The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling

Insulin signaling regulating genes: effect on T2DM and cardiovascular risk

Type 2 diabetes mellitus (T2DM) is a complex disorder that has a heterogeneous genetic and environmental background. In this Review, we discuss the role of relatively infrequent polymorphisms of genes that regulate insulin signaling (including the K121Q polymorphism of ENPP1, the G972R polymorphism of IRS1 and the Q84R polymorphism of TRIB3) in T2DM and other conditions related to insulin resistance. The biological relevance of these three polymorphisms has been very thoroughly characterized both in vitro and in vivo and the available data indicate that they all affect insulin signaling and action as well as insulin secretion. They also affect insulin-mediated regulation of endothelial cell function. In addition, several reports indicate that the effects of all three polymorphisms on the risk of T2DM and cardiovascular diseases related to insulin resistance depend on the clinical features of the individual, including their body weight and age at disease onset. Thus, these polymorphisms might be used to demonstrate how difficult it is to ascertain the contribution of relatively infrequent genetic variants with heterogeneous effects on disease susceptibility. Unraveling the role of such variants might be facilitated by improving disease definition and focusing on specific subsets of patients.

Towards a molecular risk map--recent advances on the etiology of inflammatory bowel disease

Recent advances have enabled a comprehensive understanding of the genetic architecture of inflammatory bowel disease (IBD) with over 30 identified and replicated disease loci. The pathophysiological consequences of disease gene variants in Crohn disease and ulcerative colitis, the two main subentities of IBD, so far are only understood on the single disease gene level, yet complex network analyses linking the individual risk factors into a molecular risk map are still missing. In this review, we will focus on recent pathways and cellular functions that emerged from the genetic studies (e.g. innate immunity, autophagy) and delineate the existence of shared (e.g. IL23R, IL12B) and unique (e.g. NOD2 for CD) risk factors for the disease subtypes. Ultimately, the defined molecular profiles may identify individuals at risk early in life and may serve as a guidance to administer personalized interventions for causative therapies and/or early targeted prevention strategies. Due to this comparatively advanced level of molecular understanding in the field, IBD may represent precedent also for future developments of individualized genetic medicine in other polygenic disorders in general.

Methodological Issues in Multistage Genome-wide Association Studies

Because of the high cost of commercial genotyping chip technologies, many investigations have used a two-stage design for genome-wide association studies, using part of the sample for an initial discovery of "promising" SNPs at a less stringent significance level and the remainder in a joint analysis of just these SNPs using custom genotyping. Typical cost savings of about 50% are possible with this design to obtain comparable levels of overall type I error and power by using about half the sample for stage I and carrying about 0.1% of SNPs forward to the second stage, the optimal design depending primarily upon the ratio of costs per genotype for stages I and II. However, with the rapidly declining costs of the commercial panels, the generally low observed ORs of current studies, and many studies aiming to test multiple hypotheses and multiple endpoints, many investigators are abandoning the two-stage design in favor of simply genotyping all available subjects using a standard high-density panel. Concern is sometimes raised about the absence of a "replication" panel in this approach, as required by some high-profile journals, but it must be appreciated that the two-stage design is not a discovery/replication design but simply a more efficient design for discovery using a joint analysis of the data from both stages. Once a subset of highly-significant associations has been discovered, a truly independent "exact replication" study is needed in a similar population of the same promising SNPs using similar methods. This can then be followed by (1) "generalizability" studies to assess the full scope of replicated associations across different races, different endpoints, different interactions, etc.; (2) fine-mapping or re-sequencing to try to identify the causal variant; and (3) experimental studies of the biological function of these genes. Multistage sampling designs may be more useful at this stage, say for selecting subsets of subjects for deep re-sequencing of regions identified in the GWAS.

Significance of genome-wide association studies in molecular anthropology

The successful advent of a genome-wide approach in association studies raises the hopes of human geneticists for solving a genetic maze of complex traits especially the disorders. This approach, which is replete with the application of cutting-edge technology and supported by big science projects (like Human Genome Project; and even more importantly the International HapMap Project) and various important databases (SNP database, CNV database, etc.), has had unprecedented success in rapidly uncovering many of the genetic determinants of complex disorders. The magnitude of this approach in the genetics of classical anthropological variables like height, skin color, eye color, and other genome diversity projects has certainly expanded the horizons of molecular anthropology. Therefore, in this article we have proposed a genome-wide association approach in molecular anthropological studies by providing lessons from the exemplary study of the Wellcome Trust Case Control Consortium. We have also highlighted the importance and uniqueness of Indian population groups in facilitating the design and finding optimum solutions for other genome-wide association-related challenges.

Correcting for cryptic relatedness in population-based association studies of continuous traits

Cryptic relatedness was suggested to be an important source of confounding in population-based association studies (PBAS). The magnitude and manner of cryptic relatedness affecting the performance of PBAS of continuous traits remain to be investigated. We simulated a set of related samples through biased sampling and inbreeding, and evaluated the power and type I error rates of simple association tests (SAT) without correcting for cryptic relatedness. We also used extended likelihood ratio tests (ELRT) to conduct PBAS accounting for cryptic relatedness, and compared it with genomic control (GC). Cryptic relatedness decreased the power as well as increased the type I error rates of SAT in both biased sampling and inbreeding models. The impact of cryptic relatedness on the performance of SAT appeared to be limited in the biased sampling model. However, cryptic relatedness in inbred populations may result in excessive false positive results of SAT. Compared with SAT and GC, ELRT obtained improved power and type I error rates under various scenarios. Ignoring cryptic relatedness may increase spurious association results in PBAS. Our ELRT provides a novel approach to control cryptic relatedness in PBAS of human continuous traits.

