease. Am J Hum Genet 2004;74482- 494
PubMed Link to Article
Schick  JHIyengar  SKKlein  BE  et al.  A whole-genome screen of a quantitative trait of age-related maculopathy in sibships from the Beaver Dam Eye Study. Am J Hum Genet 2003;721412- 1424
PubMed Link to Article
Iyengar  SKSong  DKlein  BE  et al.  Dissection of genomewide-scan data in extended families reveals a major locus and oligogenic susceptibility for age-related macular degeneration. Am J Hum Genet 2004;7420- 39
PubMed Link to Article
Majewski  JSchultz  DWWeleber  RG  et al.  Age-related macular degeneration—a genome scan in extended families. Am J Hum Genet 2003;73540- 550
PubMed Link to Article
Seddon  JMSantangelo  SLBook  KChong  SCote  J A genomewide scan for age-related macular degeneration provides evidence for linkage to several chromosomal regions. Am J Hum Genet 2003;73780- 790
PubMed Link to Article
Weeks  DEConley  YPTsai  HJ  et al.  Age-related maculopathy: a genomewide scan with continued evidence of susceptibility loci within the 1q31, 10q26, and 17q25 regions. Am J Hum Genet 2004;75174- 189
PubMed Link to Article
Weeks  DEConley  YPTsai  HJ  et al.  Age-related maculopathy: an expanded genome-wide scan with evidence of susceptibility loci within the 1q31 and 17q25 regions. Am J Ophthalmol 2001;132682- 692
PubMed Link to Article
Weeks  DEConley  YPMah  TS  et al.  A full genome scan for age-related maculopathy. Hum Mol Genet 2000;91329- 1349
PubMed Link to Article
Fisher  SAAbecasis  GRYashar  BM  et al.  Meta-analysis of genome scans of age-related macular degeneration. Hum Mol Genet 2005;142257- 2264
PubMed Link to Article
Kruglyak  LDaly  MJReeve-Daly  MPLander  ES Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet 1996;581347- 1363
PubMed
Shastry  BSHartzer  MKTrese  MT Familial exudative vitreoretinopathy: multiple modes of inheritance. Clin Genet 1993;44275- 276
PubMed Link to Article
Young  TL Ophthalmic genetics/inherited eye disease. Curr Opin Ophthalmol 2003;14296- 303
PubMed Link to Article
MacDonald  IMSasi  R Molecular genetics of inherited eye disorders. Clin Invest Med 1994;17474- 498
PubMed
Cremers  FPvan den Hurk  JAden Hollander  AI Molecular genetics of Leber congenital amaurosis. Hum Mol Genet 2002;111169- 1176
PubMed Link to Article
 Genetic eye diseases: retinitis pigmentosa and other inherited eye disorders. Proceedings of the International Symposium on Genetics and Ophthalmology, held in Jerusalem, Israel, September 1981. Birth Defects Orig Artic Ser 1982;18 ((6)) 1- 746
PubMed
 Center for Inherited Disease Research Web site. http://www.cidr.jhmi.edu/
Peacock  EWhiteley  P Perlegen Sciences, Inc. Pharmacogenomics 2005;6439- 442
PubMed Link to Article
Padhukasahasram  BWall  JMarjoram  PNordborg  M Estimating recombination rates from SNPs using summary statistics. Genetics September 15, 2006;lsqb;Epub ahead of print]
PubMed
Sheffield  VCCarmi  RKwitek-Black  A  et al.  Identification of a Bardet-Biedl syndrome locus on chromosome 3 and evaluation of an efficient approach to homozygosity mapping. Hum Mol Genet 1994;31331- 1335
PubMed Link to Article
Hastbacka  Jde la  CAKaitila  ISistonen  PWeaver  ALander  E Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nat Genet 1992;2204- 211
PubMed Link to Article
Houwen  RHBaharloo  SBlankenship  K  et al.  Genome screening by searching for shared segments: mapping a gene for benign recurrent intrahepatic cholestasis. Nat Genet 1994;8380- 386
PubMed Link to Article
Lander  ESBotstein  D Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science 1987;2361567- 1570
PubMed Link to Article
Edwards  AORitter  R  IIIAbel  KJManning  APanhuysen  CFarrer  LA Complement factor H polymorphism and age-related macular degeneration. Science 2005;308421- 424
PubMed Link to Article
