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

Genome-wide association study suggests genetic candidate loci of insulin dysregulation in Finnhorses

Equine metabolic syndrome (EMS) is a common welfare problem in horses worldwide. It is characterized by insulin dysregulation (ID), predisposition to laminitis and often obesity. EMS is multifactorial by nature, with both the environment and genetics contributing to the phenotype. Environmental factors, such as feeding and exercise, can be controlled, thus forming the basis for treatment and prevention. Genetic factors, by contrast, are less well-known and not easily controllable. The aim of this study was to identify potential genetic loci influencing ID/EMS in Finnhorses. A single-breed (Finnhorse) case-control genome-wide association study (GWAS) of ID was conducted with controls that included age-appropriate non-ID horses. ID status was determined with an oral sugar test (OST) for fasted horses. Seventy-one Finnhorses participated (n = 34 ID, n = 37 control). DNA samples (hair roots) were genotyped for 65 157 single-nucleotide polymorphisms (SNPs) with the Illumina Equine SNP70 BeadChip, and these data were analysed for association and FST outliers with genomic tools. P-values that exceeded the suggestive threshold (P = 1.00 ×10-5) were found in SNP BIEC2_383954 (P = 3.45 ×10-6) in chromosome 17 and SNP BIEC2_312374 (P = 1.89 ×10-5) in chromosome 15. Hierarchical and Bayesian FST outlier tests also detected these SNPs. Potential candidate genes associated with the ID close to SNP BIEC2_383954, with functions in carbohydrate metabolism, were Arginine and Glutamate Rich 1 (ARGLU1) and Ephrin-B2 (EFNB2).

A digital coding combination analysis for mutational genotyping using pyrosequencing

In the present study, we developed a novel digital coding combination analysis (DCCA) to analyze the gene mutation based on the sample combination principle. The principle is that any numerically named sample is divided into two groups, any two samples are not grouped in the same two groups, and any sample can be tested within the detection limit. Therefore, we proposed a specific combination that N samples were divided into M groups. Then N samples were analyzed, which could obtain the mutation results of M mixed groups. If only two groups showed positive (mutant type) signals, the same sample number from two positive signal groups would be the positive sample, and the remaining samples were negative (wild type). If three groups or more exhibited positive results, the same sample number from three positive signal groups would be the positive sample. If some samples remained uncertain, individual samples could be analyzed on a small scale. In the present study, we used the two genotypes of a mutation site (A5301G) to verify whether it was a useful and promising method. The results showed that we could quantitatively detect mutations and demonstrate 100% consistent results against a panel of defined mixtures with the detection limit using pyrosequencing. This method was suitable, sensitive, and reproducible for screening and analyzing low-frequency mutation samples, which could reduce reagent consumption and cost by approximately 70-80% compared with conventional clinical methods.

Dissecting the catatonia phenotype in psychotic and mood disorders on the basis of familial-genetic factors

BACKGROUND

This study examines the familial aggregation (familiality) of different phenotypic definitions of catatonia in a sample of multiplex families with psychotic and mood disorders.

METHODS

Participants were probands with a lifetime diagnosis of a DSM-IV functional psychotic disorder, their parents and at least one first-degree relative with a psychotic disorder. The study sample included 441 families comprising 2703 subjects, of whom 1094 were affected and 1609 unaffected. Familiality (h²) was estimated by linear mixed models using family membership as a random effect, with h² indicating the portion of phenotypic variance accounted for by family membership.

RESULTS

Familiality estimates highly varied for individual catatonia signs (h²=0.17-0.65), principal component analysis-derived factors (h²=0.29-0.49), number of catatonia signs present (h²=0.03-0.43) and severity of the catatonia syndrome (h²=0.25-0.59). Phenotypes maximizing familiality estimates included individual signs (mutism and rigidity, both h²=0.65), presence of ≥5 catatonia signs (h²=0.43), a classical catatonia factor (h²=0.49), a DSM-IV catatonia syndrome at a severity level of moderate or higher (h²=0.59) and the diagnostic construct of psychosis with prominent catatonia features (h²=0.56). Familiality estimates of a DSM-IV catatonia syndrome did not significantly differ across the diagnostic categories of psychotic and mood disorders (h²=0.40-0.47).

CONCLUSIONS

The way in which catatonia is defined has a strong impact on familiality estimates with some catatonia phenotypes exhibiting substantial familial aggregation, which may inform about the most adequate phenotypes for molecular studies. From a familial-genetic perspective, the catatonia phenotype in psychotic and mood disorders has a transdiagnostic character.

Selecting cases and controls for DNA sequencing studies using family histories of disease

Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome-wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

Familiality of Psychotic Disorders: A Polynosologic Study in Multiplex Families

INTRODUCTION

Phenotype definition of psychotic disorders has a strong impact on the degree of familial aggregation. Nevertheless, the extent to which distinct classification systems affect familial aggregation (ie, familiality) remains an open question. This study was aimed at examining the familiality associated with 4 nosologic systems of psychotic disorders (DSM-IV, ICD-10, Leonhard's classification and a data-driven approach) and their constituting diagnoses in a sample of multiplex families with psychotic disorders.

METHODS

Participants were probands with a psychotic disorder, their parents and at least one first-degree relative with a psychotic disorder. The sample was made of 441 families comprising 2703 individuals, of whom 1094 were affected and 1709 unaffected.

RESULTS

The Leonhard classification system had the highest familiality (h (2) = 0.64), followed by the empirical (h (2) = 0.55), DSM-IV (h (2) = 0.50), and ICD-10 (h (2) = 0.48). Familiality estimates for individual diagnoses varied considerably (h (2) = 0.25-0.79). Regarding schizophrenia diagnoses, Leonhard's systematic schizophrenia (h (2) = 0.78) had the highest familiality, followed by latent class core schizophrenia (h (2) = 0.74), DSM-IV schizophrenia (h (2) = 0.48), and ICD-10 schizophrenia (h (2) = 0.41). Psychotic mood disorders showed substantial familiality across nosologic systems (h (2) = 0.60-0.77). Domains of psychopathology other than reality-distortion symptoms showed moderate familiality irrespective of diagnosis (h (2) = 0.22-0.52) with the deficit syndrome of schizophrenia showing the highest familiality (h (2) = 0.66).