An Incomplete-Data Quasi-likelihood Approach to Haplotype-Based Genetic Association Studies on Related Individuals

We propose an incomplete-data, quasi-likelihood framework, for estimation and score tests, which accommodates both dependent and partially-observed data. The motivation comes from genetic association studies, where we address the problems of estimating haplotype frequencies and testing association between a disease and haplotypes of multiple tightly-linked genetic markers, using case-control samples containing related individuals. We consider a more general setting in which the complete data are dependent with marginal distributions following a generalized linear model. We form a vector Z whose elements are conditional expectations of the elements of the complete-data vector, given selected functions of the incomplete data. Assuming that the covariance matrix of Z is available, we form an optimal linear estimating function based on Z, which we solve by an iterative method. This approach addresses key difficulties in the haplotype frequency estimation and testing problems in related individuals: (1) dependence that is known but can be complicated; (2) data that are incomplete for structural reasons, as well as possibly missing, with different amounts of information for different observations; (3) the need for computational speed in order to analyze large numbers of markers; (4) a well-established null model, but an alternative model that is unknown and is problematic to fully specify in related individuals. For haplotype analysis, we give sufficient conditions for consistency and asymptotic normality of the estimator and asymptotic χ(2) null distribution of the score test. We apply the method to test for association of haplotypes with alcoholism in the GAW 14 COGA data set.

Pharmacogenetic of response efficacy to antipsychotics in schizophrenia: pharmacodynamic aspects. Review and implications for clinical research

Pharmacogenetics constitutes a new and growing therapeutic approach in the identification of the predictive factors of the response to antipsychotic treatment. This review aims to summarize recent finding into pharmacodynamic approach of pharmacogenetics of antipsychotics and particularly second generation. Studies were identified in the MEDLINE database from 1993 to July 2008 by combining the following Medical Subject Heading search terms: genetic, polymorphism, single nucleotide polymorphism, pharmacogenetics, antipsychotics, and response to treatment as well as individual antipsychotics names. Only pharmacodynamics studies were analyzed and we focused on efficacy studies. We also reviewed the references of ll identified articles. Most studies follow a polymorphism-by-polymorphism approach, and concern polymorphisms of genes coding for dopamine and serotonin receptors. Haplotypic approach has been considered in some studies. Few have studied the combinations of polymorphisms of several genes as a predictive factor of the response to antipsychotics. We present this gene-by-gene approach while detailing the features of the polymorphisms being studied (functionality, linkage disequilibrium) and the features of the studies (studied treatment(s), prospective/retrospective study, pharmacological dosage). We discuss the heterogeneity of the results and their potential clinical implications and extract methodological suggestions for the future concerning phenotype characterization, genotypes variants studied and methodological and statistical approach.

A genome-wide association study in 574 schizophrenia trios using DNA pooling

The cost of genome-wide association (GWA) studies can be prohibitively high when large samples are genotyped. We conducted a GWA study on schizophrenia (SZ) and to reduce the cost, we used DNA pooling. We used a parent-offspring trios design to avoid the potential problems of population stratification. We constructed pools from 605 unaffected controls, 574 SZ patients and a third pool from all the parents of the patients. We hybridized each pool eight times on Illumina HumanHap550 arrays. We estimated the allele frequencies of each pool from the averaged intensities of the arrays. The significance level of results in the trios sample was estimated on the basis of the allele frequencies in cases and non-transmitted pseudocontrols, taking into account the technical variability of the data. We selected the highest ranked SNPs for individual genotyping, after excluding poorly performing SNPs and those that showed a trend in the opposite direction in the control pool. We genotyped 63 SNPs in 574 trios and analysed the results with the transmission disequilibrium test. Forty of those were significant at P<0.05, with the best result at P=1.2 x 10(-6) for rs11064768. This SNP is within the gene CCDC60, a coiled-coil domain gene. The third best SNP (P=0.00016) is rs893703, within RBP1, a candidate gene for schizophrenia.

Novel sib pair selection strategy increases power in quantitative association analysis

Quantitative-trait association studies have been widely used in search for genetic loci for complex traits in recent years. Yet, fiscal constraints still prohibit many on-going research projects from recruiting a large number of individuals for genotyping to reach a desired level of statistical power. Accordingly, in this article, we describe a novel sib pair sampling strategy for genotyping in QTL association studies. With the use of phenotypic scores (and IBD allele-sharing probabilities if available), the genetic effect of a biallelic additive trait locus can be properly modelled within the maximum-likelihood variance components framework proposed by Fulker et al. (Am J Hum Genet 64(1):259-267, 1999) and sib pairs can be rank-ordered by use of informativeness indices. The performance of our method was investigated using simulation. The power of our approach was shown to be higher when compared with other phenotypic selection schemes. An R-script implementing all the selection approaches (including the traditional phenotype-based ones) used in the simulation is available at http://statgen.hku.hk/jshkwan .