Klein  MLSchultz  DWEdwards  A  et al.  Age-related macular degeneration: clinical features in a large family and linkage to chromosome 1q. Arch Ophthalmol 1998;1161082- 1088
PubMed Link to Article
Haines  JLHauser  MASchmidt  S  et al.  Complement factor H variant increases the risk of age-related macular degeneration. Science 2005;308419- 421
PubMed Link to Article
Rivera  AFisher  SAFritsche  LG  et al.  Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum Mol Genet 2005;143227- 3236
PubMed Link to Article
Jakobsdottir  JConley  YPWeeks  DEMah  TSFerrell  REGorin  MB Susceptibility genes for age-related maculopathy on chromosome 10q26. Am J Hum Genet 2005;77389- 407
PubMed Link to Article
Fisher  R The correlation between relatives on the supposition of Mendelian in heritance. Trans R Soc Edinburgh 1918; ((52)) 399- 433
Rotimi  CNChen  GAdeyemo  AA  et al.  Genomewide scan and fine mapping of quantitative trait loci for intraocular pressure on 5q and 14q in West Africans. Invest Ophthalmol Vis Sci 2006;473262- 3267
PubMed Link to Article
Goddard  KAWitte  JSSuarez  BKCatalona  WJOlson  JM Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4. Am J Hum Genet 2001;681197- 1206
PubMed Link to Article
Olson  JM A general conditional-logistic model for affected-relative-pair linkage studies. Am J Hum Genet 1999;651760- 1769
PubMed Link to Article
Hauser  ERWatanabe  RMDuren  WLBass  MPLangefeld  CDBoehnke  M Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 2004;2753- 63
PubMed Link to Article
Schaid  DJOlson  JMGauderman  WJElston  RC Regression models for linkage: issues of traits, covariates, heterogeneity, and interaction. Hum Hered 2003;5586- 96
PubMed Link to Article
Strachan  TRead  AP Human Molecular Genetics 3.  Oxford, England Garland Science/Taylor & Francis Group2003;
Ewens  WJSpielman  RS The transmission/disequilibrium test: history, subdivision, and admixture. Am J Hum Genet 1995;57455- 464
PubMed Link to Article
Spielman  RSMcGinnis  REEwens  WJ Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993;52506- 516
PubMed
Spielman  RSEwens  WJ A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am J Hum Genet 1998;62450- 458
PubMed Link to Article
Tang  SToda  YKashiwagi  K  et al.  The association between Japanese primary open-angle glaucoma and normal tension glaucoma patients and the optineurin gene. Hum Genet 2003;113276- 279
PubMed Link to Article
Rezaie  TChild  AHitchings  R  et al.  Adult-onset primary open-angle glaucoma caused by mutations in optineurin. Science 2002;2951077- 1079
PubMed Link to Article
Wiggs  JLAllingham  RRVollrath  D  et al.  Prevalence of mutations in TIGR/myocilin in patients with adult and juvenile primary open-angle glaucoma. Am J Hum Genet 1998;631549- 1552
PubMed Link to Article
Fingert  JHHeon  ELiebmann  JM  et al.  Analysis of myocilin mutations in 1703 glaucoma patients from five different populations. Hum Mol Genet 1999;8899- 905
PubMed Link to Article
Colomb  ENguyen  TDBechetoille  A  et al.  Association of a single nucleotide polymorphism in the TIGR/MYOCILIN gene promoter with the severity of primary open-angle glaucoma. Clin Genet 2001;60220- 225
PubMed Link to Article
Baird  PNCraig  JERichardson  AJ  et al.  Analysis of 15 primary open-angle glaucoma families from Australia identifies a founder effect for the Q368STOP mutation of myocilin. Hum Genet 2003;112110- 116
PubMed
Adam  MFBelmouden  ABinisti  P  et al.  Recurrent mutations in a single exon encoding the evolutionarily conserved olfactomedin-homology domain of TIGR in familial open-angle glaucoma. Hum Mol Genet 1997;62091- 2097
PubMed Link to Article
Alward  WLFingert  JHCoote  MA  et al.  Clinical features associated with mutations in the chromosome 1 open-angle glaucoma gene (GLC1A). N Engl J Med 1998;3381022- 1027
PubMed Link to Article