CONCLUSIONS

While affective psychoses showed relatively high familiality estimates across classification schemes, those of nonaffective psychoses varied markedly as a function of the diagnostic scheme with a narrow schizophrenia phenotype maximizing its familial aggregation. Leonhard's classification of psychotic disorders may be better suited for molecular genetic studies than the official diagnostic systems.

A Novel Candidate Gene for Temperature-Dependent Sex Determination in the Common Snapping Turtle

Temperature-dependent sex determination (TSD) was described nearly 50 years ago. Researchers have since identified many genes that display differential expression at male- vs. female-producing temperatures. Yet, it is unclear whether these genes (1) are involved in sex determination per se, (2) are downstream effectors involved in differentiation of ovaries and testes, or (3) are thermo-sensitive but unrelated to gonad development. Here we present multiple lines of evidence linking CIRBP to sex determination in the snapping turtle, Chelydra serpentina We demonstrate significant associations between a single nucleotide polymorphism (SNP) (c63A > C) in CIRBP, transcript levels in embryonic gonads during specification of gonad fate, and sex in hatchlings from a thermal regime that produces mixed sex ratios. The A allele was induced in embryos exposed to a female-producing temperature, while expression of the C allele did not differ between female- and male-producing temperatures. In accord with this pattern of temperature-dependent, allele-specific expression, AA homozygotes were more likely to develop ovaries than AC heterozygotes, which, in turn, were more likely to develop ovaries than CC homozygotes. Multiple regression using SNPs in CIRBP and adjacent loci suggests that c63A > C may be the causal variant or closely linked to it. Differences in CIRBP allele frequencies among turtles from northern Minnesota, southern Minnesota, and Texas reflect small and large-scale latitudinal differences in TSD pattern. Finally, analysis of CIRBP protein localization reveals that CIRBP is in a position to mediate temperature effects on the developing gonads. Together, these studies strongly suggest that CIRBP is involved in determining the fate of the bipotential gonad.

Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease

Genome-wide association studies (GWASs) are an optimal design for discovery of disease risk loci for diseases whose underlying genetic architecture includes many common causal loci of small effect (a polygenic architecture). We consider two designs that deserve careful consideration if the true underlying genetic architecture of the trait is polygenic: parent-offspring trios and unscreened control subjects. We assess these designs in terms of quantification of the total contribution of genome-wide genetic markers to disease risk (SNP heritability) and power to detect an associated risk allele. First, we show that trio designs should be avoided when: (1) the disease has a lifetime risk > 1%; (2) trio probands are ascertained from families with more than one affected sibling under which scenario the SNP heritability can drop by more than 50% and power can drop as much as from 0.9 to 0.15 for a sample of 20,000 subjects; or (3) assortative mating occurs (spouse correlation of the underlying liability to the disorder), which decreases the SNP heritability but not the power to detect a single locus in the trio design. Some studies use unscreened rather than screened control subjects because these can be easier to collect; we show that the estimated SNP heritability should then be scaled by dividing by (1 - K × u)(2) for disorders with population prevalence K and proportion of unscreened control subjects u. When omitting to scale appropriately, the SNP heritability of, for example, major depressive disorder (K = 0.15) would be underestimated by 28% when none of the control subjects are screened.

Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values

Background

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro embryo production and embryo transfer (JIVET) can boost rates of genetic gain but they can also increase rates of inbreeding. Inbreeding can be managed using the principles of optimal contribution selection (OCS), which maximizes genetic gain while placing a penalty on the rate of inbreeding. We evaluated the potential benefits and synergies that exist between genomic selection (GS) and reproductive technologies under OCS for sheep and cattle breeding programs.

Methods

Various breeding program scenarios were simulated stochastically including: (1) a sheep breeding program for the selection of a single trait that could be measured either early or late in life; (2) a beef breeding program with an early or late trait; and (3) a dairy breeding program with a sex limited trait. OCS was applied using a range of penalties (severe to no penalty) on co-ancestry of selection candidates, with the possibility of using multiple ovulation and embryo transfer (MOET) and/or juvenile in vitro embryo production and embryo transfer (JIVET) for females. Each breeding program was simulated with and without genomic selection.

Results

All breeding programs could be penalized to result in an inbreeding rate of 1 % increase per generation. The addition of MOET to artificial insemination or natural breeding (AI/N), without the use of GS yielded an extra 25 to 60 % genetic gain. The further addition of JIVET did not yield an extra genetic gain. When GS was used, MOET and MOET + JIVET programs increased rates of genetic gain by 38 to 76 % and 51 to 81 % compared to AI/N, respectively.

Conclusions

Large increases in genetic gain were found across species when female reproductive technologies combined with genomic selection were applied and inbreeding was managed, especially for breeding programs that focus on the selection of traits measured late in life or that are sex-limited. Optimal contribution selection was an effective tool to optimally allocate different combinations of reproductive technologies. Applying a range of penalties to co-ancestry of selection candidates allows a comprehensive exploration of the inbreeding vs. genetic gain space.

Genotype-Based Score Test for Association Testing in Families

The multiplex-case and control design in which multiple cases are sampled from the same family is considered. In such studies phenotype information of the un-genotyped relatives might be available. We intend to use additional family information when performing genetic association tests. A score test is revisited to provide a flexible framework to accommodate various genetic models and to improve power of the association test by adding available family information. The proposed test accounts for correlations induced by multiple cases from the same pedigree, directly deals with X-linked SNPs in mixed-sex-related samples, and incorporates additional phenotypic information such as the number of (un-genotyped) siblings and parents with similar symptoms by assigning the weights to (genotyped) multiplex cases. In addition, the score test directly incorporates posterior probabilities of imputed genotypes, which leads to an efficiency measure that reflects imputation uncertainty on the test conducted. The proposed test is applied to real applications for illustration. Its efficiency is demonstrated via simulations.

