Genome-wide association studies: a powerful tool for neurogenomics

Full access

As their power and utility increase, genome-wide association (GWA) studies are poised to become an important element of the neurosurgeon's toolkit for diagnosing and treating disease. In this paper, the authors review recent findings and discuss issues associated with gathering and analyzing GWA data for the study of neurological diseases and disorders, including those of neurosurgical importance. Their goal is to provide neurosurgeons and other clinicians with a better understanding of the practical and theoretical issues associated with this line of research. A modern GWA study involves testing hundreds of thousands of genetic markers across an entire genome, often in thousands of individuals, for any significant association with a particular disease. The number of markers assayed in a study presents several practical and theoretical issues that must be considered when planning the study. Genome-wide association studies show great promise in our understanding of the genes underlying common neurological diseases and disorders, as well as in leading to a new generation of genetic tests for clinicians.

Abbreviations used in this paper: GWA = genome-wide association; HWE = Hardy-Weinberg equilibrium; SNP = single-nucleotide polymorphism.

Abstract

As their power and utility increase, genome-wide association (GWA) studies are poised to become an important element of the neurosurgeon's toolkit for diagnosing and treating disease. In this paper, the authors review recent findings and discuss issues associated with gathering and analyzing GWA data for the study of neurological diseases and disorders, including those of neurosurgical importance. Their goal is to provide neurosurgeons and other clinicians with a better understanding of the practical and theoretical issues associated with this line of research. A modern GWA study involves testing hundreds of thousands of genetic markers across an entire genome, often in thousands of individuals, for any significant association with a particular disease. The number of markers assayed in a study presents several practical and theoretical issues that must be considered when planning the study. Genome-wide association studies show great promise in our understanding of the genes underlying common neurological diseases and disorders, as well as in leading to a new generation of genetic tests for clinicians.

A goal of molecular genetics is to discover the genetic architecture of human phenotypes, especially diseases. The research community has recently made great strides toward associating loci (genes) with phenotypes (diseases), but much work remains.80 These advances have resulted from significant increases in the scale and power of genetic-linkage tests, which have grown from candidate-gene analyses to GWA studies.

Genome-wide association studies are intended to address some of the shortcomings of traditional candidate-gene linkage tests. Classic linkage studies are typically difficult to conduct, at least in part because they require a priori knowledge about the biology of the disease under study (to select candidate genes) as well as a familiarity with the genetic variants (that is, mutations) in the candidate genes that could alter function or expression.113 Additionally, there is an inherent bias in the candidate-gene approach stemming from the typically small number of genes that are selected for testing. The low-throughput nature of candidate-gene studies obviously makes them ill suited for testing roughly 30,000 genes and the millions of observed genetic variants in the human genome.

There has been a significant increase in the number of GWA studies being conducted, with ~ 400 published to date.52,53 In general, these studies have 1) reinforced the importance of the genetic variation that underlies phenotypic variation, 2) illustrated that genetic variation almost always results from multiple Mendelian mutations rather than a single mutation, and 3) demonstrated that genetic variation typically explains only a small fraction of the observed phenotypic variation.4,80,114 Within the field of neuroscience, recent GWA studies have provided insights into the genetic basis of many common neurological diseases and disorders (Table 1). Such studies have been conducted for conditions including Parkinson disease,39,67,93,113 malignant gliomas,23,95,109,143 multiple sclerosis,7,29,90 Alzheimer disease,15,18,41,72,73 autism,3,8,22,45,75,107,133 schizophrenia,25,119,138 lumbar disc disease,131 idiopathic scoliosis,43,140 and restless-leg syndrome.36,139

TABLE 1:

Genome-wide association studies of common diseases and disorders of the brain, spine, and nervous system*

Condition StudiedReference
neurooncology
glioma109
high-grade glioma143
neuroblastoma77
high-risk neuroblastoma cerebrovascular disease24
intracranial aneurysm20
hemorrhagic stroke9
ischemic stroke47, 79, 144
neurological disease
age-related macular degeneration62
Alzheimer disease2, 15, 18, 26, 41, 72, 73, 96, 103, 135, 136
amyotrophic lateral sclerosis21, 27, 34, 35, 38, 63, 105, 129, 130
Creutzfeldt-Jakob susceptibility81
multiple sclerosis6, 7, 12, 28, 29, 57, 59, 90
Parkinson disease42, 76, 93
progressive supranuclear palsy82
restless legs syndrome36, 104, 139
brain function & physiology
cognition97, 108
memory94
pain60
brain vol12
sleep46
attention deficit hyperactivity disorder5, 64, 65, 68, 83, 87, 117
autism75, 133
bipolar disorder13, 40, 51, 106, 115, 116, 137, 145
major depressive disorder85, 120
panic disorder91
neuroticism111, 128
schizophrenia25, 58, 61, 66, 88, 89, 110, 112, 118, 119, 121, 132, 138
personality dimensions123
addiction
alcohol dependence125
methamphetamine dependence126
nicotine dependence17, 19, 37, 124, 127

* Compiled from information contained in the National Human Genome Research Institute GWA study catalog.52,53

Without doubt, the field of genomics is going to play a central role in the clinical care of the future neurological patient. Physicians will therefore need to have at least a basic understanding of the research tools and concepts routinely used in this field. The purpose of this review is to familiarize the clinician with the fundamentals of GWA studies and to highlight their potential clinical application.

Genome-Wide Association Models

Recent progress toward understanding human genetic variation has advanced genetic-linkage and genetic-association studies from candidate-gene analyses to GWA studies.98 The power of the GWA approach lies in the breadth and number of genetic variants tested during the course of a study. The GWA study also has the advantage of being an unbiased search for the genetic variants associated with a particular disease and therefore offers the possibility of discovering new associations of genes and pathways with diseases.4

Genome-Wide Association Theory

A phenotype is an observable trait produced by an underlying genotype. The genetic differences among individuals in the human population are commonly called “mutations” and most frequently are single-nucleotide changes within the DNA sequences of genes.11,80 Many mutations are expected to be harmful and thus to be removed from the population by natural selection. Fewer mutations are expected to be beneficial or fitness neutral, and such mutations can persist in a population over time while proceeding to fixation (every individual carries it) or loss (lost by the actions of selection and drift); with either fate, any genetic variation is lost and therefore not observable. Prior to being fixed or lost, a mutation is carried by only part of the population and is referred to as a “polymorphism.” Single-nucleotide polymorphisms are commonly used as genetic markers in GWA studies and are the focus of this review—although alternative genetic features can be used for GWA studies. These alternatives may be particularly useful for GWA studies of psychiatric disorders, in which genetic features such as gene copynumber variations14,45,107,119 and gross chromosomal rearrangements8 appear to be important to the genetic etiology of this class of diseases.

The aim of a typical GWA study is to associate one or more SNPs with a particular disease phenotype (Fig. 1). The tested SNPs are not expected to be the causal genetic factors; rather, they are used to mark (“tag”) particular regions of chromosomes that likely contain many genetic variants in high linkage disequilibrium with the tested SNP. Linkage disequilibrium occurs when 2 or more alleles at distinct genetic loci occur together significantly more or less frequently than expected by chance based on the constituent allele frequencies. Single-nucleotide polymorphisms are therefore an efficient way to screen many mutations at once, and thus identify chromosomal locations within which the true causal variants are likely to reside. In fact, the true causal variant may not itself be a nucleotide mutation—it may be an insertion or deletion mutation.

Fig. 1.
Fig. 1.

Schematic representation of a GWA study. Left: A mutation (black circles) entering and spreading through a population over time. Right: A contingency table in which the mutation is significantly more abundant among the cases than the controls.

Genome-wide association studies differ in their assumptions about the type of genetic variation underlying the disease of interest. Most such studies operate under the “common disease/common variant” hypothesis, which proposes that phenotypic variation is the result of many common SNPs, each of which contributes only a modest effect.100 An alternative hypothesis, the “multiple rare variant” hypothesis, proposes that phenotypic variation results from the potentially more modest effects of many rare SNPs.98 These two hypotheses are not necessarily mutually exclusive—the variation underlying a disease may fall into both categories; rather they are intended to guide the design, analysis, and interpretation of GWA studies.

Genetic variation can interact differently to produce the particular disease under study. It can interact additively, in which case alleles confer a mean effect that does not depend on the state of other alleles, or nonadditively, which means that the effect of an allele results from dominance effects and epistatic interactions with other loci. Additive genetic variation is most commonly considered in GWA studies, in which SNPs are typically considered as independent entities.4 Recently, there has been significant progress toward developing statistical models that assess nonadditive genetic effects, and these models promise to greatly enhance the scope and power of GWA studies.30,32

Genome-Wide Association Study Design

The design of a GWA study primarily depends on the specific project goals, but practical factors such as budget and time must be considered as well. The most common design for a GWA study is the case-control format, in which there is a cohort of cases (affected individuals) and a cohort of controls (unaffected individuals). The individuals in the case cohort are assumed to have a greater prevalence of disease-causing alleles than those in the control cohort,80 a hypothesis that can be assessed by one or more statistical methods discussed below.