Estimating population haplotype frequencies from pooled DNA samples using PHASE algorithm

Recent studies show that the PHASE algorithm is a state-of-the-art method for population-based haplotyping from individually genotyped data. We present a modified version of PHASE for estimating population haplotype frequencies from pooled DNA data. The algorithm is compared with (i) a maximum likelihood estimation under the multinomial model and (ii) a deterministic greedy algorithm, on both simulated and real data sets (HapMap data). Our results suggest that the PHASE algorithm is a method of choice also on pooled DNA data. The main reason for improvement over the other approaches is assumed to be the same as with individually genotyped data: the biologically motivated model of PHASE takes into account correlated genealogical histories of the haplotypes by modelling mutations and recombinations. The important questions of efficiency of DNA pooling as well as influence of the pool size on the accuracy of the estimates are also considered. Our results are in line with the earlier findings in that the pool size should be relatively small, only 2-5 individuals in our examples, in order to provide reliable estimates of population haplotype frequencies.

Family-based genome-wide association studies

In the last 2 years, the effort to identify genes affecting common diseases and complex traits has been accelerated through the use of genome-wide association studies (GWAS). The availability of existing large collections of linkage data paved the way for the use of family-based GWAS. Although most published GWAS used population-based designs, family-based designs have played an important role, particularly in replication stages. Family-based designs offer advantages in terms of quality control, the robustness to population stratification and the ability to perform genetic analyses that cannot be achieved using a sample of unrelated individuals, such as testing for the effect of imprinted genes on phenotypes, testing whether a genetic variant is inherited or de novo and combined linkage and association analysis.

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model

Background

High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals.

Results

This method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r² = 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way.

Conclusion

We conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.

Family-based samples can play an important role in genetic association studies

Over the past 2 decades, DNA samples from thousands of families have been collected and genotyped for linkage studies of common complex diseases, such as type 2 diabetes, asthma, and prostate cancer. Unfortunately, little success has been achieved in identifying genetic susceptibility risk factors through these considerable efforts. However, significant success in identifying common disease risk-associated variants has been recently achieved from genome-wide association studies using unrelated case-control samples. These genome-wide association studies are typically done using population-based cases and controls that are ascertained irrespective of their family history for the disease of interest. Few genetic association studies have taken full advantage of the considerable resources that are available from the linkage-based family collections despite evidence showing cases that have a positive family history of disease are more likely to carry common genetic variants associated with disease susceptibility. Herein, we argue that population stratification is still a concern in case-control genetic association studies, despite the development of analytic methods designed to account for this source of confounding, for a subset of single nucleotide polymorphisms in the genome, most notably those single nucleotide polymorphisms in regions involved with natural selection. We note that current analytic approaches designed to address the issue of population stratification in case-control studies cannot definitively distinguish between true and false associations, and we argue that family-based samples can still serve an invaluable role in following up findings from case-control studies.

Comparison of population-based association study methods correcting for population stratification

Population stratification can cause spurious associations in population-based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population-based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population-based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies.

Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects

Genome-wide association (GWA) can elucidate molecular genetic bases for human individual differences in complex phenotypes that include vulnerability to addiction. Here, we review (a) evidence that supports polygenic models with (at least) modest heterogeneity for the genetic architectures of addiction and several related phenotypes; (b) technical and ethical aspects of importance for understanding GWA data, including genotyping in individual samples versus DNA pools, analytic approaches, power estimation, and ethical issues in genotyping individuals with illegal behaviors; (c) the samples and the data that shape our current understanding of the molecular genetics of individual differences in vulnerability to substance dependence and related phenotypes; (d) overlaps between GWA data sets for dependence on different substances; and (e) overlaps between GWA data for addictions versus other heritable, brain-based phenotypes that include bipolar disorder, cognitive ability, frontal lobe brain volume, the ability to successfully quit smoking, neuroticism, and Alzheimer's disease. These convergent results identify potential targets for drugs that might modify addictions and play roles in these other phenotypes. They add to evidence that individual differences in the quality and quantity of brain connections make pleiotropic contributions to individual differences in vulnerability to addictions and to related brain disorders and phenotypes. A "connectivity constellation" of brain phenotypes and disorders appears to receive substantial pathogenic contributions from individual differences in a constellation of genes whose variants provide individual differences in the specification of brain connectivities during development and in adulthood. Heritable brain differences that underlie addiction vulnerability thus lie squarely in the midst of the repertoire of heritable brain differences that underlie vulnerability to other common brain disorders and phenotypes.

Attention-deficit hyperactivity disorder (ADHD): does new research support old concepts?

OBJECTIVE

To examine the evidence for and against the classification of attention-deficit hyperactivity disorder (ADHD) as a valid disease entity, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV ), criteria.

DATA SOURCES

Sources included but were not limited to published literature on ADHD accessed via PubMed (http://www.ncbi.nlm.nih.gov/PubMed/).

STUDY SELECTION

Peer-reviewed research, review articles, consensus statements, "white papers," and proceedings of professional meetings were used.

DATA EXTRACTION

Focused on evidence base and scientific validity of conclusions.

DATA SYNTHESIS

Evidence for a genetic or neuroanatomic cause of ADHD is insufficient. Experimental work shows that executive function deficits do not explain ADHD. The psychometric properties of widely used ADHD rating scales do not meet standards expected for disease identification.

CONCLUSIONS

ADHD is unlikely to exist as an identifiable disease. Inattention, hyperactivity, and impulsivity are symptoms of many underlying treatable medical, emotional, and psychosocial conditions affecting children.

Folate and Clefts of the Lip and Palate—A U.K.-Based Case-Control Study: Part II: Biochemical and Genetic Analysis

Objective:

To investigate associations between nonsyndromic oral clefts and biochemical measures of folate status and the MTHFR C677T variant in the United Kingdom, where there has been no folic acid fortification program.