Study Designs for Exploring the Non-HLA Genetics in Celiac Disease

Since the breakthrough of genome-wide association studies and genetic studies of common complex diseases like celiac disease have been able to finally identify reproducible gene regions affecting risk of developing disease. Before it was possible to perform genome-wide association analysis, the field struggled with genome-wide linkage analysis to identify gene regions. Genome-wide linkage had been very successful in identifying genes underlying monogenic diseases, but common complex polygenic diseases did not prove so tractable. This chapter will describe the genome-wide methods available for genetic analyses of families today and compare these with the previous analyses performed in the 1990s and early twenty-first century.

Cellular adhesion gene SELP is associated with rheumatoid arthritis and displays differential allelic expression

In rheumatoid arthritis (RA), a key event is infiltration of inflammatory immune cells into the synovial lining, possibly aggravated by dysregulation of cellular adhesion molecules. Therefore, single nucleotide polymorphisms of 14 genes involved in cellular adhesion processes (CAST, ITGA4, ITGB1, ITGB2, PECAM1, PTEN, PTPN11, PTPRC, PXN, SELE, SELP, SRC, TYK2, and VCAM1) were analyzed for association with RA. Association analysis was performed consecutively in three European RA family sample groups (Nfamilies = 407). Additionally, we investigated differential allelic expression, a possible functional consequence of genetic variants. SELP (selectin P, CD62P) SNP-allele rs6136-T was associated with risk for RA in two RA family sample groups as well as in global analysis of all three groups (ptotal = 0.003). This allele was also expressed preferentially (p<10-6) with a two- fold average increase in regulated samples. Differential expression is supported by data from Genevar MuTHER (p1 = 0.004; p2 = 0.0177). Evidence for influence of rs6136 on transcription factor binding was also found in silico and in public datasets reporting in vitro data. In summary, we found SELP rs6136-T to be associated with RA and with increased expression of SELP mRNA. SELP is located on the surface of endothelial cells and crucial for recruitment, adhesion, and migration of inflammatory cells into the joint. Genetically determined increased SELP expression levels might thus be a novel additional risk factor for RA.

Disentangling pooled triad genotypes for association studies

Association studies that genotype affected offspring and their parents (triads) offer robustness to genetic population structure while enabling assessments of maternal effects, parent-of-origin effects, and gene-by-environment interaction. We propose case-parents designs that use pooled DNA specimens to make economical use of limited available specimens. One can markedly reduce the number of genotyping assays required by randomly partitioning the case-parent triads into pooling sets of h triads each and creating three pools from every pooling set, one pool each for mothers, fathers, and offspring. Maximum-likelihood estimation of relative risk parameters proceeds via log-linear modeling using the expectation-maximization algorithm. The approach can assess offspring and maternal genetic effects and accommodate genotyping errors and missing genotypes. We compare the power of our proposed analysis for testing offspring and maternal genetic effects to that based on a difference approach and that of the gold standard based on individual genotypes, under a range of allele frequencies, missing parent proportions, and genotyping error rates. Power calculations show that the pooling strategies cause only modest reductions in power if genotyping errors are low, while reducing genotyping costs and conserving limited specimens.

Utilising family-based designs for detecting rare variant disease associations

Rare genetic variants are thought to be important components in the causality of many diseases but discovering these associations is challenging. We demonstrate how best to use family-based designs to improve the power to detect rare variant disease associations. We show that using genetic data from enriched families (those pedigrees with greater than one affected member) increases the power and sensitivity of existing case-control rare variant tests. However, we show that transmission- (or within-family-) based tests do not benefit from this enrichment. This means that, in studies where a limited amount of genotyping is available, choosing a single case from each of many pedigrees has greater power than selecting multiple cases from fewer pedigrees. Finally, we show how a pseudo-case-control design allows a greater range of statistical tests to be applied to family data.

A novel computational biostatistics approach implies impaired dephosphorylation of growth factor receptors as associated with severity of autism

The prevalence of autism spectrum disorders (ASDs) has increased 20-fold over the past 50 years to >1% of US children. Although twin studies attest to a high degree of heritability, the genetic risk factors are still poorly understood. We analyzed data from two independent populations using u-statistics for genetically structured wide-locus data and added data from unrelated controls to explore epistasis. To account for systematic, but disease-unrelated differences in (non-randomized) genome-wide association studies (GWAS), a correlation between P-values and minor allele frequency with low granularity data and for conducting multiple tests in overlapping genetic regions, we present a novel study-specific criterion for 'genome-wide significance'. From recent results in a comorbid disease, childhood absence epilepsy, we had hypothesized that axonal guidance and calcium signaling are involved in autism as well. Enrichment of the results in both studies with related genes confirms this hypothesis. Additional ASD-specific variations identified in this study suggest protracted growth factor signaling as causing more severe forms of ASD. Another cluster of related genes suggests chloride and potassium ion channels as additional ASD-specific drug targets. The involvement of growth factors suggests the time of accelerated neuronal growth and pruning at 9-24 months of age as the period during which treatment with ion channel modulators would be most effective in preventing progression to more severe forms of autism. By extension, the same computational biostatistics approach could yield profound insights into the etiology of many common diseases from the genetic data collected over the last decade.

Advances in understanding stroke risk in children--a geneticist's view

The advent of the genomic era has provided novel insights into the genetic architecture of common complex diseases, such as thrombophilia and stroke. Since 2006, a growing number of genome wide association studies (GWAS) for common complex diseases have revealed new candidate loci and genomic regions that play an important role in disease aetiology and progression. While GWAS on the above mentioned traits are abundant in adults, similar studies in paediatric study cohorts are lagging behind. However, genetic research in this important clinical area has gained momentum and starts to provide us with exciting insights into the genetic underpinnings of stroke with paediatric onset. Here we review recent advances in genetic association studies underlying stroke in children and aim to translate the results to clinical utility. These studies comprise candidate gene approaches and GWAS, and represent the current status on what we have learnt about the genetic architecture underlying paediatric stroke, and how this may affect medical practice in the years to come.