Power is the most critical aspect to consider when designing a GWA study. Study power determines the likelihood that a trial will detect significant genetic differences between case and control populations, if any such differences truly exist. Sample size profoundly affects the study power, and, in general, the largest sample that is feasible to genotype should be used. Study power can also be increased by carefully controlling for any population substructure and by cautiously selecting the control population.4,11,80

The individuals in the case and control populations are assumed to be ”unrelated,” which means that their ancestral relationships are distant and therefore unknown.11 However, the case and control individuals should not be so unrelated that there are distinct subgroups that share a common ancestry (for example, Western European or African heritage). When such subgroups exist, there is said to be population substructure or population stratification, which can be especially problematic when correlated with the case-control delineation. Population stratification can produce false-positive signals because some genetic variants can occur at different frequencies in the two groups as a result of ancestry, even though they are unrelated to the disease under study. Many statistical methods exist to control for population stratification11,80 and have been implemented in common software packages such as PLINK.99 Furthermore, recent evidence has suggested that many concerns about population stratification may be overinflated; however, every effort should be made to eliminate its effects.11,80,137

Selection of the control population is crucial to the success of a GWA study. Common-control populations, which are genotyped populations that can be used across studies, have been successfully utilized in GWA studies.137 The main concerns about common controls are the presence of population stratification and the potential loss of power given the presence of latent (undiagnosed) disease in the control population. A study-specific control population is almost always ideal, but can greatly increase the time and cost of a GWA study (controls must be genotyped) and is subject to selection bias when differences, in addition to the disease under study, exist between cases and controls.80 Historical controls, which are previously genotyped individuals used in current studies, have the potential to reduce study power when there is significant genetic divergence between extant cases and historical controls. Historical controls may be the best option available, however, and recent evidence has suggested that some of the drawbacks may be overcome by increases in sample size.137

An alternative study design is “family-based association” in which genetic tests are performed within families. Family-based association studies offer strong control of background genetic differences at the cost of overall study power. The additional costs associated with studying large numbers of families and recent improvements in statistical methods to control for population stratification make family-based studies attractive for only specialized circumstances.80

Genome-Wide Association Study Methods

Genotype Calling

Genotyping is the phase in which an instrument determines (“calls”) the genotype, or state of both alleles, at every SNP locus tested. Genotype calling is typically performed in a high-throughput manner by using highly automated instruments, such as those commercially available from Affymetrix and Illumina. Commercial genotyping instruments use high-density microarrays of SNPs (“SNP chips,” informally) that have been identified through projects such as the International HapMap Consortium.56

The latest generation of commercial SNP genotyping platforms can routinely test ~ 2 million genetic loci in a single assay. The loci include ~ 1 million SNPs and an approximately equal number of copy-number variants; a state-of-the-art instrument with robotic automation can generate ~ 40–50 million genotypes per day. However, investigators in most published studies have used earlier genotyping platforms that tested ~ 300,000–500,000 SNPs. One advantage of commercial genotyping platforms is that the selection of high-quality SNPs to test is no longer a challenge left to individual researchers. The latest genotyping platforms include SNPs that are spaced, on average, 1–2 kilobases apart and cover ~ 95% of the human genome, including sex chromosomes and mitochondrial genomes. Current genotyping instruments poorly sample regions of the human genome with infrequent restriction enzyme sites, which precludes isolating SNPs in these regions. An additional benefit of commercial platforms is that they lead to further standardization of the SNPs tested across studies.

Quality Control of Genotype Data

Genome-wide association studies produce enormous amounts of data, and therefore data quality is of paramount importance. Rigorous quality control measures must be implemented at each stage of the study—from DNA extraction and amplification through to analyzing and interpreting the data. A common source of errors is genotype calling, which must strike a balance between stringency and call rate. If base calling is excessively stringent, then most markers will have a low call rate, which can inflate the false-positive rate.11 On the other hand, overly relaxed genotype calling will produce significant numbers of miscalled genotypes. Finally, a threshold call rate must be selected, and SNPs whose call rates fall below this threshold should be excluded from consideration. Individuals with low overall call rates should be removed because such rates suggest that their DNA samples may be problematic.

Each marker should be tested in the control population to ensure it is in HWE, which describes the expected genotype frequencies (based on allele frequencies) in a randomly mating population in the absence of selection, mutation, and migration.50 Extreme deviations from HWE might be symptomatic of genotype calling errors, and such markers can generally be removed with impunity.80 On the other hand, moderate deviations from HWE may be expected in the cases, and therefore the inclusion threshold should not remove these markers because they may provide additional information when searching for disease-associated SNPs.11,142 The HWE inclusion threshold must be carefully selected to balance overall inclusiveness with the purging of potentially problematic markers. A flexible and powerful approach is the use of the observed distribution of HWE values for each marker to determine an appropriate threshold for inclusion.137

There are a few remaining aspects to consider when implementing quality-control measures. Individuals whose genotype does not agree with their stated ethnicity or sex should not be included. Methods should be implemented to ensure all individuals in the study share a common ancestral background (for example, all of Western European descent) to minimize population substructure. Individuals with evidence of genetic syndromes (for example, fragile-X syndrome or trisomy 21) should also be excluded. In family-based studies, markers with unusually high rates of Mendelian errors—potentially a sign of frequent miscalling of genotypes—should be discarded.11,80

Testing for Association

After obtaining a high-quality set of genotypes, a GWA study typically moves into the analysis phase during which SNPs are tested for their association with the phenotype of interest. This phase essentially consists of applying one or more statistical tests of association to each marker tested. There are several statistical models available, although the viable options may be constrained by the study design (for example, phenotypic variable). The statistics underlying these methods can be quite difficult to understand, and therefore only the general features of the most common tests are discussed herein. The interested reader is encouraged to investigate one of many recent reviews for more detailed treatments of GWA study statistics.11,134

A significant problem with many tests of association is the extensive multiple-testing burden;11,80,134 this problem becomes more significant as the number of SNPs tested increases. Multiple-testing burden refers to the increased false-positive rate that results from performing multiple independent tests on the same data set. Several methods exist to correct the p values for multiple testing and reduce the likelihood of false-positive signals.10,54,134 The most conservative approach is a Bonferroni correction in which individual p values are each multiplied by N (the number of SNPs tested) to maintain the false-positive rate at a desired level. More flexible and relaxed falsediscovery rate (FDR) calculations can be used as well.16 Such corrections yield a greater number of potential SNPs positively associated with the disease under study, but also create a greater risk of including false-positive associations. Simulation models can also be used to empirically determine a threshold p value that appropriately balances overall inclusiveness with false-positive risk. Under any multiple-testing correction approach, the required p value for attaining significance for any particular SNP is exceedingly small because modern GWA studies effectively conduct hundreds of thousands of statistical tests.

The most basic test for a single SNP in a case-control design is that for independence between the 2 rows (cases and controls) and 3 columns (genotypes) of a contingency table.11 A chi-square or Fisher exact test would be an appropriate statistical test. Alternatively, one could use a Cochran-Armitage test, which is based on the differences in allele counts rather than genotype counts, and may be more powerful for complex traits for which the contribution of individual SNPs is thought to be roughly additive.80

More advanced statistical approaches based on regression modeling are routinely used when analyzing GWA data. Logistic regression models are suitable for case-control studies in which the phenotype is binary (for example, presence or absence of a disease), whereas a linear regression or an ANOVA model is suitable for testing continuous phenotypes (for example, degree of spine curvature in a scoliosis study). One advantage of using regression models is that epistatic interactions between SNPs can be readily incorporated into the model, as can other covariates such as age or sex. An important theoretical consideration is that regression-based methods assume that phenotypes are observed prospectively, whereas most GWA studies select individuals based on phenotype and then determine the genotype.11

Family-based association studies model SNP flow through pedigrees. Software packages such as MERLIN and LAMP (freely available at http://csg.sph.umich.edu) are widely used suites of programs for the analysis of large pedigree data sets.1 MERLIN is designed to test quantitative trait association, whereas LAMP69,70 is intended for discrete trait association. Both software programs use likelihood statistics to assess the relative probabilities of alternative patterns of gene flow through the pedigrees in the data set.

Interpretation of Results

Once a set of associated SNPs has been identified, a search for additional evidence to support the observed association must begin. Ideally, the study would be replicated with a different population of cases and controls to ensure that the same SNPs would be identified in these new individuals. Such replication may not be feasible, however, for reasons such as cost or time.

Vendor-supplied annotation files can be used to determine the physical and genomic location of each SNP within the genome. The annotation for each SNP may also include additional information, such as whether the SNP is located within a gene (intronic/exonic) or an intergenic region. Using this information, one can search databases such as Entrez for additional DNA, RNA, or protein sequence information, or Medline can be queried for other studies corroborating an observed SNP association. For example, genes adjacent to an associated SNP can be checked to determine if any have prior evidence linking them to the disease under study, which could identify genomic locations that might be good targets for more extensive sequencing efforts.