Evans  DMCardon  LR Genome-wide association: a promising start to a long race. Trends Genet 2006;22350- 354
PubMed Link to Article
Lawrence  RWEvans  DMCardon  LR Prospects and pitfalls in whole genome association studies. Philos Trans R Soc Lond B Biol Sci 2005;3601589- 1595
PubMed Link to Article
Palmer  LJCardon  LR Shaking the tree: mapping complex disease genes with linkage disequilibrium. Lancet 2005;3661223- 1234
PubMed Link to Article
Zeggini  ERayner  WMorris  AP  et al.  An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets. Nat Genet 2005;371320- 1322
PubMed Link to Article
Zondervan  KTCardon  LR The complex interplay among factors that influence allelic association. Nat Rev Genet 2004;589- 100
PubMed Link to Article
Stone  EMFingert  JHAlward  WL  et al.  Identification of a gene that causes primary open angle glaucoma. Science 1997;275668- 670
PubMed Link to Article
Lin  SChakravarti  ACutler  DJ Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat Genet 2004;361181- 1188
PubMed Link to Article
Lin  SChakravarti  ACutler  DJ Haplotype and missing data inference in nuclear families. Genome Res 2004;141624- 1632
PubMed Link to Article
Lin  SCutler  DJZwick  MEChakravarti  A Haplotype inference in random population samples. Am J Hum Genet 2002;711129- 1137
PubMed Link to Article
Shen  RFan  JBCampbell  D  et al.  High-throughput SNP genotyping on universal bead arrays. Mutat Res 2005;57370- 82
PubMed Link to Article
Oliphant  ABarker  DLStuelpnagel  JRChee  MS BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 2002; ((suppl)) 56- 58, 60-61
PubMed
Gunderson  KLKruglyak  SGraige  MS  et al.  Decoding randomly ordered DNA arrays. Genome Res 2004;14870- 877
PubMed Link to Article
Gunderson  KLSteemers  FJRen  H  et al.  Whole-genome genotyping. Methods Enzymol 2006;410359- 376
PubMed
Fan  JBYeakley  JMBibikova  M  et al.  A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res 2004;14878- 885
PubMed Link to Article
Fan  JBOliphant  AShen  R  et al.  Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol 2003;6869- 78
PubMed Link to Article
Lipshutz  RJTaverner  FHennessy  KHartzell  GDavis  R DNA sequence confidence estimation. Genomics 1994;19417- 424
PubMed Link to Article
Lockhart  DJDong  HByrne  MC  et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996;141675- 1680
PubMed Link to Article
Chee  MYang  RHubbell  E  et al.  Accessing genetic information with high-density DNA arrays. Science 1996;274610- 614
PubMed Link to Article
Pritchard  JKRosenberg  NA Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 1999;65220- 228
PubMed Link to Article
Pritchard  JKDonnelly  P Case-control studies of association in structured or admixed populations. Theor Popul Biol 2001;60227- 237
PubMed Link to Article
Devlin  BRoeder  K Genomic control for association studies. Biometrics 1999;55997- 1004
PubMed Link to Article
Kuehn  BM NIH initiatives to probe contribution of genes, environment in disease. JAMA 2006;2951633- 1634
PubMed
Wilson  SHOlden  K The environmental genome project: phase I and beyond. Mol Interv 2004;4147- 156
PubMed
Wright  AFHastie  ND Complex genetic diseases: controversy over the Croesus code. Genome Biol 2001;2 (8) comment 2007
PubMed
Doris  PA Hypertension genetics, single nucleotide polymorphisms, and the common disease: common variant hypothesis. Hypertension 2002;39323- 331
PubMed Link to Article
Pritchard  JK Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 2001;69124- 137
PubMed Link to Article
Manolio  TABailey-Wilson  JECollins  FS Genes, environment and the value of prospective cohort studies. Nat Rev Genet 2006;7812- 820
PubMed Link to Article
Benjamini  YHochberg  Y Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995;57289- 300
Tsuchiya  TSchwarz  PEBosque-Plata  LD  et al.  Association of the calpain-10 gene with type 2 diabetes in Europeans: results of pooled and meta-analyses. Mol Genet Metab 2006;89174- 184