A possible mechanism behind autoimmune disorders discovered by genome-wide linkage and association analysis in celiac disease

Celiac disease is a common autoimmune disorder characterized by an intestinal inflammation triggered by gluten, a storage protein found in wheat, rye and barley. Similar to other autoimmune diseases such as type 1 diabetes, psoriasis and rheumatoid arthritis, celiac disease is the result of an immune response to self-antigens leading to tissue destruction and production of autoantibodies. Common diseases like celiac disease have a complex pattern of inheritance with inputs from both environmental as well as additive and non-additive genetic factors. In the past few years, Genome Wide Association Studies (GWAS) have been successful in finding genetic risk variants behind many common diseases and traits. To complement and add to the previous findings, we performed a GWAS including 206 trios from 97 nuclear Swedish and Norwegian families affected with celiac disease. By stratifying for HLA-DQ, we identified a new genome-wide significant risk locus covering the DUSP10 gene. To further investigate the associations from the GWAS we performed pathway analyses and two-locus interaction analyses. These analyses showed an over-representation of genes involved in type 2 diabetes and identified a set of candidate mechanisms and genes of which some were selected for mRNA expression analysis using small intestinal biopsies from 98 patients. Several genes were expressed differently in the small intestinal mucosa from patients with celiac autoimmunity compared to intestinal mucosa from control patients. From top-scoring regions we identified susceptibility genes in several categories: 1) polarity and epithelial cell functionality; 2) intestinal smooth muscle; 3) growth and energy homeostasis, including proline and glutamine metabolism; and finally 4) innate and adaptive immune system. These genes and pathways, including specific functions of DUSP10, together reveal a new potential biological mechanism that could influence the genesis of celiac disease, and possibly also other chronic disorders with an inflammatory component.

Structural and population-based evaluations of TBC1D1 p.Arg125Trp

Obesity is now a leading cause of preventable death in the industrialised world. Understanding its genetic influences can enhance insight into molecular pathogenesis and potential therapeutic targets. A non-synonymous polymorphism (rs35859249, p.Arg125Trp) in the N-terminal TBC1D1 phosphotyrosine-binding (PTB) domain has shown a replicated association with familial obesity in women. We investigated these findings in the Avon Longitudinal Study of Parents and Children (ALSPAC), a large European birth cohort of mothers and offspring, and by generating a predicted model of the structure of this domain. Structural prediction involved the use of three separate algorithms; Robetta, HHpred/MODELLER and I-TASSER. We used the transmission disequilibrium test (TDT) to investigate familial association in the ALSPAC study cohort (N = 2,292 mother-offspring pairs). Linear regression models were used to examine the association of genotype with mean measurements of adiposity (Body Mass Index (BMI), waist circumference and Dual-energy X-ray absorptiometry (DXA) assessed fat mass), and logistic regression was used to examine the association with odds of obesity. Modelling showed that the R125W mutation occurs in a location of the TBC1D1 PTB domain that is predicted to have a function in a putative protein:protein interaction. We did not detect an association between R125W and BMI (mean per allele difference 0.27 kg/m² (95% Confidence Interval: 0.00, 0.53) P = 0.05) or obesity (odds ratio 1.01 (95% Confidence Interval: 0.77, 1.31, P = 0.96) in offspring after adjusting for multiple comparisons. Furthermore, there was no evidence to suggest that there was familial association between R125W and obesity (χ² = 0.06, P = 0.80). Our analysis suggests that R125W in TBC1D1 plays a role in the binding of an effector protein, but we find no evidence that the R125W variant is related to mean BMI or odds of obesity in a general population sample.

Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10⁻¹²; NPHP1, rs10173717, P=1.74 × 10⁻¹²; CADPS2, rs3757536, P=1.54 × 10⁻¹⁰; GREM2, rs12129547, P=1.69 × 10⁻¹³, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.

Current Omics Technologies in Biomarker Discovery

Biomarkers are playing an increasingly important role in drug discovery and development and can be applied for many purposes, including disease mechanism study, diagnosis, prognosis, staging, and treatment selection. Advances in high-throughput “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, significantly accelerate the pace of biomarker discovery. Comprehensive molecular profiling using these “omics” technology has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics. Although each “omics” technology plays important roles in biomarker research, different “omics” platforms have different strengths and limitations. This chapter aims to give an overview of these “omics” technologies and their current application in the biomarker discovery.

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, presents different genetic association strategies (candidate gene, genome-wide association, and high-throughput sequencing), introduces basic methods of statistical analysis for case-control data and approaches to combining case-control studies, and discusses measures of association and impact. Admixed populations, controlling for confounding (including population stratification), consideration of multiple loci and environmental risk factors, and complementary analyses of haplotypes, genes, and pathways are briefly discussed. Readers are referred to basic texts on epidemiology for more details on general conduct of case-control studies.

BLUP genotype imputation for case-control association testing with related individuals and missing data

We consider the problem of case-control association testing in samples that contain related individuals, where we assume the pedigree structure is known. Typically, for each marker tested, some individuals will have missing genotype data. The MQLS method has been proposed for association testing in this situation. We show that the MQLS method is equivalent to an approach in which missing genotypes are imputed using the best linear unbiased predictor (BLUP) based on relatives' genotype data. Viewed this way, the MQLS exactly corrects for the imputation error and for the extra correlation due to imputation. We also investigate the amount of additional power for detecting association that is provided by this BLUP imputation approach.