Method:

Dietary details were obtained from the mothers of 112 cases of cleft lip with or without cleft palate (CL±P), 78 cleft palate only (CP) cases, and 248 unaffected infants. Infant and parental MTHFR C677T genotype was determined. Red blood cell (RBC) and serum folate and homocysteine levels were assessed in 12-month postpartum blood samples from a subset of mothers. The data were analyzed by logistic and log-linear regression methods.

Results:

There was an inverse association between CL±P and maternal MTHFR CT (odds ratio [OR]  =  0.5, 95% confidence interval [CI]  =  0.31–0.95) and TT (OR  =  0.6, 95% CI  =  0.21–1.50) genotypes, with similar risk estimates for CP. There was no clear association with infant MTHFR genotype. Higher levels of maternal postpartum RBC and serum folate were associated with a lower risk for CL±P and an increased risk for CP. Higher levels of serum homocysteine were associated with a slightly increased risk for both CL±P and CP.

Conclusion:

While the inverse relation between the mother's having the MTHFR C677T variant and both CL±P and CP suggests perturbation of maternal folate metabolism is of etiological importance, contrasting relations between maternal postpartum levels of RBC and serum folate by type of cleft are difficult to explain.

An optimal DNA pooling strategy for progressive fine mapping

We present a cost-effective DNA pooling strategy for fine mapping of a single Mendelian gene in controlled crosses. The theoretical argument suggests that it is potentially possible for a single-stage pooling approach to reduce the overall experimental expense considerably by balancing costs for genotyping and sample collection. Further, the genotyping burden can be reduced through multi-stage pooling. Numerical results are provided for practical guidelines. For example, the genotyping effort can be reduced to only a small fraction of that needed for individual genotyping at a small loss of estimation accuracy or at a cost of increasing sample sizes slightly when recombination rates are 0.5% or less. An optimal two-stage pooling scheme can reduce the amount of genotyping to 19.5%, 14.5% and 6.4% of individual genotyping efforts for identifying a gene within 1, 0.5, and 0.1 cM, respectively. Finally, we use a genetic data set for mapping the rice xl(t) gene to demonstrate the feasibility and efficiency of the DNA pooling strategy. Taken together, the results demonstrate that this DNA pooling strategy can greatly reduce the genotyping burden and the overall cost in fine mapping experiments.

Population-based case-control association studies

This unit provides an overview of the design and analysis of population-based case-control studies of genetic risk factors for complex disease. Considerations specific to genetic studies are emphasized. The unit reviews basic study designs, differentiating case-control studies from others, discusses selection of genetic markers for use in studies, introduces basic methods of analysis of case-control data, and discusses measures of association and impact. Controlling for confounding (including population stratification), consideration of multiple loci, and haplotype analysis are briefly discussed. Readers are referred to basic texts on epidemiology for more details on general conduct of case-control studies.

An integrated genome-wide association analysis on rheumatoid arthritis data

We propose a nonparametric association analysis combining both family and unrelated case-control genotype data. Under the assumption of Hardy-Weinberg equilibrium, we formed an affected group to compare with a group of unaffecteds.

Comparison with traditional case-control chi-square test and transmission-disequilibrium test shows that this new approach has noticeably improved power. All analysis was based on the simulated rheumatoid arthritis data provided by Genetic Analysis Workshop 15. In the situation of population stratification, we also suggest an approach to update the genotype data using principal components. However, the Genetic Analysis Workshop 15 simulation data does not simulate population stratification. All analysis was done without knowledge of the answers.

MPDA: microarray pooled DNA analyzer

BACKGROUND

Microarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.

RESULTS

We propose a generalized concept of pooled DNA and present a user-friendly tool named Microarray Pooled DNA Analyzer (MPDA) that we developed to analyze hybridization intensity data from microarray-based pooled DNA experiments. MPDA enables whole-genome DNA preferential amplification/hybridization analysis, allele frequency estimation, association mapping, allelic imbalance detection, and permits integration with shared data resources online. Graphic and numerical outputs from MPDA support global and detailed inspection of large amounts of genomic data. Four whole-genome data analyses are used to illustrate the major functionalities of MPDA. The first analysis shows that MPDA can characterize genomic patterns of preferential amplification/hybridization and provide calibration information for pooled DNA data analysis. The second analysis demonstrates that MPDA can accurately estimate allele frequencies. The third analysis indicates that MPDA is cost-effective and reliable for association mapping. The final analysis shows that MPDA can identify regions of chromosomal aberration in cancer without paired-normal tissue.

CONCLUSION

MPDA, the software that integrates pooled DNA association analysis and allelic imbalance analysis, provides a convenient analysis system for extensive whole-genome pooled DNA data analysis. The software, user manual and illustrated examples are freely available online at the MPDA website listed in the Availability and requirements section.

Association of genetic variants in GABRA3 gene and thyrotoxic hypokalaemic periodic paralysis in Thai population

BACKGROUND

Genetic predisposition has been suggested to play role in the pathogenesis of thyrotoxic hypokalaemic periodic paralysis (THPP).

OBJECTIVES

In this study, we assessed the differences of single-nucleotide polymorphisms (SNP) allelic frequency between THPP patients and well-characterized controls in order to find the susceptibility genetic variants related to THPP using microarray-based assessments on pooled DNA.