PubMed Link to Article

Figures

Tables

References

Elston  RCRao  DC Statistical modeling and analysis in human genetics. Annu Rev Biophys Bioeng 1978;7253- 286
PubMed Link to Article
Bailer  AJPiegorsch  WW Estimating integrals using quadrature methods with an application in pharmacokinetics. Biometrics 1990;461201- 1211
PubMed Link to Article
Lander  ESLinton  LMBirren  B  et al.  Initial sequencing and analysis of the human genome. Nature 2001;409860- 921
PubMed Link to Article
Venter  JCAdams  MDMyers  EW  et al.  The sequence of the human genome. Science 2001;2911304- 1351
PubMed Link to Article
The International HapMap Consortium, The International HapMap Project. Nature 2003;426789- 796
PubMed Link to Article
International HapMap Consortium, Integrating ethics and science in the International HapMap Project. Nat Rev Genet 2004;5467- 475
PubMed Link to Article
Risch  N Linkage strategies for genetically complex traits, I: multilocus models. Am J Hum Genet 1990;46222- 228
PubMed
James  JW Frequency in relatives for an all-or-none trait. Ann Hum Genet 1971;3547- 49
PubMed Link to Article
Maller  JGeorge  SPurcell  S  et al.  Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat Genet 2006;381055- 1059
PubMed Link to Article
Guo  SW Inflation of sibling recurrence-risk ratio, due to ascertainment bias and/or overreporting. Am J Hum Genet 1998;63252- 258
PubMed Link to Article
Elston  RC 'Twixt cup and lip: how intractable is the ascertainment problem? Am J Hum Genet 1995;5615- 17
PubMed
Duggirala  RWilliams  JTWilliams-Blangero  SBlangero  J A variance component approach to dichotomous trait linkage analysis using a threshold model. Genet Epidemiol 1997;14987- 992
PubMed Link to Article
Strachan  TRead  AP Human Molecular Genetics 2. 2nd New York, NY Garland Science 1999;
Thomas  DC Statistical Methods in Genetic Epidemiology.  New York, NY Oxford University Press 2004;
Wiggs  JL Genes associated with human glaucoma. Ophthalmol Clin North Am 2005;18335- 343
PubMed Link to Article
Francis  PJ Genetics of inherited retinal disease. J R Soc Med 2006;99189- 191
PubMed Link to Article
Kelly  JMaumenee  IH Hereditary macular diseases. Int Ophthalmol Clin 1999;3983- 115
PubMed Link to Article
Chung  MLotery  AJ Genetics update of macular diseases. Ophthalmol Clin North Am 2002;15459- 465
PubMed Link to Article
Hewitt  AWCraig  JEMackey  DA Complex genetics of complex traits: the case of primary open-angle glaucoma. Clin Experiment Ophthalmol 2006;34472- 484
PubMed Link to Article
Libby  RTGould  DBAnderson  MGJohn  SW Complex genetics of glaucoma susceptibility. Annu Rev Genomics Hum Genet 2005;615- 44
PubMed Link to Article
Morton  NE Genetic tests under incomplete ascertainment. Am J Hum Genet 1959;111- 16
PubMed
Elston  RC The genetic dissection of multifactorial traits. Clin Exp Allergy 1995;25 ((suppl 2)) 103- 106
PubMed Link to Article
Haseman  JKElston  RC The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 1972;23- 19
PubMed Link to Article
Brown  DLGorin  MBWeeks  DE Efficient strategies for genomic searching using the affected-pedigree-member method of linkage analysis. Am J Hum Genet 1994;54544- 552
PubMed
Goldgar  DE Multipoint analysis of human quantitative genetic variation. Am J Hum Genet 1990;47957- 967
PubMed
Amos  CI Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 1994;54535- 543
PubMed
Lander  EKruglyak  L Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 1995;11241- 247
PubMed Link to Article
Abecasis  GRYashar  BMZhao  Y  et al.  Age-related macular degeneration: a high-resolution genome scan for susceptibility loci in a population enriched for late-stage disease. Am J Hum Genet 2004;74482- 494
PubMed Link to Article