Limitations of GWA Studies

Theoretical Limitations

There is appreciable work to be done on the practical and analytical front for improving the current generation of GWA methods. First and foremost, even the most powerful GWA study can only explain a small percentage of the observed phenotypic variation. This fact partially emphasizes the need for models and methods that explicitly consider the interactions of genes with their environment. Gene-by-environment interactions present a significant practical challenge to GWA studies; it is extremely difficult to determine the relevant times and environmental variables to measure.31,33 In the future, statistical methods that consider the interactions between multiple genetic variants must be advanced.32,55 The power to model and detect epistatic interactions among mutations in a genome offers great hope to more completely explain phenotypic variation and uncover a greater number of loci. Loci that are not strongly associated with a trait individually may be very strongly associated when considered in combination with other mutations.

Genome-wide association studies only test for a statistically significant association of a marker with a trait and cannot make a causal statement. The direct test for causality between associated SNPs and their flanking genes would involve the mutation of each candidate gene and the determination of the resulting phenotype. Obviously, this is impossible with humans, but other organisms, such as yeast, mice, and primates, may be good surrogate models for the human disease. One or more of these model organisms might be useful in refining the set of associated SNPs and further understanding the genes and pathways mutated in diseased individuals. It is a long road from a GWA study to determining which genes and pathways are defective in the diseased state; however, GWA analysis can be an important first step in identifying otherwise unknown genes and pathways involved in diseases.

Practical Limitations

At present, the most significant practical limitation for GWA studies is cost. Genotyping is expected to cost ~ $500 per individual, not considering the necessary instrumentation. Current genotyping instruments require a significant initial investment of roughly $300,000. Alternatively, research service providers can be contracted for some or all stages of data production (DNA extraction, genotyping, and so forth). Significant costs may also be associated with obtaining sufficient high-quality data, especially for rare diseases with a low incidence. Moreover, significant time and resources may be required to generate the genetic data (genotyping) for the control population.

The large amounts of data generated during the course of a GWA study must be processed on relatively powerful computers with a large storage capacity. Additionally, computer software may need to be developed inhouse to store and analyze the data, or existing software programs may need to be acquired. Commercial software packages are available to analyze the data, and there are freely available options as well.

Clinical Applications of GWA Studies

Genetic Testing

In addition to identifying a set of alleles associated with a particular disease, the GWA approach can be used to identify risk alleles, that is, those that appear to confer an increased risk for developing a disease. Genetic risks have been based on family history or candidate gene testing. Genome-wide association approaches may one day be able to provide a comprehensive picture of an individual's risk for developing any of a wide range of disorders.

Although it has been proposed that risk calculations based on GWA data might ultimately replace those based on family history,102 genetic tests have gained only limited acceptance in the medical community.48,49 The clinical utility of genetic testing has been difficult to demonstrate for a variety of reasons, but significant effort is currently being expended to overcome these obstacles. Single-nucleotide polymorphisms implicated in the most powerful GWA studies typically explain only a small fraction of the observed variation for a disease, which partly stems from a combination of methodological and practical limitations. Genome-wide association tests carry significant direct costs. It has been difficult to demonstrate their cost-effectiveness because 1) risk loci have typically low penetrance, 2) the benefits of genetic testing are hard to quantify because treatment for the disease may improve over time or a patient's adherence to preventive measures may decline over time, and 3) there is a potential cost to the patient in terms of stigmatization by society or psychological stress resulting from his or her knowledge about potential future disease.101 Nonetheless, GWA screens have been conducted for a number of diseases of neurosurgical importance.114

Malignant glioma is the most common type of primary brain tumor, and the prognosis remains poor despite surgical and oncological advancements.43,74 As in other types of cancer, there is great interest in identifying susceptibility loci for these aggressive tumors. Such loci would allow for the estimation of the risk of developing malignant glioma in a particular individual during his or her lifetime. Significantly at-risk individuals could be monitored more closely, with the hope that increased surveillance might lead to earlier detection and better treatment outcomes.

Recently, authors of a large GWA study were able to identify 5 genetic loci that appear to confer a significant risk for the development of malignant gliomas.109 The SNPs identified potentially support the importance of the cyclin-dependent kinase inhibitor 2A–cyclin-dependent kinase 4 (CDKN2A-CDK4) signaling pathway, as well as the genes involved in genomic stability and telomere preservation. A few of the genes and chromosomal regions have been implicated in other types of cancer. Interestingly, 2 of these loci appear to be associated with a greater risk for the development of high-grade gliomas.143 One of the chromosomal regions identified is the 9p21 region in which the CDKN2B gene resides. This gene participates in the control of cell division and is frequently deleted in high-grade gliomas. The other identified chromosomal region is 20q13 in which RTEL1 is located. The RTEL1 gene encodes a DNA helicase that is critical for the maintenance of telomere length.

Idiopathic scoliosis is the most common childhood spine disorder, affecting almost 3% of children globally.140,141 The disease appears to cluster within families, although the precise mode of inheritance has yet to be determined. A recent GWA study revealed significant linkage of a region of chromosome 8 with idiopathic scoliosis, specifically with the CHD7 gene.44

Lumbar disc disease is a significant source of disability, and one of the most common disorders seen in neurosurgical practices.92 A recent small-scale GWA study demonstrated an association between a region of chromosome 21 and lumbar disc disease.131 The results of this study are encouraging, although larger and more densely sampled GWA studies must be conducted to corroborate the data. Furthermore, lumbar disc disease may present special problems relating to phenotype scoring since many individuals carry asymptomatic disc herniations.

Diagnostics, Tumor Grading, and Prognosis

The GWA study framework also holds great promise for molecular diagnostics in medicine. Molecular diagnostics use genetic markers to ascertain the clinical status of a patient's tissue sample. For example, a patient's brain tumor tissue sample is traditionally sent for anatomical pathology analysis. In the future, a patient's specimen may be sent to a genetics laboratory for analysis. The potential advantages of genetics-based diagnostics are that such analyses are typically unambiguous, unbiased, and completely objective. There is still much work to be done for this field to move from potential applications to effective clinical solutions.

Recently, significant progress has been made toward developing molecular diagnostics for brain tumors, in particular malignant gliomas. For example, there is a great deal of interest in developing tumor-grading methods based on the genetic states of tumors rather than their anatomical appearance.86,122 These methods would use genetic markers as an adjunct to traditional grading of tumors by a pathology lab. There is also significant interest in developing tumor prognosis classifiers based on genetic markers, with some recent success.95 Progress has been made in developing artificial-intelligence classifier models that predict survival time based on the genomic expression patterns of select genetic loci.71,78

Personalized Medicine and Tumor Drugs

Lastly, we note that GWA studies may one day lead to clinical regimens that are individually tailored to each patient. For instance, genetic testing is routinely being done for oligodendrogliomas, with the 1p/19q screen for chemotherapeutic effectiveness. However, the GWA approach permits screening for more markers in a single sweep and offers much more precision in associating genotypes with clinically important phenotypes. In addition, understanding genes, as opposed to chromosome arms, will allow for a deeper understanding into the molecular genetic mechanisms behind drug function and metabolism, and lead to better therapies in the future.84,122

Conclusions

Genome-wide association studies hold great promise for decrypting the complex genetic architecture of many diseases. Furthermore, GWA approaches have the potential to power a new generation of genetic tests, which one day may be used to estimate an individual's risk for a particular disease or to predict which chemotherapeutic agent or biological treatment will be most effective. As the costs decline and the analytical methods become more powerful, genetic testing may become a feasible option for patients seeking to understand the health risks conferred by the mutations residing in their DNA. Given their advancement to date, the current generation of neurosurgeons and neurologists can expect to use patient genetics as part of their clinical decision-making at the bedside.