METHODS

Fifty cases of THPP and 50 male hyperthyroid patients without hypokalaemia as controls were recruited. Equal amounts of individual genomic DNA were pooled from each group. Estimated allele frequencies of SNPs were derived by averaging relative allele signal score obtained by Affymetrix GeneChip(R) Mapping 10K Arrays.

RESULTS

Sixty-nine loci that display robust allele frequency differences between THPP and controls were identified. SNP rs750841 (A > T) in intron 3 of the gamma-aminobutyric acid (GABA) receptor alpha3 subunit (GABRA3) gene possessed the most significant difference in allele frequency (27% in THPP case and 5% in controls, P = 0.007). Actual allele frequencies obtained from genotyping in each individual were very similar to the estimated frequency from the pools (28% in THPP and 2% in controls, and P = 0.0002). Nearby DNA sequences of GABRA3 were sequenced and an additional two SNPs were found (A > C at exon 1 and G > T of rs12688128). Allele A of rs750841 and allele G of rs12688128 in intron 3 were predominantly found in THPP with significant genetic relative risk of 19 (P < 0.0002; 95%CI 2.4-151.6).

CONCLUSIONS

Whole-genome scanning on pooled DNA provides an accurate, useful screening tool for elucidating genetic underpinnings of THPP. SNPs at intron 3 of GABRA3 are found to be associated with THPP.

Review and evaluation of methods correcting for population stratification with a focus on underlying statistical principles

When two or more populations have been separated by geographic or cultural boundaries for many generations, drift, spontaneous mutations, differential selection pressures and other factors may lead to allele frequency differences among populations. If these 'parental' populations subsequently come together and begin inter-mating, disequilibrium among linked markers may span a greater genetic distance than it typically does among populations under panmixia [see glossary]. This extended disequilibrium can make association studies highly effective and more economical than disequilibrium mapping in panmictic populations since less marker loci are needed to detect regions of the genome that harbor phenotype-influencing loci. However, under some circumstances, this process of intermating (as well as other processes) can produce disequilibrium between pairs of unlinked loci and thus create the possibility of confounding or spurious associations due to this population stratification. Accordingly, researchers are advised to employ valid statistical tests for linkage disequilibrium mapping allowing conduct of genetic association studies that control for such confounding. Many recent papers have addressed this need. We provide a comprehensive review of advances made in recent years in correcting for population stratification and then evaluate and synthesize these methods based on statistical principles such as (1) randomization, (2) conditioning on sufficient statistics, and (3) identifying whether the method is based on testing the genotype-phenotype covariance (conditional upon familial information) and/or testing departures of the marginal distribution from the expected genotypic frequencies.

Pesticide exposure and risk of Parkinson's disease: a family-based case-control study

BACKGROUND

Pesticides and correlated lifestyle factors (e.g., exposure to well-water and farming) are repeatedly reported risk factors for Parkinson's disease (PD), but few family-based studies have examined these relationships.

METHODS

Using 319 cases and 296 relative and other controls, associations of direct pesticide application, well-water consumption, and farming residences/occupations with PD were examined using generalized estimating equations while controlling for age-at-examination, sex, cigarette smoking, and caffeine consumption.

RESULTS

Overall, individuals with PD were significantly more likely to report direct pesticide application than their unaffected relatives (odds ratio = 1.61; 95% confidence interval, 1.13-2.29). Frequency, duration, and cumulative exposure were also significantly associated with PD in a dose-response pattern (p </= 0.013). Associations of direct pesticide application did not vary by sex but were modified by family history of PD, as significant associations were restricted to individuals with no family history. When classifying pesticides by functional type, both insecticides and herbicides were found to significantly increase risk of PD. Two specific insecticide classes, organochlorines and organophosphorus compounds, were significantly associated with PD. Consuming well-water and living/working on a farm were not associated with PD.

CONCLUSION

These data corroborate positive associations of broadly defined pesticide exposure with PD in families, particularly for sporadic PD. These data also implicate a few specific classes of pesticides in PD and thus emphasize the need to consider a more narrow definition of pesticides in future studies.

A review of current approaches to identifying human genes involved in myopia

The prevalence of myopia is high in many parts of the world, particularly among the Orientals such as Chinese and Japanese. Like other complex diseases such as diabetes and hypertension, myopia is likely to be caused by both genetic and environmental factors, and possibly their interactions. Owing to multiple genes with small effects, genetic heterogeneity and phenotypic complexity, the study of the genetics of myopia poses a complex challenge. This paper reviews the current approaches to the genetic analysis of complex diseases and how these can be applied to the identification of genes that predispose humans to myopia. These approaches include parametric linkage analysis, non-parametric linkage analysis like allele-sharing methods and genetic association studies. Basic concepts, advantages and disadvantages of these approaches are discussed and explained using examples from the literature on myopia. Microsatellites and single nucleotide polymorphisms are common genetic markers in the human genome and are indispensable tools for gene mapping. High throughput genotyping of millions of such markers has become feasible and efficient with recent technological advances. In turn, this makes the identification of myopia susceptibility genes a reality.