Sample size and statistical power calculation in genetic association studies

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

XM: association testing on the X-chromosome in case-control samples with related individuals

Genetic variants on the X-chromosome could potentially play an important role in some complex traits. However, development of methods for detecting association with X-linked markers has lagged behind that for autosomal markers. We propose methods for case-control association testing with X-chromosome markers in samples with related individuals. Our method, XM, appropriately adjusts for both correlation among relatives and male-female allele copy number differences. Features of XM include: (1) it is applicable to and computationally feasible for completely general combinations of family and case-control designs; (2) it allows for both unaffected controls and controls of unknown phenotype to be included in the same analysis; (3) it can incorporate phenotype information on relatives with missing genotype data; and (4) it adjusts for sex-specific trait prevalence values. We propose two other tests, Xχ and XW, which can also be useful in certain contexts. We derive the best linear unbiased estimator of allele frequency, and its variance, for X-linked markers. In simulation studies with related individuals, we demonstrate the power and validity of the proposed methods. We apply the methods to X-chromosome association analysis of (1) asthma in a Hutterite sample and (2) alcohol dependence in the GAW 14 COGA data. In analysis (1), we demonstrate computational feasibility of XM and the applicability of our robust variance estimator. In analysis (2), we detect significant association, after Bonferroni correction, between alcohol dependence and single nucleotide polymorphism rs979606 in the monoamine oxidases A gene, where this gene has previously been found to be associated with substance abuse and antisocial behavior.

Allele Frequencies and ther2Measure of Linkage Disequilibrium: Impact on Design and Interpretation of Association Studies

The design and interpretation of genetic association studies depends on the relationship between the genotyped variants and the underlying functional variant, often parameterized as the squared correlation orr2measure of linkage disequilibrium between two loci. While it has long been recognized that placing a constraint on ther2between two loci also places a constraint on the difference in frequencies between the coupled alleles, this constraint has not been quantified. Here, quantification of this severe constraint is presented. For example, forr2≥ .8, the maximum difference in allele frequency is ± .06 which occurs when one locus has allele frequency .5. Forr2≥ .8 and allele frequency at one locus of .1, the maximum difference in allele frequency at the second locus is only ± .02. The impact on the design and interpretation of association studies is discussed.

Variations in CRHR1 are associated with persistent pulmonary hypertension of the newborn

INTRODUCTION

Persistent pulmonary hypertension of the newborn (PPHN) is associated with substantial infant morbidity and mortality. Recently, genetic associations have been found in idiopathic pulmonary arterial hypertension.

RESULTS

PPHN was significantly (P < 0.05) associated with genetic variants in corticotropin-releasing hormone (CRH) receptor 1, CRHR1 and CRH-binding protein, CRHBP. Association with CRHR1 rs4458044 passed the Bonferroni threshold for significance. No mutations were found in the bone morphogenetic protein receptor type II (BMPR2) gene.

DISCUSSION

We describe previously unreported genetic associations between PPHN and CRHR1 and CRHBP. These findings may have implications for further understanding the pathophysiology of PPHN and treatment.

METHODS

We performed a family-based candidate gene study to examine a genetic association with PPHN and sequenced the BMPR2 gene in 72 individuals. We enrolled 110 families with infants diagnosed with PPHN based on inclusion criteria. After medical chart review, 22 subjects were excluded based on predefined criteria, and DNA samples from 88 affected infants and at least one parent per infant were collected and genotyped. Thirty-two single-nucleotide polymorphisms in 12 genes involved in vasoconstriction/vasodilation, lung development, surfactant regulation, or vascular endothelial cell function were investigated using family-based association tests.

Genetic studies on the ghrelin, growth hormone secretagogue receptor (GHSR) and ghrelin O-acyl transferase (GOAT) genes

Ghrelin is a 28 amino acid peptide hormone that is produced both centrally and peripherally. Regulated by the ghrelin O-acyl transferase enzyme, ghrelin exerts its action through the growth hormone secretagogue receptor, and is implicated in a diverse range of physiological processes. These implications have placed the ghrelin signaling pathway at the center of a large number of candidate gene and genome-wide studies which aim to identify the genetic basis of human heterogeneity. In this review we summarize the available data on the genetic variability of ghrelin, its receptor and its regulatory enzyme, and their association with obesity, stature, type 2 diabetes, cardiovascular disease, eating disorders, and reward seeking behavior.

Family-based designs for genome-wide association studies

Association mapping has successfully identified common SNPs associated with many diseases. However, the inability of this class of variation to account for most of the supposed heritability has led to a renewed interest in methods - primarily linkage analysis - to detect rare variants. Family designs allow for control of population stratification, investigations of questions such as parent-of-origin effects and other applications that are imperfectly or not readily addressed in case-control association studies. This article guides readers through the interface between linkage and association analysis, reviews the new methodologies and provides useful guidelines for applications. Just as effective SNP-genotyping tools helped to realize the potential of association studies, next-generation sequencing tools will benefit genetic studies by improving the power of family-based approaches.

Comparative genome analysis with the human genome reveals chicken genes associated with fatness and body weight

The selection of meat-type chickens (broilers) for rapid growth has been accompanied by excessive fat deposition. In this study, we analysed 53 candidate genes that are associated with obesity and obesity-related traits in humans, for which we found chicken orthologues by BLAST searches. We have identified single nucleotide polymorphisms (SNPs) with significant differences in allele frequencies between broilers and layers in each of the following six candidate genes: adrenergic, beta-2-, receptor, surface (ADRB2); melanocortin 5 receptor (MC5R); leptin receptor (LEPR), McKusick-Kaufman syndrome (MKKS), milk fat globule-EGF factor 8 protein (MFGE8) and adenylate kinase 1 (AK1). To examine associations with fatness and/or body weight, we used birds of extreme phenotypes in F(2) and backcross populations with varying levels of abdominal fat weight per cent (%AFW) and body weight. We then assessed the level of gene expression by real-time PCR. In two genes, ADRB2 and MFGE8, we found significant association with %AFW. The ADRB2 gene was found to have a significantly higher expression in the liver of lean chickens compared with those of the fat individuals. We believe that this approach can be applied for the identification of other quantitative genes.