Disclosure

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

References

  • 1

    Abecasis GRCherny SSCookson WOCardon LR: Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:971012002

  • 2

    Abraham RMoskvina VSims RHollingworth PMorgan AGeorgieva L: A genome-wide association study for lateonset Alzheimer's disease using DNA pooling. BMC Med Genomics 1:442008

  • 3

    Abrahams BSGeschwind DH: Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet 9:3413552008

  • 4

    Altshuler DDaly MJLander ES: Genetic mapping in human disease. Science 322:8818882008

  • 5

    Anney RJLasky-Su JO'Dúshláine CKenny ENeale BMMulligan A: Conduct disorder and ADHD: evaluation of conduct problems as a categorical and quantitative trait in the international multicentre ADHD genetics study. Am J Med Genet B Neuropsychiatr Genet 147B:136913782008

  • 6

    Aulchenko YSHoppenbrouwers IARamagopalan SVBroer LJafari NHillert J: Genetic variation in the KIF1B locus influences susceptibility to multiple sclerosis. Nat Genet 40:140214032008

  • 7

    Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene): Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 41:8248282009

  • 8

    Autism Genome Project Consortium: Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39:3193282007

  • 9

    Bae JSCheong HSKim JOLee SOKim EMLee HW: Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population. Biochem Biophys Res Commun 373:5935962008

  • 10

    de Bakker PIYelensky RPe'er IGabriel SBDaly MJAltshuler D: Efficiency and power in genetic association studies. Nat Genet 37:121712232005

  • 11

    Balding DJ: A tutorial on statistical methods for population association studies. Nat Rev Genet 7:7817912006

  • 12

    Baranzini SEWang JGibson RAGalwey NNaegelin YBarkhof F: Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 18:7677782009

  • 13

    Baum AEAkula NCabanero MCardona ICorona WKlemens B: A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol Psychiatry 13:1972072008

  • 14

    Beckmann JSEstivill XAntonarakis SE: Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability. Nat Rev Genet 8:6396462007

  • 15

    Beecham GWMartin ERLi YJSlifer MAGilbert JRHaines JL: Genome-wide association study implicates a chromosome 12 risk locus for late-onset Alzheimer disease. Am J Hum Genet 84:35432009

  • 16

    Benjamini YHochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc 57:2893001995

  • 17

    Berrettini WYuan XTozzi FSong KFrancks CChilcoat H: Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol Psychiatry 13:3683732008

  • 18

    Bertram LLange CMullin KParkinson MHsiao MHogan MF: Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE. Am J Hum Genet 83:6236322008

  • 19

    Bierut LJMadden PABreslau NJohnson EOHatsukami DPomerleau OF: Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet 16:24352007

  • 20

    Bilguvar KYasuno KNiemelä MRuigrok YMvon Und Zu Fraunberg Mvan Duijn CM: Susceptibility loci for intracranial aneurysm in European and Japanese populations. Nat Genet 40:147214772008

  • 21

    Blauw HMVeldink JHvan Es MAvan Vught PWSaris CGvan der Zwaag B: Copy-number variation in sporadic amyotrophic lateral sclerosis: a genome-wide screen. Lancet Neurol 7:3193262008

  • 22

    Bucan MAbrahams BSWang KGlessner JTHerman EISonnenblick LI: Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genet 5:e10005362009

  • 23

    Cancer Genome Atlas Research Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:106110682008

  • 24

    Capasso MDevoto MHou CAsgharzadeh SGlessner JTAttiyeh EF: Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat Genet 41:7187232009

  • 25

    Cardno AGHolmans PARees MIJones LAMcCarthy GMHamshere ML: A genomewide linkage study of age at onset in schizophrenia. Am J Med Genet 105:4394452001

  • 26

    Carrasquillo MMZou FPankratz VSWilcox SLMa LWalker LP: Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease. Nat Genet 41:1921982009

  • 27

    Chiò ASchymick JCRestagno GScholz SWLombardo FLai SL: A two-stage genome-wide association study of sporadic amyotrophic lateral sclerosis. Hum Mol Genet 18:152415322009

  • 28

    Comabella MCraig DWCamina-Tato MMorcillo CLopez CNavarro A: Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms. PLoS One 3:e34902008

  • 29

    Comabella MCraig DWMorcillo-Suárez CRío JNavarro AFernández M: Genome-wide scan of 500,000 singlenucleotide polymorphisms among responders and nonresponders to interferon beta therapy in multiple sclerosis. Arch Neurol 66:9729782009

  • 30

    Cordell HJ: Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11:246324682002

  • 31

    Cordell HJ: Estimation and testing of gene-environment interactions in family-based association studies. Genomics 93:592009

  • 32

    Cordell HJ: Genome-wide association studies: detecting genegene interactions that underlie human diseases. Nat Rev Genet 10:3924042009

  • 33

    Cordell HJBarratt BJClayton DG: Case/pseudocontrol analysis in genetic association studies: a unified framework for detection of genotype and haplotype associations, gene-gene and gene-environment interactions, and parent-of-origin effects. Genet Epidemiol 26:1671852004

  • 34

    Cronin SBerger SDing JSchymick JCWashecka NHernandez DG: A genome-wide association study of sporadic ALS in a homogenous Irish population. Hum Mol Genet 17:7687742008

  • 35

    Cronin STomik BBradley DGSlowik AHardiman O: Screening for replication of genome-wide SNP associations in sporadic ALS. Eur J Hum Genet 17:2132182009

  • 36

    Desautels ATurecki GMontplaisir JSequeira AVerner ARouleau GA: Identification of a major susceptibility locus for restless legs syndrome on chromosome 12q. Am J Hum Genet 69:126612702001

  • 37

    Drgon TMontoya IJohnson CLiu QRWalther DHamer D: Genome-wide association for nicotine dependence and smoking cessation success in NIH research volunteers. Mol Med 15:21272009

  • 38

    Dunckley THuentelman MJCraig DWPearson JVSzelinger SJoshipura K: Whole-genome analysis of sporadic amyotrophic lateral sclerosis. N Engl J Med 357:7757882007

  • 39

    Farrer MJHaugarvoll KRoss OAStone JTMilkovic NMCobb SA: Genomewide association, Parkinson disease, and PARK10. Am J Hum Genet 78:10841088author reply 1092–10942006

  • 40

    Ferreira MAO'Donovan MCMeng YAJones IRRuderfer DMJones L: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40:105610582008

  • 41

    Feulner TMLaws SMFriedrich PWagenpfeil SWurst SHRiehle C: Examination of the current top candidate genes for AD in a genome-wide association study. Mol Psychiatry [epub ahead of print]2009

  • 42

    Fung HCScholz SMatarin MSimón-Sánchez JHernandez DBritton A: Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 5:9119162006

  • 43

    Furnari FBFenton TBachoo RMMukasa AStommel JMStegh A: Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev 21:268327102007

  • 44

    Gao XGordon DZhang DBrowne RHelms CGillum J: CHD7 gene polymorphisms are associated with susceptibility to idiopathic scoliosis. Am J Hum Genet 80:9579652007

  • 45

    Glessner JTWang KCai GKorvatska OKim CEWood S: Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459:5695732009

  • 46

    Gottlieb DJO'Connor GTWilk JB: Genome-wide association of sleep and circadian phenotypes. BMC Med Genet 8:1 SupplS92007

  • 47

    Gretarsdottir SThorleifsson GManolescu AStyrkarsdottir UHelgadottir AGschwendtner A: Risk variants for atrial fibrillation on chromosome 4q25 associate with ischemic stroke. Ann Neurol 64:4024092008

  • 48

    Grosse SDKhoury MJ: What is the clinical utility of genetic testing?. Genet Med 8:4484502006

  • 49

    Grosse SDRogowski WHRoss LFCornel MCDondorp WJKhoury MJ: Population screening for genetic disorders in the 21st century: evidence, economics, and ethics. Public Health Genomics [epub ahead of print]2009

  • 50

    Hartl DLClark AG: Principles of Population Genetics Sunderland, MASinauer2007

  • 51

    Hattori EToyota TIshitsuka YIwayama YYamada KUjike H: Preliminary genome-wide association study of bipolar disorder in the Japanese population. Am J Med Genet B Neuropsychiatr Genet [epub ahead of print]2009

  • 52

    Hindorff LAJunkins HAMehta JPManolio TA: A catalog of published genome-wide association studies. www.genome.gov/gwastudies [Accessed October 27 2009]

  • 53

    Hindorff LASethupathy PJunkins HARamos EMMehta JPCollins FS: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:936293672009

  • 54

    Hirschhorn JNDaly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:951082005

  • 55

    Hoggart CJWhittaker JCDe Iorio MBalding DJ: Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet 4:e10001302008

  • 56

    International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 449:8518612007

  • 57

    International Multiple Sclerosis Genetics Consortium: Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357:8518622007

  • 58

    International Schizophrenia Consortium: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:7487522009

  • 59

    De Jager PLJia XWang Jde Bakker PIOttoboni LAggarwal NT: Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 41:7767822009

  • 60

    Kim HRamsay ELee HWahl SDionne RA: Genomewide association study of acute post-surgical pain in humans. Pharmacogenomics 10:1711792009

  • 61

    Kirov GZaharieva IGeorgieva LMoskvina VNikolov ICichon S: A genome-wide association study in 574 schizophrenia trios using DNA pooling. Mol Psychiatry 14:7968032009

  • 62

    Klein RJZeiss CChew EYTsai JYSackler RSHaynes C: Complement factor H polymorphism in age-related macular degeneration. Science 308:3853892005

  • 63

    Landers JEMelki JMeininger VGlass JDvan den Berg LHvan Es MA: Reduced expression of the kinesin-associated protein 3 (KIFAP3) gene increases survival in sporadic amyotrophic lateral sclerosis. Proc Natl Acad Sci U S A 106:900490092009

  • 64

    Lasky-Su JAnney RJNeale BMFranke BZhou KMaller JB: Genome-wide association scan of the time to onset of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:135513582008