Study designs to enhance identification of genetic factors in healthy aging

The sequencing of the human genome and the growing understanding of its function are providing powerful new research tools for identifying genetic variants that are associated with complex diseases and traits. Somewhat less emphasis has been given to genes related to healthy aging, although the approaches for studying health-related traits are analogous to those used for disease-related studies. A critical step prior to the design of such studies is to define a healthy aging phenotype, which should be standardized to permit comparisons across studies and should involve more than simple longevity. Phenotypes of particular value for genetic research are those with high heritability and close relationships to gene products or pathways, preferably with minimal or at least measurable environmental influences. Appropriate study designs to identify genotype-phenotype associations include family-based linkage studies, candidate gene association analyses, and genome-wide association studies. Advances in genotyping and sequencing technologies, and the generation of the human haplotype map database, now permit the cost-effective investigation of the very large sample sizes needed for genome-wide association studies in unrelated individuals. Challenges in interpretation and translation of such studies include assessing the potential for bias and confounding, as well as determining the clinical validity and utility of findings proposed for wider application. Many such studies are currently supported or being planned across the National Institutes of Health (NIH), and lend themselves to the kind of coordinated clinical research envisioned in programs such as the NIH Roadmap.

Optimal two-stage design for case-control association analysis incorporating genotyping errors

Two-stage design is a cost effective approach for identifying disease genes in genetic studies and it has received much attention recently. In general, there are two types of two-stage designs that differ on the methods and samples used to measure allele frequencies in the first stage: (1) Individual genotyping is used in the first stage; (2) DNA pooling is used in the first stage. In this paper, we focus on the latter. Zuo et al. (2006) investigated statistical power of such a design, among other things, but the cost of the study was not taken into account. The purpose of this paper is to study the optimal design under the given overall cost. We investigate how to allocate the resources to the two stages. Note that in addition to the measurement errors associated with DNA pooling, genotyping errors are also unavoidable with individual genotyping. Therefore, we discuss the optimal design combining genotyping errors associated with individual genotyping. The joint statistical distributions of test statistics in the first and second stages are derived. For a fixed cost, our results show that the optimal design requires no additional samples in the second stage but only that the samples in the first stage be re-used. When the second stage uses an entirely independent sample, however, the optimal design under a given cost depends on the population allele frequency and allele frequency difference between the case and control groups. For the current genotyping costs, we can roughly allocate 1/3 to 1/2 of the total sample size to the first stage for screening.

Genome-wide association studies of quantitative traits with related individuals: little (power) lost but much to be gained

For complex disease genetics research in human populations, remarkable progress has been made in recent times with the publication of a number of genome-wide association scans (GWAS) and subsequent statistical replications. These studies have identified new genes and pathways implicated in disease, many of which were not known before. Given these early successes, more GWAS are being conducted and planned, both for disease and quantitative phenotypes. Many researchers and clinicians have DNA samples available on collections of families, including both cases and controls. Twin registries around the world have facilitated the collection of large numbers of families, with DNA and multiple quantitative phenotypes collected on twin pairs and their relatives. In the design of a new GWAS with a fixed budget for the number of chips, the question arises whether to include or exclude related individuals. It is commonly believed to be preferable to use unrelated individuals in the first stage of a GWAS because relatives are 'over-matched' for genotypes. In this study, we quantify that for GWAS of a quantitative phenotype, relative to a sample of unrelated individuals surprisingly little power is lost when using relatives. The advantages of using relatives are manifold, including the ability to perform more quality control, the choice to perform within-family tests of association that are robust to population stratification, and the ability to perform joint linkage and association analysis. Therefore, the advantages of using relatives in GWAS for quantitative traits may well outweigh the small disadvantage in terms of statistical power.

Analysis of quantitative trait loci

Diseases with complex inheritance are characterized by multiple genetic and environmental factors that often interact to produce clinical symptoms. In addition, etiological heterogeneity (different risk factors causing similar phenotypes) obscure the inheritance pattern among affected relatives and hamper the feasibility of gene-mapping studies. For such diseases, the careful selection of quantitative phenotypes that may represent intermediary risk factors for disease development (intermediate phenotypes) is etiologically more homogeneous than the disease per se. Over the last 15 years quantitative trait locus mapping has become a popular method for understanding the genetic basis for intermediate phenotypes. This chapter provides an introduction to classical and recent strategies for mapping quantitative trait loci in humans.

Case-control association analysis of rheumatoid arthritis with candidate genes using related cases

We performed a case-control association analysis of rheumatoid arthritis (RA) for several candidate genes using the North American Rheumatoid Arthritis Consortium (NARAC) data provided in Genetic Analysis Workshop 15. We conducted the case-control association analysis using all related cases and unrelated controls and compared the results with those from the analysis of samples using only one randomly selected case from each family and all unrelated controls. For both analyses we used a weighted composite likelihood ratio test based on single-nucleotide polymorphism (SNP) markers or haplotypes accounting for the correlation among samples within a family. Several SNPs, including R620W in the candidate gene PTPN22, showed an association with RA status, which confirmed previously reported results. Several other SNPs in the candidate genes, such as CTLA4, HAVCR1, and SUMO4, also had rather small p-values (<0.05), suggesting the associations between them and RA. Our results showed that the p-values obtained from the analysis including all related cases were generally smaller than those obtained from the analysis including only one randomly selected case per family. These results, together with the results, based on simulated data, showed that higher power could be achieved using all related cases.

Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests

Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.

Case-control studies with affected sibships

Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index F_ST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well.

Association mapping using pooled DNA

The genetic dissection of complex disorders via genetic marker data has gained popularity in the postgenome era. Methods for typing genetic markers on human chromosomes continue to improve. Compared with the popular individual genotyping experiment, a pooled-DNA experiment (alleotyping experiment) is more cost effective when carrying out genetic typing. This chapter provides an overview of association mapping using pooled DNA and describes a five-stage study design including the preliminary calibration of peak intensities, estimation of allele frequency, single-locus association mapping, multilocus association mapping, and a confirmation study. Software and an analysis of authentic data are presented. The strengths and weaknesses of pooled-DNA analyses, as well as possible future applications for this method, are discussed.