Effective sample size: Quick estimation of the effect of related samples in genetic case-control association analyses

Affected relatives are essential for pedigree linkage analysis, however, they cause a violation of the independent sample assumption in case-control association studies. To avoid the correlation between samples, a common practice is to take only one affected sample per pedigree in association analysis. Although several methods exist in handling correlated samples, they are still not widely used in part because these are not easily implemented, or because they are not widely known. We advocate the effective sample size method as a simple and accessible approach for case-control association analysis with correlated samples. This method modifies the chi-square test statistic, p-value, and 95% confidence interval of the odds-ratio by replacing the apparent number of allele or genotype counts with the effective ones in the standard formula, without the need for specialized computer programs. We present a simple formula for calculating effective sample size for many types of relative pairs and relative sets. For allele frequency estimation, the effective sample size method captures the variance inflation exactly. For genotype frequency, simulations showed that effective sample size provides a satisfactory approximation. A gene which is previously identified as a type 1 diabetes susceptibility locus, the interferon-induced helicase gene (IFIH1), is shown to be significantly associated with rheumatoid arthritis when the effective sample size method is applied. This significant association is not established if only one affected sib per pedigree were used in the association analysis. Relationship between the effective sample size method and other methods - the generalized estimation equation, variance of eigenvalues for correlation matrices, and genomic controls - are discussed.

GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits

Background

In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed.

Results

This paper presents GWAPower, a statistical software package of power calculation designed for GWA studies with quantitative traits, where genetic effect is defined as heritability. Based on several popular one-degree-of-freedom genetic models, this method avoids the need to specify the non-centrality parameter of the F-distribution under the alternative hypothesis. Therefore, it can use heritability information directly without approximation. In GWAPower, the power calculation can be easily adjusted for adding covariates and linkage disequilibrium information. An example is provided to illustrate GWAPower, followed by discussions.

Conclusions

GWAPower is a user-friendly free software package for calculating statistical power based on heritability in GWA studies with quantitative traits. The software is freely available at: http://dl.dropbox.com/u/10502931/GWAPower.zip

A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples

A practical way to reduce the cost of surveying single-nucleotide polymorphism (SNP) in a large number of individuals is to measure the allele frequencies in pooled DNA samples. Pyrosequencing^TM has been frequently used for this application because signals generated by this approach are proportional to the amount of DNA templates. The Pyrosequencing^TM pyrogram is determined by the dispensing order of dNTPs, which is usually designed based on the known SNPs to avoid asynchronistic extensions of heterozygous sequences. Therefore, utilizing the pyrogram signals to identify de novo SNPs in DNA pools has never been undertook. Here, in this study we developed an algorithm to address this issue. With the sequence and pyrogram of the wild-type allele known in advance, we could use the pyrogram obtained from the pooled DNA sample to predict the sequence of the unknown mutant allele (de novo SNP) and estimate its allele frequency. Both computational simulation and experimental Pyrosequencing^TM test results suggested that our method performs well. The web interface of our method is available at http://life.nctu.edu.tw/∼yslin/PSM/.

A triple combination strategy corrects population stratification bias and saves genotyping cost

OBJECTIVE

The case-control association study has gained popularity for mapping disease-susceptibility gene(s) of complex human diseases. However, the study is prone to population stratification bias.

STUDY DESIGN AND SETTING

The authors proposed a triple combination strategy of stratum matching, genomic control, and multiple DNA pooling for population-based case-control association studies. Monte Carlo simulation showed that the type I error rates are well controlled using this strategy.

RESULTS

As for power, it increases under the following situations: (1) more accurate stratum matching, (2) more null markers for genomic control, (3) more DNA pooling sets, and (4) more accurate DNA pooling measurement.

CONCLUSION

The triple combination strategy corrects population stratification bias and saves genotyping cost.

Toward a better understanding of ADHD: LPHN3 gene variants and the susceptibility to develop ADHD

During the past 15 years, an impressive amount of genetic information has become available in the research field of psychiatry, particularly as it relates to attention-deficit/hyperactivity disorder (ADHD). However, the classical clinical approach to ADHD has minimally affected and not significantly been improved by this genetic revolution. It is difficult to predict how long it will take for genetic findings to alter the way clinicians treat patients with ADHD. New medications or treatment protocols may take years to become routine clinical practice. However, when taken together, recent successes in genomics, pharmacogenomics, and genetic epidemiology have the potential (1) to prevent comorbid consequences of ADHD, (2) to individualize therapies for patients with ADHD, and (3) to define new epidemiological policies to aid with the impact of ADHD on society. Here, we present an overview of how genetic research may affect and improve the quality of life of patients with ADHD: as an example, we use the discovery of LPHN3, a new gene in which variants have recently been shown to be associated with ADHD.

Confounding from cryptic relatedness in haplotype-based association studies

Cryptic relatedness was suggested to be an important source of confounding in population-based association studies (PBAS). The impact of cryptic relatedness on the performance of haplotype phase inference and haplotype-based association tests is not clear. In this study, we used the Hapmap genetic data to simulate a set of related samples. We evaluated the accuracy of haplotype phase inferred by PHASE 2.1 and calculated the power, type I error rates, accuracy and positive prediction value (PPV) of haplotype frequency-based association tests (HFAT) and haplotype similarity-based association tests (HSAT) under various scenarios, considering relatedness levels, disease models and sample sizes. Cryptic relatedness appeared to slightly increase the accuracy of haplotype phase inference. We observed significant negative effect of cryptic relatedness on the performance of HFAT and HSAT. Ignoring cryptic relatedness may increase spurious association results in haplotype-based PBAS.