  • 65

    Lasky-Su JNeale BMFranke BAnney RJZhou KMaller JB: Genome-wide association scan of quantitative traits for attention deficit hyperactivity disorder identifies novel associations and confirms candidate gene associations. Am J Med Genet B Neuropsychiatr Genet 147B:134513542008

  • 66

    Lencz TMorgan TVAthanasiou MDain BReed CRKane JM: Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia. Mol Psychiatry 12:5725802007

  • 67

    Lesage SBrice A: Parkinson's disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet 18:R1R48592009

  • 68

    Lesch KPTimmesfeld NRenner TJHalperin RRöser CNguyen TT: Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm 115:157315852008

  • 69

    Li MBoehnke MAbecasis GR: Efficient study designs for test of genetic association using sibship data and unrelated cases and controls. Am J Hum Genet 78:7787922006

  • 70

    Li MBoehnke MAbecasis GR: Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal. Am J Hum Genet 76:9349492005

  • 71

    Li AWalling JAhn SKotliarov YSu QQuezado M: Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. Cancer Res 69:209120992009

  • 72

    Li HWetten SLi LSt Jean PLUpmanyu RSurh L: Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease. Arch Neurol 65:45532008

  • 73

    Liu FArias-Vásquez ASleegers KAulchenko YSKayser MSanchez-Juan P: A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population. Am J Hum Genet 81:17312007

  • 74

    Louis DN: Molecular pathology of malignant gliomas. Annu Rev Pathol 1:971172006

  • 75

    Ma DSalyakina DJaworski JMKonidari IWhitehead PLAndersen AN: A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann Hum Genet 73:2632732009

  • 76

    Maraganore DMde Andrade MLesnick TGStrain KJFarrer MJRocca WA: High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet 77:6856932005

  • 77

    Maris JMMosse YPBradfield JPHou CMonni SScott RH: Chromosome 6p22 locus associated with clinically aggressive neuroblastoma. N Engl J Med 358:258525932008

  • 78

    Marko NFToms SABarnett GHWeil R: Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study. Genomics 91:3954062008

  • 79

    Matarín MBrown WMScholz SSimón-Sánchez JFung HCHernandez D: A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol 6:4144202007

  • 80

    McCarthy MIAbecasis GRCardon LRGoldstein DBLittle JIoannidis JP: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:3563692008

  • 81

    Mead SPoulter MUphill JBeck JWhitfield JWebb TE: Genetic risk factors for variant Creutzfeldt-Jakob disease: a genome-wide association study. Lancet Neurol 8:57662009

  • 82

    Melquist SCraig DWHuentelman MJCrook RPearson JVBaker M: Identification of a novel risk locus for progressive supranuclear palsy by a pooled genomewide scan of 500,288 single-nucleotide polymorphisms. Am J Hum Genet 80:7697782007

  • 83

    Mick ENeale BMiddleton FAMcGough JJFaraone SV: Genome-wide association study of response to methylphenidate in 187 children with attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:141214182008

  • 84

    Mladkova NChakravarti A: Molecular profiling in glioblastoma: prelude to personalized treatment. Curr Oncol Rep 11:53612009

  • 85

    Muglia PTozzi FGalwey NWFrancks CUpmanyu RKong XQ: Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts. Mol Psychiatry [epub ahead of print]2008

  • 86

    Nakamura MShimada KIshida ENakase HKonishi N: Genetic analysis to complement histopathological diagnosis of brain tumors. Histol Histopathol 22:3273352007

  • 87

    Neale BMLasky-Su JAnney RFranke BZhou KMaller JB: Genome-wide association scan of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:133713442008

  • 88

    Need ACGe DWeale MEMaia JFeng SHeinzen EL: A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genet 5:e10003732009

  • 89

    O'Donovan MCCraddock NNorton NWilliams HPeirce TMoskvina V: Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 40:105310552008

  • 90

    Oksenberg JRBaranzini SESawcer SHauser SL: The genetics of multiple sclerosis: SNPs to pathways to pathogenesis. Nat Rev Genet 9:5165262008

  • 91

    Otowa TYoshida ESugaya NYasuda SNishimura YInoue K: Genome-wide association study of panic disorder in the Japanese population. J Hum Genet 54:1221262009

  • 92

    Paassilta PLohiniva JGöring HHPerälä MRäinä SSKarppinen J: Identification of a novel common genetic risk factor for lumbar disk disease. JAMA 285:184318492001

  • 93

    Pankratz NWilk JBLatourelle JCDeStefano ALHalter CPugh EW: Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum Genet 124:5936052009

  • 94

    Papassotiropoulos AStephan DAHuentelman MJHoerndli FJCraig DWPearson JV: Common Kibra alleles are associated with human memory performance. Science 314:4754782006

  • 95

    Parsons DWJones SZhang XLin JC-HLeary RJAngenendt P: An integrated genomic analysis of human glioblastoma multiforme. Science 321:180718122008

  • 96

    Poduslo SEHuang RHuang JSmith S: Genome screen of late-onset Alzheimer's extended pedigrees identifies TRPC4AP by haplotype analysis. Am J Med Genet B Neuropsychiatr Genet 150B:50552009

  • 97

    Poduslo SEHuang RSpiro A: A genome screen of successful aging without cognitive decline identifies LRP1B by haplotype analysis. Am J Med Genet B Neuropsychiatr Genet [epub ahead of print]2009

  • 98

    Pritchard JK: Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet 69:1241372001

  • 99

    Purcell SNeale BTodd-Brown KThomas LFerreira MABender D: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:5595752007

  • 100

    Reich DELander ES: On the allelic spectrum of human disease. Trends Genet 17:5025102001

  • 101

    Rogowski W: Current impact of gene technology on healthcare. A map of economic assessments. Health Policy 80:3403572007

  • 102

    Rogowski W: Genetic screening by DNA technology: a systematic review of health economic evidence. Int J Technol Assess Health Care 22:3273372006

  • 103

    Schjeide BMHooli BParkinson MHogan MFDiVito JMullin K: GAB2 as an Alzheimer disease susceptibility gene: follow-up of genomewide association results. Arch Neurol 66:2502542009

  • 104

    Schormair BKemlink DRoeske DEckstein GXiong LLichtner P: PTPRD (protein tyrosine phosphatase receptor type delta) is associated with restless legs syndrome. Nat Genet 40:9469482008

  • 105

    Schymick JCScholz SWFung HCBritton AArepalli SGibbs JR: Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 6:3223282007

  • 106

    Scott LJMuglia PKong XQGuan WFlickinger MUpmanyu R: Genome-wide association and meta-analysis of bipolar disorder in individuals of European ancestry. Proc Natl Acad Sci U S A 106:750175062009

  • 107

    Sebat JLakshmi BMalhotra DTroge JLese-Martin CWalsh T: Strong association of de novo copy number mutations with autism. Science 316:4454492007

  • 108

    Seshadri SDeStefano ALAu RMassaro JMBeiser ASKelly-Hayes M: Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study. BMC Med Genet 8:1 SupplS152007

  • 109

    Shete SHosking FJRobertson LBDobbins SESanson MMalmer B: Genome-wide association study identifies five susceptibility loci for glioma. Nat Genet 41:8999042009

  • 110

    Shi JLevinson DFDuan JSanders ARZheng YPe'er I: Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460:7537572009

  • 111

    Shifman SBhomra ASmiley SWray NRJames MRMartin NG: A whole genome association study of neuroticism using DNA pooling. Mol Psychiatry 13:3023122008

  • 112

    Shifman SJohannesson MBronstein MChen SXCollier DACraddock NJ: Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women. PLoS Genet 4:e282008

  • 113

    Simón-Sánchez JScholz SMatarin Mdel MFung HCHernandez DGibbs JR: Genomewide SNP assay reveals mutations underlying Parkinson disease. Hum Mutat 29:3153222008

  • 114

    Simón-Sánchez JSingleton A: Genome-wide association studies in neurological disorders. Lancet Neurol 7:106710722008

  • 115

    Sklar PSmoller JWFan JFerreira MAPerlis RHChambert K: Whole-genome association study of bipolar disorder. Mol Psychiatry 13:5585692008

  • 116

    Smith ENBloss CSBadner JABarrett TBelmonte PLBerrettini W: Genome-wide association study of bipolar disorder in European American and African American individuals. Mol Psychiatry 14:7557632009

  • 117

    Sonuga-Barke EJLasky-Su JNeale BMOades RChen WFranke B: Does parental expressed emotion moderate genetic effects in ADHD? An exploration using a genome wide association scan. Am J Med Genet B Neuropsychiatr Genet 147B:135913682008

  • 118

    Stefansson HOphoff RASteinberg SAndreassen OACichon SRujescu D: Common variants conferring risk of schizophrenia. Nature 460:7447472009

  • 119

    Stefansson HRujescu DCichon SPietiläinen OPHIngason ASteinberg S: Large recurrent microdeletions associated with schizophrenia. Nature 455:2322362008