Increased efficiency of case-control association analysis by using allele-sharing and covariate information

OBJECTIVE

We compared the efficiency of case selection strategies for following up a genome-wide linkage screen of multiplex families. We simulated datasets under three models by which continuous environmental or clinical covariates may contribute to disease risk or linkage heterogeneity: (i) a quantitative trait locus (QTL) underlying a continuous disease risk factor, (ii) a gene-environment interaction model, (iii) a heterogeneity model defined by distinct covariate distributions in linked and unlinked families.

METHODS

Marker genotypes and covariate values were generated for affected sibling pair (ASP) families, according to the three models above. We evaluated two case selection strategies relative to a reference design, which compared all family probands to a sample of unrelated controls ('all'). The first strategy ignored covariates and selected probands from families with NPL scores > or =0 ('linked best'). The second strategy selected probands from families identified by an ordered subset analysis (OSA), which utilizes family-specific linkage and covariate information.

RESULTS

The 'linked best' design provided power very similar to the 'all' design under all three models. Under some QTL and heterogeneity models, the OSA design was both most powerful and most efficient.

CONCLUSIONS

Incorporating allele sharing and covariate information from ASP families into a case-control study design can increase power and reduce genotyping cost.

Simultaneously correcting for population stratification and for genotyping error in case-control association studies

In population-based case-control association studies, the regular chi (2) test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2x3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits.

Case-control association testing with related individuals: a more powerful quasi-likelihood score test

We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the MQLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the MQLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the MQLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 (P=5.9x10(-7)), tsc1750530 (P=4.0x10(-7)), tsc0046696 (P=4.7x10(-7)), and tsc0057290 (P=5.2x10(-7)) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.

Interactions among genes in the ErbB-Neuregulin signalling network are associated with increased susceptibility to schizophrenia

BACKGROUND

Evidence of genetic association between the NRG1 (Neuregulin-1) gene and schizophrenia is now well-documented. Furthermore, several recent reports suggest association between schizophrenia and single-nucleotide polymorphisms (SNPs) in ERBB4, one of the receptors for Neuregulin-1. In this study, we have extended the previously published associations by investigating the involvement of all eight genes from the ERBB and NRG families for association with schizophrenia.

METHODS

Eight genes from the ERBB and NRG families were tested for association to schizophrenia using a collection of 396 cases and 1,342 blood bank controls ascertained from Aberdeen, UK. A total of 365 SNPs were tested. Association testing of both alleles and genotypes was carried out using the fast Fisher's Exact Test (FET). To understand better the nature of the associations, all pairs of SNPs separated by >or= 0.5 cM with at least nominal evidence of association (P < 0.10) were tested for evidence of pairwise interaction by logistic regression analysis.

RESULTS

42 out of 365 tested SNPs in the eight genes from the ERBB and NRG gene families were significantly associated with schizophrenia (P < 0.05). Associated SNPs were located in ERBB4 and NRG1, confirming earlier reports. However, novel associations were also seen in NRG2, NRG3 and EGFR. In pairwise interaction tests, clear evidence of gene-gene interaction was detected for NRG1-NRG2, NRG1-NRG3 and EGFR-NRG2, and suggestive evidence was also seen for ERBB4-NRG1, ERBB4-NRG2, ERBB4-NRG3 and ERBB4-ERBB2. Evidence of intragenic interaction was seen for SNPs in ERBB4.

CONCLUSION

These new findings suggest that observed associations between NRG1 and schizophrenia may be mediated through functional interaction not just with ERBB4, but with other members of the NRG and ERBB families. There is evidence that genetic interaction among these loci may increase susceptibility to schizophrenia.

Association study between the dopamine receptor D(4) gene and obsessive-compulsive disorder

Pharmacological and neuroanatomical evidence suggest the involvement of the dopaminergic system in obsessive-compulsive disorder (OCD). Analysis of the 48-bp dopamine receptor D(4) (DRD4) gene polymorphism in a sample of 210 OCD patients and 202 healthy control subjects showed a significant association (chi(2)=27.5, df=6, p=0.0003). This difference was attributable to a lower frequency of allele 4R in OCD patients compared with the control group (chi(2)=9.33, p=0.0027). However, we did not replicate previous findings of an association between the 7R allele and OCD patients with tics. Finally, we analyzed a sub-sample of 86 OCD families. E-TDT analysis in 70 informative parents did not confirm the association observed in our case-control analysis. In conclusion, the current study cannot exclude an association between DRD4 gene and OCD in the largest sample analyzed. However, further studies will be required to confirm if the DRD4 gene is involved in the pathogenesis of this disorder.