Schick  JHIyengar  SKKlein  BE  et al.  A whole-genome screen of a quantitative trait of age-related maculopathy in sibships from the Beaver Dam Eye Study. Am J Hum Genet 2003;721412- 1424
PubMed Link to Article
Iyengar  SKSong  DKlein  BE  et al.  Dissection of genomewide-scan data in extended families reveals a major locus and oligogenic susceptibility for age-related macular degeneration. Am J Hum Genet 2004;7420- 39
PubMed Link to Article
Majewski  JSchultz  DWWeleber  RG  et al.  Age-related macular degeneration—a genome scan in extended families. Am J Hum Genet 2003;73540- 550
PubMed Link to Article
Seddon  JMSantangelo  SLBook  KChong  SCote  J A genomewide scan for age-related macular degeneration provides evidence for linkage to several chromosomal regions. Am J Hum Genet 2003;73780- 790
PubMed Link to Article
Weeks  DEConley  YPTsai  HJ  et al.  Age-related maculopathy: a genomewide scan with continued evidence of susceptibility loci within the 1q31, 10q26, and 17q25 regions. Am J Hum Genet 2004;75174- 189
PubMed Link to Article
Weeks  DEConley  YPTsai  HJ  et al.  Age-related maculopathy: an expanded genome-wide scan with evidence of susceptibility loci within the 1q31 and 17q25 regions. Am J Ophthalmol 2001;132682- 692
PubMed Link to Article
Weeks  DEConley  YPMah  TS  et al.  A full genome scan for age-related maculopathy. Hum Mol Genet 2000;91329- 1349
PubMed Link to Article
Fisher  SAAbecasis  GRYashar  BM  et al.  Meta-analysis of genome scans of age-related macular degeneration. Hum Mol Genet 2005;142257- 2264
PubMed Link to Article
Kruglyak  LDaly  MJReeve-Daly  MPLander  ES Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet 1996;581347- 1363
PubMed
Shastry  BSHartzer  MKTrese  MT Familial exudative vitreoretinopathy: multiple modes of inheritance. Clin Genet 1993;44275- 276
PubMed Link to Article
Young  TL Ophthalmic genetics/inherited eye disease. Curr Opin Ophthalmol 2003;14296- 303
PubMed Link to Article
MacDonald  IMSasi  R Molecular genetics of inherited eye disorders. Clin Invest Med 1994;17474- 498
PubMed
Cremers  FPvan den Hurk  JAden Hollander  AI Molecular genetics of Leber congenital amaurosis. Hum Mol Genet 2002;111169- 1176
PubMed Link to Article
 Genetic eye diseases: retinitis pigmentosa and other inherited eye disorders. Proceedings of the International Symposium on Genetics and Ophthalmology, held in Jerusalem, Israel, September 1981. Birth Defects Orig Artic Ser 1982;18 ((6)) 1- 746
PubMed
 Center for Inherited Disease Research Web site. http://www.cidr.jhmi.edu/
Peacock  EWhiteley  P Perlegen Sciences, Inc. Pharmacogenomics 2005;6439- 442
PubMed Link to Article
Padhukasahasram  BWall  JMarjoram  PNordborg  M Estimating recombination rates from SNPs using summary statistics. Genetics September 15, 2006;lsqb;Epub ahead of print]
PubMed
Sheffield  VCCarmi  RKwitek-Black  A  et al.  Identification of a Bardet-Biedl syndrome locus on chromosome 3 and evaluation of an efficient approach to homozygosity mapping. Hum Mol Genet 1994;31331- 1335
PubMed Link to Article
Hastbacka  Jde la  CAKaitila  ISistonen  PWeaver  ALander  E Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nat Genet 1992;2204- 211
PubMed Link to Article
Houwen  RHBaharloo  SBlankenship  K  et al.  Genome screening by searching for shared segments: mapping a gene for benign recurrent intrahepatic cholestasis. Nat Genet 1994;8380- 386
PubMed Link to Article
Lander  ESBotstein  D Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science 1987;2361567- 1570
PubMed Link to Article
Edwards  AORitter  R  IIIAbel  KJManning  APanhuysen  CFarrer  LA Complement factor H polymorphism and age-related macular degeneration. Science 2005;308421- 424
PubMed Link to Article
Klein  MLSchultz  DWEdwards  A  et al.  Age-related macular degeneration: clinical features in a large family and linkage to chromosome 1q. Arch Ophthalmol 1998;1161082- 1088