  • 120

    Sullivan PFde Geus EJWillemsen GJames MRSmit JHZandbelt T: Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry 14:3593752009

  • 121

    Sullivan PFLin DTzeng JYvan den Oord EPerkins DStroup TS: Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry 13:5705842008

  • 122

    Sulman EPGuerrero MAldape K: Beyond grade: molecular pathology of malignant gliomas. Semin Radiat Oncol 19:1421492009

  • 123

    Terracciano ASanna SUda MDeiana BUsala GBusonero F: Genome-wide association scan for five major dimensions of personality. Mol Psychiatry [epub ahead of print]2008

  • 124

    Thorgeirsson TEGeller FSulem PRafnar TWiste AMagnusson KP: A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452:6386422008

  • 125

    Treutlein JCichon SRidinger MWodarz NSoyka MZill P: Genome-wide association study of alcohol dependence. Arch Gen Psychiatry 66:7737842009

  • 126

    Uhl GRDrgon TLiu QRJohnson CWalther DKomiyama T: Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch Gen Psychiatry 65:3453552008

  • 127

    Uhl GRLiu QRDrgon TJohnson CWalther DRose JE: Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs. BMC Genet 8:102007

  • 128

    van den Oord EJKuo PHHartmann AMWebb BTMöller HJHettema JM: Genomewide association analysis followed by a replication study implicates a novel candidate gene for neuroticism. Arch Gen Psychiatry 65:106210712008

  • 129

    van Es MAVan Vught PWBlauw HMFranke LSaris CGAndersen PM: ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genome-wide association study. Lancet Neurol 6:8698772007

  • 130

    van Es MAvan Vught PWBlauw HMFranke LSaris CGVan den Bosch L: Genetic variation in DPP6 is associated with susceptibility to amyotrophic lateral sclerosis. Nat Genet 40:29312008

  • 131

    Virtanen IMNoponen NBarral SKarppinen JLi HVuoristo M: Putative susceptibility locus on chromosome 21q for lumbar disc disease (LDD) in the Finnish population. J Bone Miner Res 22:7017072007

  • 132

    Walsh TMcClellan JMMcCarthy SEAddington AMPierce SBCooper GM: Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320:5395432008

  • 133

    Wang KZhang HMa DBucan MGlessner JTAbrahams BS: Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459:5285332009

  • 134

    Wang WYBarratt BJClayton DGTodd JA: Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6:1091182005

  • 135

    Waring SCRosenberg RN: Genome-wide association studies in Alzheimer disease. Arch Neurol 65:3293342008

  • 136

    Webster JAMyers AJPearson JVCraig DWHu-Lince DCoon KD: Sorl1 as an Alzheimer's disease predisposition gene?. Neurodegener Dis 5:60642008

  • 137

    Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:6616782007

  • 138

    Williams NMNorton NWilliams HEkholm BHamshere MLLindblom Y: A systematic genomewide linkage study in 353 sib pairs with schizophrenia. Am J Hum Genet 73:135513672003

  • 139

    Winkelmann JSchormair BLichtner PRipke SXiong LJalilzadeh S: Genome-wide association study of restless legs syndrome identifies common variants in three genomic regions. Nat Genet 39:100010062007

  • 140

    Wise CABarnes RGillum JHerring JABowcock AMLovett M: Localization of susceptibility to familial idiopathic scoliosis. Spine (Phila Pa 1976) 25:237223802000

  • 141

    Wise CAGao XShoemaker SGordon DHerring JA: Understanding genetic factors in idiopathic scoliosis, a complex disease of childhood. Curr Genomics 9:51592008

  • 142

    Wittke-Thompson JKPluzhnikov ACox NJ: Rational inferences about departures from Hardy-Weinberg equilibrium. Am J Hum Genet 76:9679862005

  • 143

    Wrensch MJenkins RBChang JSYeh RFXiao YDecker PA: Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat Genet 41:9059082009

  • 144

    Yamada YFuku NTanaka MAoyagi YSawabe MMetoki N: Identification of CELSR1 as a susceptibility gene for ischemic stroke in Japanese individuals by a genome-wide association study. J Atherosclerosis [epub ahead of print]2009

  • 145

    Zhang DCheng LQian YAlliey-Rodriguez NKelsoe JRGreenwood T: Singleton deletions throughout the genome increase risk of bipolar disorder. Mol Psychiatry 14:3763802009

If the inline PDF is not rendering correctly, you can download the PDF file here.

Article Information

Address correspondence to: Matthew C. Cowperthwaite, Ph.D., NeuroTexas Institute, 1015 East 32nd Street, Suite 404, Austin, Texas 78705. email: matthew.cowperthwaite@stdavids.com.

© AANS, except where prohibited by US copyright law.

Headings

Figures

  • View in gallery

    Schematic representation of a GWA study. Left: A mutation (black circles) entering and spreading through a population over time. Right: A contingency table in which the mutation is significantly more abundant among the cases than the controls.

References

1

Abecasis GRCherny SSCookson WOCardon LR: Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:971012002

2

Abraham RMoskvina VSims RHollingworth PMorgan AGeorgieva L: A genome-wide association study for lateonset Alzheimer's disease using DNA pooling. BMC Med Genomics 1:442008

3

Abrahams BSGeschwind DH: Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet 9:3413552008

4

Altshuler DDaly MJLander ES: Genetic mapping in human disease. Science 322:8818882008

5

Anney RJLasky-Su JO'Dúshláine CKenny ENeale BMMulligan A: Conduct disorder and ADHD: evaluation of conduct problems as a categorical and quantitative trait in the international multicentre ADHD genetics study. Am J Med Genet B Neuropsychiatr Genet 147B:136913782008

6

Aulchenko YSHoppenbrouwers IARamagopalan SVBroer LJafari NHillert J: Genetic variation in the KIF1B locus influences susceptibility to multiple sclerosis. Nat Genet 40:140214032008

7

Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene): Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 41:8248282009

8

Autism Genome Project Consortium: Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39:3193282007

9

Bae JSCheong HSKim JOLee SOKim EMLee HW: Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population. Biochem Biophys Res Commun 373:5935962008

10

de Bakker PIYelensky RPe'er IGabriel SBDaly MJAltshuler D: Efficiency and power in genetic association studies. Nat Genet 37:121712232005

11

Balding DJ: A tutorial on statistical methods for population association studies. Nat Rev Genet 7:7817912006

12

Baranzini SEWang JGibson RAGalwey NNaegelin YBarkhof F: Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 18:7677782009

13

Baum AEAkula NCabanero MCardona ICorona WKlemens B: A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol Psychiatry 13:1972072008

14

Beckmann JSEstivill XAntonarakis SE: Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability. Nat Rev Genet 8:6396462007

15

Beecham GWMartin ERLi YJSlifer MAGilbert JRHaines JL: Genome-wide association study implicates a chromosome 12 risk locus for late-onset Alzheimer disease. Am J Hum Genet 84:35432009

16

Benjamini YHochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc 57:2893001995

17

Berrettini WYuan XTozzi FSong KFrancks CChilcoat H: Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol Psychiatry 13:3683732008

18

Bertram LLange CMullin KParkinson MHsiao MHogan MF: Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE. Am J Hum Genet 83:6236322008

19

Bierut LJMadden PABreslau NJohnson EOHatsukami DPomerleau OF: Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet 16:24352007

20

Bilguvar KYasuno KNiemelä MRuigrok YMvon Und Zu Fraunberg Mvan Duijn CM: Susceptibility loci for intracranial aneurysm in European and Japanese populations. Nat Genet 40:147214772008

21

Blauw HMVeldink JHvan Es MAvan Vught PWSaris CGvan der Zwaag B: Copy-number variation in sporadic amyotrophic lateral sclerosis: a genome-wide screen. Lancet Neurol 7:3193262008

22

Bucan MAbrahams BSWang KGlessner JTHerman EISonnenblick LI: Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genet 5:e10005362009

23

Cancer Genome Atlas Research Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:106110682008

24

Capasso MDevoto MHou CAsgharzadeh SGlessner JTAttiyeh EF: Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat Genet 41:7187232009

25

Cardno AGHolmans PARees MIJones LAMcCarthy GMHamshere ML: A genomewide linkage study of age at onset in schizophrenia. Am J Med Genet 105:4394452001

26

Carrasquillo MMZou FPankratz VSWilcox SLMa LWalker LP: Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease. Nat Genet 41:1921982009

27

Chiò ASchymick JCRestagno GScholz SWLombardo FLai SL: A two-stage genome-wide association study of sporadic amyotrophic lateral sclerosis. Hum Mol Genet 18:152415322009

28

Comabella MCraig DWCamina-Tato MMorcillo CLopez CNavarro A: Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms. PLoS One 3:e34902008

29

Comabella MCraig DWMorcillo-Suárez CRío JNavarro AFernández M: Genome-wide scan of 500,000 singlenucleotide polymorphisms among responders and nonresponders to interferon beta therapy in multiple sclerosis. Arch Neurol 66:9729782009