Application of association mapping to understanding the genetic diversity of plant germplasm resources

Compared to the conventional linkage mapping, linkage disequilibrium (LD)-mapping, using the nonrandom associations of loci in haplotypes, is a powerful high-resolution mapping tool for complex quantitative traits. The recent advances in the development of unbiased association mapping approaches for plant population with their successful applications in dissecting a number of simple to complex traits in many crop species demonstrate a flourish of the approach as a “powerful gene tagging” tool for crops in the plant genomics era of 21st century. The goal of this review is to provide nonexpert readers of crop breeding community with (1) the basic concept, merits, and simple description of existing methodologies for an association mapping with the recent improvements for plant populations, and (2) the details of some of pioneer and recent studies on association mapping in various crop species to demonstrate the feasibility, success, problems, and future perspectives of the efforts in plants. This should be helpful for interested readers of international plant research community as a guideline for the basic understanding, choosing the appropriate methods, and its application.

Linkage to 20p13 including the ANGPT4 gene in families with mixed Alzheimer's disease and vascular dementia

This study aimed at identifying novel susceptibility genes for a mixed phenotype of Alzheimer's disease and vascular dementia. Results from a genome scan showed strongest linkage to 20p13 in 18 families, and subsequent fine mapping was performed with both microsatellites and single-nucleotide polymorphisms in 18 selected candidate transcripts in an extended sample set of 30 families. The multipoint linkage peak was located at marker rs2144151 in the ANGPT4 gene, which is a strong candidate gene for vascular disease because of its involvement in angiogenesis. Although the significance of the linkage decreased, we find this result intriguing, considering that we included additional families, and thus the reduced linkage signal may be caused by genetic heterogeneity.

The power of the Transmission Disequilibrium Test in the presence of population stratification

The Transmission Disequilibrium Test (TDT) is a family-based test for association based on the rate of transmission of alleles from heterozygous parents to affected offspring, and has gained popularity as this test preserves the Type I error rate. Population stratification results in a decreased number of heterozygous parents compared to that expected assuming Hardy-Weinberg Equilibrium (Wahlund Effect). We show that population stratification changes the relative proportion of the informative mating types. The decrease in the number of heterozygous parents and the change in the relative proportion of the informative mating types result in significant changes to the sample sizes required to achieve the power desired. We show examples of the changes in sample sizes, and provide an easy method for estimating TDT sample sizes in the presence of population stratification. This method potentially aids in reducing the number of false-negative association studies.

A 2cM genome-wide scan of European Holstein cattle affected by classical BSE

Background

Classical bovine spongiform encephalopathy (BSE) is an acquired prion disease that is invariably fatal in cattle and has been implicated as a significant human health risk. Polymorphisms that alter the prion protein of sheep or humans have been associated with variations in transmissible spongiform encephalopathy susceptibility or resistance. In contrast, there is no strong evidence that non-synonymous mutations in the bovine prion gene (PRNP) are associated with classical BSE disease susceptibility. However, two bovine PRNP insertion/deletion polymorphisms, one within the promoter region and the other in intron 1, have been associated with susceptibility to classical BSE. These associations do not explain the full extent of BSE susceptibility, and loci outside of PRNP appear to be associated with disease incidence in some cattle populations. To test for associations with BSE susceptibility, we conducted a genome wide scan using a panel of 3,072 single nucleotide polymorphism (SNP) markers on 814 animals representing cases and control Holstein cattle from the United Kingdom BSE epidemic.

Results

Two sets of BSE affected Holstein cattle were analyzed in this study, one set with known family relationships and the second set of paired cases with controls. The family set comprises half-sibling progeny from six sires. The progeny from four of these sires had previously been scanned with microsatellite markers. The results obtained from the current analysis of the family set yielded both some supporting and new results compared with those obtained in the earlier study. The results revealed 27 SNPs representing 18 chromosomes associated with incidence of BSE disease. These results confirm a region previously reported on chromosome 20, and identify additional regions on chromosomes 2, 14, 16, 21 and 28. This study did not identify a significant association near the PRNP in the family sample set. The only association found in the PRNP region was in the case-control sample set and this was not significant after multiple test correction. The genome scan of the case-control animals did not identify any associations that passed a stringent genome-wide significance threshold.

Conclusions

Several regions of the genome are statistically associated with the incidence of classical BSE in European Holstein cattle. Further investigation of loci on chromosomes 2, 14, 16, 20, 21 and 28 will be required to uncover any biological significance underlying these marker associations.

Endothelial nitric oxide synthase T-786C mutation, a reversible etiology of Prinzmetal's angina pectoris

Because the endothelial nitric oxide synthase (eNOS) T-786C polymorphism is associated with reduced nitric oxide production and coronary artery spasm in Japanese patients, we speculated that it might be reversibly associated with Prinzmetal's variant angina in white Americans. Polymerase chain reaction analyses of eNOS T-786C and stromelysin 5A6A polymorphisms were done in 31 women and 12 men (42 white and 1 black American, median age 50 years), with well-documented Prinzmetal's variant angina. We matched each case with 1 healthy control by race and gender. Of the 43 cases, 21 (49%) were homozygous for wild-type normal eNOS, 19 (44%) were T-786C heterozygotes, and 3 (7%) were T-786C homozygotes. Of the 43 controls, 31 (72%) were homozygous for wild-type normal eNOS, 12 (28%) were T-786C heterozygotes, and 0 (0%) were T-786C homozygotes (p = .013). The mutant eNOS T-786C allele frequency in patients was 25 (29%) of 86 vs 12 (14%) of 86 in the controls (p = 0.016). Patients did not differ from controls for the distribution of the stromelysin 6A mutation (p = 0.66) or for the mutant 6A allele frequency (53% in cases, 50% in controls; p = 0.65). Nineteen patients took nitric oxide-elevating l-arginine (9.2 g/day, orally). Of these 19 patients, 10 (53%) became free of angina, 3 (16%) were improved but not angina free, and 6 (32%) had no change in their angina. Using l-arginine, the physical ability score (Seattle Angina Questionnaire) increased from a median of 42 to 72 of a total possible score of 100 (p = 0.011), satisfaction with symptom reduction increased from 53 to 61 (p = 0.004), and the perception of quality of life as acceptable increased from 29 to 50 (p = 0.001). In conclusion, the eNOS T-786C mutation appears to be a reversible etiology of Prinzmetal's variant angina in white Americans whose angina might be ameliorated by l-arginine.