PubMed Link to Article
Haines  JLHauser  MASchmidt  S  et al.  Complement factor H variant increases the risk of age-related macular degeneration. Science 2005;308419- 421
PubMed Link to Article
Rivera  AFisher  SAFritsche  LG  et al.  Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum Mol Genet 2005;143227- 3236
PubMed Link to Article
Jakobsdottir  JConley  YPWeeks  DEMah  TSFerrell  REGorin  MB Susceptibility genes for age-related maculopathy on chromosome 10q26. Am J Hum Genet 2005;77389- 407
PubMed Link to Article
Fisher  R The correlation between relatives on the supposition of Mendelian in heritance. Trans R Soc Edinburgh 1918; ((52)) 399- 433
Rotimi  CNChen  GAdeyemo  AA  et al.  Genomewide scan and fine mapping of quantitative trait loci for intraocular pressure on 5q and 14q in West Africans. Invest Ophthalmol Vis Sci 2006;473262- 3267
PubMed Link to Article
Goddard  KAWitte  JSSuarez  BKCatalona  WJOlson  JM Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4. Am J Hum Genet 2001;681197- 1206
PubMed Link to Article
Olson  JM A general conditional-logistic model for affected-relative-pair linkage studies. Am J Hum Genet 1999;651760- 1769
PubMed Link to Article
Hauser  ERWatanabe  RMDuren  WLBass  MPLangefeld  CDBoehnke  M Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 2004;2753- 63
PubMed Link to Article
Schaid  DJOlson  JMGauderman  WJElston  RC Regression models for linkage: issues of traits, covariates, heterogeneity, and interaction. Hum Hered 2003;5586- 96
PubMed Link to Article
Strachan  TRead  AP Human Molecular Genetics 3.  Oxford, England Garland Science/Taylor & Francis Group2003;
Ewens  WJSpielman  RS The transmission/disequilibrium test: history, subdivision, and admixture. Am J Hum Genet 1995;57455- 464
PubMed Link to Article
Spielman  RSMcGinnis  REEwens  WJ Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993;52506- 516
PubMed
Spielman  RSEwens  WJ A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am J Hum Genet 1998;62450- 458
PubMed Link to Article
Tang  SToda  YKashiwagi  K  et al.  The association between Japanese primary open-angle glaucoma and normal tension glaucoma patients and the optineurin gene. Hum Genet 2003;113276- 279
PubMed Link to Article
Rezaie  TChild  AHitchings  R  et al.  Adult-onset primary open-angle glaucoma caused by mutations in optineurin. Science 2002;2951077- 1079
PubMed Link to Article
Wiggs  JLAllingham  RRVollrath  D  et al.  Prevalence of mutations in TIGR/myocilin in patients with adult and juvenile primary open-angle glaucoma. Am J Hum Genet 1998;631549- 1552
PubMed Link to Article
Fingert  JHHeon  ELiebmann  JM  et al.  Analysis of myocilin mutations in 1703 glaucoma patients from five different populations. Hum Mol Genet 1999;8899- 905
PubMed Link to Article
Colomb  ENguyen  TDBechetoille  A  et al.  Association of a single nucleotide polymorphism in the TIGR/MYOCILIN gene promoter with the severity of primary open-angle glaucoma. Clin Genet 2001;60220- 225
PubMed Link to Article
Baird  PNCraig  JERichardson  AJ  et al.  Analysis of 15 primary open-angle glaucoma families from Australia identifies a founder effect for the Q368STOP mutation of myocilin. Hum Genet 2003;112110- 116
PubMed
Adam  MFBelmouden  ABinisti  P  et al.  Recurrent mutations in a single exon encoding the evolutionarily conserved olfactomedin-homology domain of TIGR in familial open-angle glaucoma. Hum Mol Genet 1997;62091- 2097
PubMed Link to Article
Alward  WLFingert  JHCoote  MA  et al.  Clinical features associated with mutations in the chromosome 1 open-angle glaucoma gene (GLC1A). N Engl J Med 1998;3381022- 1027
PubMed Link to Article
Evans  DMCardon  LR Genome-wide association: a promising start to a long race. Trends Genet 2006;22350- 354
PubMed Link to Article