30

Cordell HJ: Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11:246324682002

31

Cordell HJ: Estimation and testing of gene-environment interactions in family-based association studies. Genomics 93:592009

32

Cordell HJ: Genome-wide association studies: detecting genegene interactions that underlie human diseases. Nat Rev Genet 10:3924042009

33

Cordell HJBarratt BJClayton DG: Case/pseudocontrol analysis in genetic association studies: a unified framework for detection of genotype and haplotype associations, gene-gene and gene-environment interactions, and parent-of-origin effects. Genet Epidemiol 26:1671852004

34

Cronin SBerger SDing JSchymick JCWashecka NHernandez DG: A genome-wide association study of sporadic ALS in a homogenous Irish population. Hum Mol Genet 17:7687742008

35

Cronin STomik BBradley DGSlowik AHardiman O: Screening for replication of genome-wide SNP associations in sporadic ALS. Eur J Hum Genet 17:2132182009

36

Desautels ATurecki GMontplaisir JSequeira AVerner ARouleau GA: Identification of a major susceptibility locus for restless legs syndrome on chromosome 12q. Am J Hum Genet 69:126612702001

37

Drgon TMontoya IJohnson CLiu QRWalther DHamer D: Genome-wide association for nicotine dependence and smoking cessation success in NIH research volunteers. Mol Med 15:21272009

38

Dunckley THuentelman MJCraig DWPearson JVSzelinger SJoshipura K: Whole-genome analysis of sporadic amyotrophic lateral sclerosis. N Engl J Med 357:7757882007

39

Farrer MJHaugarvoll KRoss OAStone JTMilkovic NMCobb SA: Genomewide association, Parkinson disease, and PARK10. Am J Hum Genet 78:10841088author reply 1092–10942006

40

Ferreira MAO'Donovan MCMeng YAJones IRRuderfer DMJones L: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40:105610582008

41

Feulner TMLaws SMFriedrich PWagenpfeil SWurst SHRiehle C: Examination of the current top candidate genes for AD in a genome-wide association study. Mol Psychiatry [epub ahead of print]2009

42

Fung HCScholz SMatarin MSimón-Sánchez JHernandez DBritton A: Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 5:9119162006

43

Furnari FBFenton TBachoo RMMukasa AStommel JMStegh A: Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev 21:268327102007

44

Gao XGordon DZhang DBrowne RHelms CGillum J: CHD7 gene polymorphisms are associated with susceptibility to idiopathic scoliosis. Am J Hum Genet 80:9579652007

45

Glessner JTWang KCai GKorvatska OKim CEWood S: Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459:5695732009

46

Gottlieb DJO'Connor GTWilk JB: Genome-wide association of sleep and circadian phenotypes. BMC Med Genet 8:1 SupplS92007

47

Gretarsdottir SThorleifsson GManolescu AStyrkarsdottir UHelgadottir AGschwendtner A: Risk variants for atrial fibrillation on chromosome 4q25 associate with ischemic stroke. Ann Neurol 64:4024092008

48

Grosse SDKhoury MJ: What is the clinical utility of genetic testing?. Genet Med 8:4484502006

49

Grosse SDRogowski WHRoss LFCornel MCDondorp WJKhoury MJ: Population screening for genetic disorders in the 21st century: evidence, economics, and ethics. Public Health Genomics [epub ahead of print]2009

50

Hartl DLClark AG: Principles of Population Genetics Sunderland, MASinauer2007

51

Hattori EToyota TIshitsuka YIwayama YYamada KUjike H: Preliminary genome-wide association study of bipolar disorder in the Japanese population. Am J Med Genet B Neuropsychiatr Genet [epub ahead of print]2009

52

Hindorff LAJunkins HAMehta JPManolio TA: A catalog of published genome-wide association studies. www.genome.gov/gwastudies [Accessed October 27 2009]

53

Hindorff LASethupathy PJunkins HARamos EMMehta JPCollins FS: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:936293672009

54

Hirschhorn JNDaly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:951082005

55

Hoggart CJWhittaker JCDe Iorio MBalding DJ: Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet 4:e10001302008

56

International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 449:8518612007

57

International Multiple Sclerosis Genetics Consortium: Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357:8518622007

58

International Schizophrenia Consortium: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:7487522009

59

De Jager PLJia XWang Jde Bakker PIOttoboni LAggarwal NT: Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 41:7767822009

60

Kim HRamsay ELee HWahl SDionne RA: Genomewide association study of acute post-surgical pain in humans. Pharmacogenomics 10:1711792009

61

Kirov GZaharieva IGeorgieva LMoskvina VNikolov ICichon S: A genome-wide association study in 574 schizophrenia trios using DNA pooling. Mol Psychiatry 14:7968032009

62

Klein RJZeiss CChew EYTsai JYSackler RSHaynes C: Complement factor H polymorphism in age-related macular degeneration. Science 308:3853892005

63

Landers JEMelki JMeininger VGlass JDvan den Berg LHvan Es MA: Reduced expression of the kinesin-associated protein 3 (KIFAP3) gene increases survival in sporadic amyotrophic lateral sclerosis. Proc Natl Acad Sci U S A 106:900490092009

64

Lasky-Su JAnney RJNeale BMFranke BZhou KMaller JB: Genome-wide association scan of the time to onset of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:135513582008

65

Lasky-Su JNeale BMFranke BAnney RJZhou KMaller JB: Genome-wide association scan of quantitative traits for attention deficit hyperactivity disorder identifies novel associations and confirms candidate gene associations. Am J Med Genet B Neuropsychiatr Genet 147B:134513542008

66

Lencz TMorgan TVAthanasiou MDain BReed CRKane JM: Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia. Mol Psychiatry 12:5725802007

67

Lesage SBrice A: Parkinson's disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet 18:R1R48592009

68

Lesch KPTimmesfeld NRenner TJHalperin RRöser CNguyen TT: Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm 115:157315852008

69

Li MBoehnke MAbecasis GR: Efficient study designs for test of genetic association using sibship data and unrelated cases and controls. Am J Hum Genet 78:7787922006

70

Li MBoehnke MAbecasis GR: Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal. Am J Hum Genet 76:9349492005

71

Li AWalling JAhn SKotliarov YSu QQuezado M: Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. Cancer Res 69:209120992009

72

Li HWetten SLi LSt Jean PLUpmanyu RSurh L: Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease. Arch Neurol 65:45532008

73

Liu FArias-Vásquez ASleegers KAulchenko YSKayser MSanchez-Juan P: A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population. Am J Hum Genet 81:17312007

74

Louis DN: Molecular pathology of malignant gliomas. Annu Rev Pathol 1:971172006

75

Ma DSalyakina DJaworski JMKonidari IWhitehead PLAndersen AN: A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann Hum Genet 73:2632732009

76

Maraganore DMde Andrade MLesnick TGStrain KJFarrer MJRocca WA: High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet 77:6856932005

77

Maris JMMosse YPBradfield JPHou CMonni SScott RH: Chromosome 6p22 locus associated with clinically aggressive neuroblastoma. N Engl J Med 358:258525932008

78

Marko NFToms SABarnett GHWeil R: Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study. Genomics 91:3954062008

79

Matarín MBrown WMScholz SSimón-Sánchez JFung HCHernandez D: A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol 6:4144202007

80

McCarthy MIAbecasis GRCardon LRGoldstein DBLittle JIoannidis JP: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:3563692008

81

Mead SPoulter MUphill JBeck JWhitfield JWebb TE: Genetic risk factors for variant Creutzfeldt-Jakob disease: a genome-wide association study. Lancet Neurol 8:57662009

82

Melquist SCraig DWHuentelman MJCrook RPearson JVBaker M: Identification of a novel risk locus for progressive supranuclear palsy by a pooled genomewide scan of 500,288 single-nucleotide polymorphisms. Am J Hum Genet 80:7697782007

83

Mick ENeale BMiddleton FAMcGough JJFaraone SV: Genome-wide association study of response to methylphenidate in 187 children with attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:141214182008

84

Mladkova NChakravarti A: Molecular profiling in glioblastoma: prelude to personalized treatment. Curr Oncol Rep 11:53612009

85

Muglia PTozzi FGalwey NWFrancks CUpmanyu RKong XQ: Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts. Mol Psychiatry [epub ahead of print]2008

86

Nakamura MShimada KIshida ENakase HKonishi N: Genetic analysis to complement histopathological diagnosis of brain tumors. Histol Histopathol 22:3273352007

87

Neale BMLasky-Su JAnney RFranke BZhou KMaller JB: Genome-wide association scan of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B:133713442008

88

Need ACGe DWeale MEMaia JFeng SHeinzen EL: A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genet 5:e10003732009

89

O'Donovan MCCraddock NNorton NWilliams HPeirce TMoskvina V: Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 40:105310552008