A likelihood-based trait-model-free approach for linkage detection of binary trait

Trait-model-free (or "allele-sharing") approach to linkage analysis is a popular tool in genetic mapping of complex traits, because of the absence of explicit assumptions about the underlying mode of inheritance of the trait. The likelihood framework introduced by Kong and Cox (1997, American Journal of Human Genetics 61, 1179-1188) allows calculation of accurate p-values and LOD scores to test for linkage between a genomic region and a trait. Their method relies on the specification of a model for the trait-dependent segregation of marker alleles at a genomic region linked to the trait. Here we propose a new such model that is motivated by the desire to extract as much information as possible from extended pedigrees containing data from individuals related over several generations. However, our model is also applicable to smaller pedigrees, and has some attractive features compared with existing models (Kong and Cox, 1997), including the fact that it incorporates information on both affected and unaffected individuals. We illustrate the proposed model on simulated and real data, and compare its performance with the existing approach (Kong and Cox, 1997). The proposed approach is implemented in the program lm_ibdtests within the framework of MORGAN 2.8 (http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml).

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure

Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for unknown population and pedigree structure while maintaining high power by taking advantage of known pedigree information when it is available. ROADTRIPS can incorporate data on arbitrary combinations of related and unrelated individuals and is computationally feasible for the analysis of genetic studies with millions of markers. In simulations with related individuals and population structure, including admixture, we demonstrate that ROADTRIPS provides a substantial improvement over existing methods in terms of power and type 1 error. The ROADTRIPS method can be used across a variety of study designs, ranging from studies that have a combination of unrelated individuals and small pedigrees to studies of isolated founder populations with partially known or completely unknown pedigrees. We apply the method to analyze two data sets: a study of rheumatoid arthritis in small UK pedigrees, from Genetic Analysis Workshop 15, and data from the Collaborative Study of the Genetics of Alcoholism on alcohol dependence in a sample of moderate-size pedigrees of European descent, from Genetic Analysis Workshop 14. We detect genome-wide significant association, after Bonferroni correction, in both studies.

Classical twin design in modern pharmacogenomics studies

Response to medication is highly variable, unpredictable and, at times, may be fatal. All drugs are more effective in certain groups of the population while showing no or minimal benefit in other groups. Although the current data on the subject are piecemeal, anecdotal evidence suggests that, in line with other common multifactorial traits, a myriad of genomic as well as environmental factors underpin population variability in drug response. Pharmacogenomics is the study of how variations in the human genome affect the variability in response to medication. Efforts to personalize treatment based on results from pharmacogenomics studies have the potential to increase efficacy, lower the overall cost of treatment, and decrease the incidence of adverse drug reactions, and are one of the major challenges of the modern era. The classical twin design has traditionally been used to assess the relative contribution of genetic and environmental factors to population variation in common, complex phenotypes, including drug response. Twins are not commonly regarded as providing the optimal design in genomic studies. However, we argue that, through their precise ‘matching’ for confounding variables (age, sex, cohort and common environmental effects), their amenability to numerous nonclassical study designs (genome-wide association studies or the role of epigenetic factors), and the availability of large, established registries worldwide, the twin model represents a flexible study design for systems-biology studies of drug response in humans. In this review, we describe the ‘classical twin model’ and its application in traditional pharmacogenetics studies, discuss the value of the twin design in the modern systems biology era, and highlight the potential of existing twin registries in formulating future strategies in pharmacogenomics research. We argue that the usefulness of this design goes beyond its traditional applications. Moreover, the flexibility of the model in concert with the amenability of large, established registries of twins worldwide to the collecting of new phenotypes will mean that the study of identical and nonidentical twins will play a considerable role in shaping our understanding of the important factors that underpin population variability in common, complex phenotypes, including response to medication.

Association study of DTNBP1 with schizophrenia in a US sample

BACKGROUND

Straub et al. (2002b) located a susceptibility region for schizophrenia at the DTNBP1 locus. At least 40 studies (including one study in US populations) attempted to replicate this original finding, but the reported findings are highly diverse and at least five pathways by which dysbindin protein might be involved in schizophrenia have been proposed. This study aimed to test the association in two common US populations by using powerful analytic methods.

METHODS

Six markers at DTNBP1 were genotyped by mass spectroscopy ('MassARRAY' technique) in a sample of 663 individuals, including 346 healthy individuals European-Americans (EAs) and 48 African-Americans (AAs), and 317 individuals with schizophrenia (235 EAs and 82 AAs). Thirty-eight ancestry-informative markers were genotyped in this sample to infer the ancestry proportions. Diplotype, haplotype, genotype, and allele frequency distributions were compared between the cases and controls, controlling for possible population stratification, admixture, and sex-specific effects, and taking interaction effects into account, using a logistic regression analysis (an extended structured association method).

RESULTS

Conventional case-control comparisons showed that genotypes of the markers P1578 (rs1018381) and P1583 (rs909706) were nominally associated with schizophrenia in EAs and in AAs, respectively. These associations became less or nonsignificant after controlling for population stratification and admixture effects (using structured association or regression analysis), and became nonsignificant after correction for multiple testing. However, regression analysis showed that the common diplotypes (ACCCTT/GCCGCC or GCCGCC/GCCGCC) and the interaction effects of haplotypes GCCGCC/GCCGCC significantly affected risk for schizophrenia in EAs, effects that were modified by sex. Fine-mapping using d or J statistics located the specific markers (d: P1328; J: P1333) closest to the putative risk sites in EAs.

CONCLUSION

This study shows that DTNBP1 is a risk gene for schizophrenia in EAs. Variation at DTNBP1 may modify risk for schizophrenia in this population.