Lawrence  RWEvans  DMCardon  LR Prospects and pitfalls in whole genome association studies. Philos Trans R Soc Lond B Biol Sci 2005;3601589- 1595
PubMed Link to Article
Palmer  LJCardon  LR Shaking the tree: mapping complex disease genes with linkage disequilibrium. Lancet 2005;3661223- 1234
PubMed Link to Article
Zeggini  ERayner  WMorris  AP  et al.  An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets. Nat Genet 2005;371320- 1322
PubMed Link to Article
Zondervan  KTCardon  LR The complex interplay among factors that influence allelic association. Nat Rev Genet 2004;589- 100
PubMed Link to Article
Stone  EMFingert  JHAlward  WL  et al.  Identification of a gene that causes primary open angle glaucoma. Science 1997;275668- 670
PubMed Link to Article
Lin  SChakravarti  ACutler  DJ Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat Genet 2004;361181- 1188
PubMed Link to Article
Lin  SChakravarti  ACutler  DJ Haplotype and missing data inference in nuclear families. Genome Res 2004;141624- 1632
PubMed Link to Article
Lin  SCutler  DJZwick  MEChakravarti  A Haplotype inference in random population samples. Am J Hum Genet 2002;711129- 1137
PubMed Link to Article
Shen  RFan  JBCampbell  D  et al.  High-throughput SNP genotyping on universal bead arrays. Mutat Res 2005;57370- 82
PubMed Link to Article
Oliphant  ABarker  DLStuelpnagel  JRChee  MS BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 2002; ((suppl)) 56- 58, 60-61
PubMed
Gunderson  KLKruglyak  SGraige  MS  et al.  Decoding randomly ordered DNA arrays. Genome Res 2004;14870- 877
PubMed Link to Article
Gunderson  KLSteemers  FJRen  H  et al.  Whole-genome genotyping. Methods Enzymol 2006;410359- 376
PubMed
Fan  JBYeakley  JMBibikova  M  et al.  A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res 2004;14878- 885
PubMed Link to Article
Fan  JBOliphant  AShen  R  et al.  Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol 2003;6869- 78
PubMed Link to Article
Lipshutz  RJTaverner  FHennessy  KHartzell  GDavis  R DNA sequence confidence estimation. Genomics 1994;19417- 424
PubMed Link to Article
Lockhart  DJDong  HByrne  MC  et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996;141675- 1680
PubMed Link to Article
Chee  MYang  RHubbell  E  et al.  Accessing genetic information with high-density DNA arrays. Science 1996;274610- 614
PubMed Link to Article
Pritchard  JKRosenberg  NA Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 1999;65220- 228
PubMed Link to Article
Pritchard  JKDonnelly  P Case-control studies of association in structured or admixed populations. Theor Popul Biol 2001;60227- 237
PubMed Link to Article
Devlin  BRoeder  K Genomic control for association studies. Biometrics 1999;55997- 1004
PubMed Link to Article
Kuehn  BM NIH initiatives to probe contribution of genes, environment in disease. JAMA 2006;2951633- 1634
PubMed
Wilson  SHOlden  K The environmental genome project: phase I and beyond. Mol Interv 2004;4147- 156
PubMed
Wright  AFHastie  ND Complex genetic diseases: controversy over the Croesus code. Genome Biol 2001;2 (8) comment 2007
PubMed
Doris  PA Hypertension genetics, single nucleotide polymorphisms, and the common disease: common variant hypothesis. Hypertension 2002;39323- 331
PubMed Link to Article
Pritchard  JK Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 2001;69124- 137
PubMed Link to Article
Manolio  TABailey-Wilson  JECollins  FS Genes, environment and the value of prospective cohort studies. Nat Rev Genet 2006;7812- 820
PubMed Link to Article
Benjamini  YHochberg  Y Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995;57289- 300
Tsuchiya  TSchwarz  PEBosque-Plata  LD  et al.  Association of the calpain-10 gene with type 2 diabetes in Europeans: results of pooled and meta-analyses. Mol Genet Metab 2006;89174- 184
PubMed Link to Article

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