90

Oksenberg JRBaranzini SESawcer SHauser SL: The genetics of multiple sclerosis: SNPs to pathways to pathogenesis. Nat Rev Genet 9:5165262008

91

Otowa TYoshida ESugaya NYasuda SNishimura YInoue K: Genome-wide association study of panic disorder in the Japanese population. J Hum Genet 54:1221262009

92

Paassilta PLohiniva JGöring HHPerälä MRäinä SSKarppinen J: Identification of a novel common genetic risk factor for lumbar disk disease. JAMA 285:184318492001

93

Pankratz NWilk JBLatourelle JCDeStefano ALHalter CPugh EW: Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum Genet 124:5936052009

94

Papassotiropoulos AStephan DAHuentelman MJHoerndli FJCraig DWPearson JV: Common Kibra alleles are associated with human memory performance. Science 314:4754782006

95

Parsons DWJones SZhang XLin JC-HLeary RJAngenendt P: An integrated genomic analysis of human glioblastoma multiforme. Science 321:180718122008

96

Poduslo SEHuang RHuang JSmith S: Genome screen of late-onset Alzheimer's extended pedigrees identifies TRPC4AP by haplotype analysis. Am J Med Genet B Neuropsychiatr Genet 150B:50552009

97

Poduslo SEHuang RSpiro A: A genome screen of successful aging without cognitive decline identifies LRP1B by haplotype analysis. Am J Med Genet B Neuropsychiatr Genet [epub ahead of print]2009

98

Pritchard JK: Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet 69:1241372001

99

Purcell SNeale BTodd-Brown KThomas LFerreira MABender D: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:5595752007

100

Reich DELander ES: On the allelic spectrum of human disease. Trends Genet 17:5025102001

101

Rogowski W: Current impact of gene technology on healthcare. A map of economic assessments. Health Policy 80:3403572007

102

Rogowski W: Genetic screening by DNA technology: a systematic review of health economic evidence. Int J Technol Assess Health Care 22:3273372006

103

Schjeide BMHooli BParkinson MHogan MFDiVito JMullin K: GAB2 as an Alzheimer disease susceptibility gene: follow-up of genomewide association results. Arch Neurol 66:2502542009

104

Schormair BKemlink DRoeske DEckstein GXiong LLichtner P: PTPRD (protein tyrosine phosphatase receptor type delta) is associated with restless legs syndrome. Nat Genet 40:9469482008

105

Schymick JCScholz SWFung HCBritton AArepalli SGibbs JR: Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 6:3223282007

106

Scott LJMuglia PKong XQGuan WFlickinger MUpmanyu R: Genome-wide association and meta-analysis of bipolar disorder in individuals of European ancestry. Proc Natl Acad Sci U S A 106:750175062009

107

Sebat JLakshmi BMalhotra DTroge JLese-Martin CWalsh T: Strong association of de novo copy number mutations with autism. Science 316:4454492007

108

Seshadri SDeStefano ALAu RMassaro JMBeiser ASKelly-Hayes M: Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study. BMC Med Genet 8:1 SupplS152007

109

Shete SHosking FJRobertson LBDobbins SESanson MMalmer B: Genome-wide association study identifies five susceptibility loci for glioma. Nat Genet 41:8999042009

110

Shi JLevinson DFDuan JSanders ARZheng YPe'er I: Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460:7537572009

111

Shifman SBhomra ASmiley SWray NRJames MRMartin NG: A whole genome association study of neuroticism using DNA pooling. Mol Psychiatry 13:3023122008

112

Shifman SJohannesson MBronstein MChen SXCollier DACraddock NJ: Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women. PLoS Genet 4:e282008

113

Simón-Sánchez JScholz SMatarin Mdel MFung HCHernandez DGibbs JR: Genomewide SNP assay reveals mutations underlying Parkinson disease. Hum Mutat 29:3153222008

114

Simón-Sánchez JSingleton A: Genome-wide association studies in neurological disorders. Lancet Neurol 7:106710722008

115

Sklar PSmoller JWFan JFerreira MAPerlis RHChambert K: Whole-genome association study of bipolar disorder. Mol Psychiatry 13:5585692008

116

Smith ENBloss CSBadner JABarrett TBelmonte PLBerrettini W: Genome-wide association study of bipolar disorder in European American and African American individuals. Mol Psychiatry 14:7557632009

117

Sonuga-Barke EJLasky-Su JNeale BMOades RChen WFranke B: Does parental expressed emotion moderate genetic effects in ADHD? An exploration using a genome wide association scan. Am J Med Genet B Neuropsychiatr Genet 147B:135913682008

118

Stefansson HOphoff RASteinberg SAndreassen OACichon SRujescu D: Common variants conferring risk of schizophrenia. Nature 460:7447472009

119

Stefansson HRujescu DCichon SPietiläinen OPHIngason ASteinberg S: Large recurrent microdeletions associated with schizophrenia. Nature 455:2322362008

120

Sullivan PFde Geus EJWillemsen GJames MRSmit JHZandbelt T: Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry 14:3593752009

121

Sullivan PFLin DTzeng JYvan den Oord EPerkins DStroup TS: Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry 13:5705842008

122

Sulman EPGuerrero MAldape K: Beyond grade: molecular pathology of malignant gliomas. Semin Radiat Oncol 19:1421492009

123

Terracciano ASanna SUda MDeiana BUsala GBusonero F: Genome-wide association scan for five major dimensions of personality. Mol Psychiatry [epub ahead of print]2008

124

Thorgeirsson TEGeller FSulem PRafnar TWiste AMagnusson KP: A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452:6386422008

125

Treutlein JCichon SRidinger MWodarz NSoyka MZill P: Genome-wide association study of alcohol dependence. Arch Gen Psychiatry 66:7737842009

126

Uhl GRDrgon TLiu QRJohnson CWalther DKomiyama T: Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch Gen Psychiatry 65:3453552008

127

Uhl GRLiu QRDrgon TJohnson CWalther DRose JE: Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs. BMC Genet 8:102007

128

van den Oord EJKuo PHHartmann AMWebb BTMöller HJHettema JM: Genomewide association analysis followed by a replication study implicates a novel candidate gene for neuroticism. Arch Gen Psychiatry 65:106210712008

129

van Es MAVan Vught PWBlauw HMFranke LSaris CGAndersen PM: ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genome-wide association study. Lancet Neurol 6:8698772007

130

van Es MAvan Vught PWBlauw HMFranke LSaris CGVan den Bosch L: Genetic variation in DPP6 is associated with susceptibility to amyotrophic lateral sclerosis. Nat Genet 40:29312008

131

Virtanen IMNoponen NBarral SKarppinen JLi HVuoristo M: Putative susceptibility locus on chromosome 21q for lumbar disc disease (LDD) in the Finnish population. J Bone Miner Res 22:7017072007

132

Walsh TMcClellan JMMcCarthy SEAddington AMPierce SBCooper GM: Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320:5395432008

133

Wang KZhang HMa DBucan MGlessner JTAbrahams BS: Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459:5285332009

134

Wang WYBarratt BJClayton DGTodd JA: Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6:1091182005

135

Waring SCRosenberg RN: Genome-wide association studies in Alzheimer disease. Arch Neurol 65:3293342008

136

Webster JAMyers AJPearson JVCraig DWHu-Lince DCoon KD: Sorl1 as an Alzheimer's disease predisposition gene?. Neurodegener Dis 5:60642008

137

Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:6616782007

138

Williams NMNorton NWilliams HEkholm BHamshere MLLindblom Y: A systematic genomewide linkage study in 353 sib pairs with schizophrenia. Am J Hum Genet 73:135513672003

139

Winkelmann JSchormair BLichtner PRipke SXiong LJalilzadeh S: Genome-wide association study of restless legs syndrome identifies common variants in three genomic regions. Nat Genet 39:100010062007

140

Wise CABarnes RGillum JHerring JABowcock AMLovett M: Localization of susceptibility to familial idiopathic scoliosis. Spine (Phila Pa 1976) 25:237223802000

141

Wise CAGao XShoemaker SGordon DHerring JA: Understanding genetic factors in idiopathic scoliosis, a complex disease of childhood. Curr Genomics 9:51592008

142

Wittke-Thompson JKPluzhnikov ACox NJ: Rational inferences about departures from Hardy-Weinberg equilibrium. Am J Hum Genet 76:9679862005

143

Wrensch MJenkins RBChang JSYeh RFXiao YDecker PA: Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat Genet 41:9059082009

144

Yamada YFuku NTanaka MAoyagi YSawabe MMetoki N: Identification of CELSR1 as a susceptibility gene for ischemic stroke in Japanese individuals by a genome-wide association study. J Atherosclerosis [epub ahead of print]2009

145

Zhang DCheng LQian YAlliey-Rodriguez NKelsoe JRGreenwood T: Singleton deletions throughout the genome increase risk of bipolar disorder. Mol Psychiatry 14:3763802009

TrendMD

Metrics

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 106 106 21
PDF Downloads 44 44 6
EPUB Downloads 0 0 0

PubMed