Calmodulin-binding transcription activator 1 ( CAMTA1 ) alleles predispose human episodic memory performance

Little is known about the genes and proteins involved in the process of human memory. To identify genetic factors related to human episodic memory performance, we conducted an ultra-high-density genome-wide screen at > 500 000 single nucleotide polymorphisms (SNPs) in a sample of normal young adults stratified for performance on an episodic recall memory test. Analysis of this data identified SNPs within the calmodulin-binding transcription activator 1 (CAMTA1) gene that were significantly associated with memory performance. A follow up study, focused on the CAMTA1 locus in an independent cohort consisting of cognitively normal young adults, singled out SNP rs4908449 with a P-value of 0.0002 as the most significant associated SNP in the region. These validated genetic findings were further supported by the identification of CAMTA1 transcript enrichment in memory-related human brain regions and through a functional magnetic resonance imaging experiment on individuals matched for memory performance that identified CAMTA1 allele-specific upregulation of medial temporal lobe brain activity in those individuals harboring the 'at-risk' allele for poorer memory performance. The CAMTA1 locus encodes a purported transcription factor that interfaces with the calcium-calmodulin system of the cell to alter gene expression patterns. Our validated genomic and functional biological findings described herein suggest a role for CAMTA1 in human episodic memory.

A generic research paradigm for identification and validation of early molecular diagnostics and new therapeutics in common disorders

Genetically complex disorders continue to confound investigators because of their many underlying factors, both genetic and environmental. In order to tease apart the heritable from the non-heritable contributions to disease, clinicians are relying on researchers in the rapidly expanding fields of high-throughput genomics to identify surrogate clinical endpoints, called biomarkers, that provide a measure of the probability that an individual will succumb to the disease in question. The goals of current biomedical research into complex disorders are to identify and utilize these biomarkers, not only for early detection, but also for personalized treatment with knowledge-guided therapeutics. As the identification of these biomarkers is basically a problem of discovery, we discuss new insights into biomarker detection utilizing the most current genomic technologies available. Additionally, we present here a generic paradigm for the validation of such molecular diagnostics as well as new treatment modalities for complex and increasingly common diseases. Lastly, we delve into the ways genomic biomarkers might be implemented in a clinical setting to allow the subsequent application of targeted therapeutics, which can help the ever expanding groups of individuals experiencing these insidious diseases.

NCAM1 and neurocognition in schizophrenia

BACKGROUND

Alterations in neurocognition may be fundamental to schizophrenia and may be endophenotypes. Neural cell adhesion molecule 1 (NCAM1, aliases NCAM and CD56) may be a candidate gene for schizophrenia or for neurocognition in schizophrenia as supported by linkage and functional findings.

METHODS

Subjects were 641 patients with schizophrenia who participated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial. Neurocognition was assessed at study baseline. Nine NCAM1 single nucleotide polymorphisms (SNPs) were blindly genotyped. Analysis of covariance was used to test for single SNP associations and haplotype regression for multilocus associations.

RESULTS

As there were suggestions of population stratification, all analyses were conducted stratified by inferred ancestry. In the "Europe only" stratum, there were nominally significant associations with five contiguous SNPs (rs1943620, rs1836796, rs1821693, rs686050, rs584427) with the strongest association at rs1836796 (p = .007). Via permutation testing, the probability of obtaining five consecutive statistically significant SNPs with p-values <or= .05 was p = .0044. These results were robust to examination of model assumptions. Haplotype analyses did not identify significant haplotype associations.

CONCLUSIONS

Although it is essential to see if these findings replicate in additional samples, we suggest that NCAM1 deserves further scrutiny for its relevance to clinical and etiological aspects of schizophrenia.

Testing for association based on excess allele sharing in a sample of related cases and controls

Samples consisting of a mix of unrelated cases and controls, small pedigrees, and much larger pedigrees present a unique challenge for association studies. Few methods are available for efficient analysis of such a broad spectrum of data structures. In this paper we introduce a new matching statistic that is well suited to complex data structures and compare it with frequency-based methods available in the literature. To investigate and compare the power of these methods we simulate datasets based on complex pedigrees. We examine the influence of various levels of linkage disequilibrium (LD) of the disease allele with a marker allele (or equivalently a haplotype). For low frequency marker alleles/haplotypes, frequency-based statistics are more powerful in detecting association. In contrast, for high frequency marker alleles, the matching statistic has greater power. The highest power for frequency-based statistics occurs when the disease allele frequency closely matches the frequency of the linked marker allele. In contrast maximum power of the matching statistic always occurs for intermediate marker allele frequency regardless of the disease allele frequency. Moreover, the matching and frequency-based statistics exhibit little correlation. We conclude that these two approaches can be viewed as complementary in finding possible association between a disease and a marker for many different situations.

Mixed-effects Logistic Approach for Association Following Linkage Scan for Complex Disorders

An association study to identify possible causal single nucleotide polymorphisms following linkage scanning is a popular approach for the genetic dissection of complex disorders. However, in association studies cases and controls are assumed to be independent, i.e., genetically unrelated. Choosing a single affected individual per family is statistically inefficient and leads to a loss of power. On the other hand, because of the relatedness of family members, using affected family members and unrelated normal controls directly leads to false-positive results in association studies. In this paper we propose a new approach using mixed-model logistic regression, in which associations are performed using family members and unrelated controls. Thus, the important genetic information can be obtained from family members while retaining high statistical power. To examine the properties of this new approach we developed an efficient algorithm, to simulate environmental risk factors and the genotypes at both the disease locus and a marker locus with and without linkage disequilibrium (LD) in families. Extensive simulation studies showed that our approach can effectively control the type-I error probability. Our approach is better than family-based designs such as TDT, because it allows the use of unrelated cases and controls and uses all of the affected members for whom DNA samples are possibly already available. Our approach also allows the inclusion of covariates such as age and smoking status. Power analysis showed that our method has higher statistical power than recent likelihood ratio-based methods when environmental factors contribute to disease susceptibility, which is true for most complex human disorders. Our method can be further extended to accommodate more complex pedigree structures.