A tutorial on statistical methods for population association studies

Association mapping reveals gene action and interactions in the determination of flowering time in barley

The interaction between members of a gene network has an important impact on the variation of quantitative traits, and can influence the outcome of phenotype/genotype association studies. Three genes (Ppd-H1, HvCO1, HvFT1) known to play an essential role in the regulation of flowering time under long days in barley were subjected to an analysis of nucleotide diversity in a collection of 220 spring barley accessions. The coding region of Ppd-H1 was highly diverse, while both HvCO1 and HvFT1 showed a rather limited level of diversity. Within all three genes, the extent of linkage disequilibrium was variable, but on average only moderate. Ppd-H1 is strongly associated with flowering time across four environments, showing a difference of five to ten days between the most extreme haplotypes. The association between flowering time and the variation at HvFT1 and HvCO1 was strongly dependent on the haplotype present at Ppd-H1. The interaction between HvCO1 and Ppd-H1 was statistically significant, but this association disappeared when the analysis was corrected for the geographical origin of the accessions. No association existed between flowering time and allelic variation at HvFT1. In contrast to Ppd-H1, functional variation at both HvCO1 and HvFT1 is limited in cultivated barley.

Genotype imputation

Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.

Gains in power for exhaustive analyses of haplotypes using variable-sized sliding window strategy: a comparison of association-mapping strategies

Linkage disequilibrium (LD)-based association mapping is often performed by analyzing either individual SNPs or block-based multi-SNP haplotypes. Sliding windows of several fixed sizes (in terms of SNP numbers) were also applied to a few simulated or real data sets. In comparison, exhaustively testing based on variable-sized sliding windows (VSW) of all possible sizes of SNPs over a genomic region has the best chance to capture the optimum markers (single SNPs or haplotypes) that are most significantly associated with the traits under study. However, the cost is the increased number of multiple tests and computation. Here, a strategy of VSW of all possible sizes is proposed and its power is examined, in comparison with those using only haplotype blocks (BLK) or single SNP loci (SGL) tests. Critical values for statistical significance testing that account for multiple testing are simulated. We demonstrated that, over a wide range of parameters simulated, VSW increased power for the detection of disease variants by approximately 1-15% over the BLK and SGL approaches. The improved performance was more significant in regions with high recombination rates. In an empirical data set, VSW obtained the most significant signal and identified the LRP5 gene as strongly associated with osteoporosis. With the use of computational techniques such as parallel algorithms and clustering computing, it is feasible to apply VSW to large genomic regions or those regions preliminarily identified by traditional SGL/BLK methods.

Imputation of missing genotypes: an empirical evaluation of IMPUTE

Background

Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood.

Results

We evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans.

Conclusion

Our analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.

Increasing heritability of BMI and stronger associations with the FTO gene over childhood

The growing evidence of health risks associated with the rise in childhood obesity adds to the urgency of understanding the determinants of BMI. Twin analyses on repeated assessments of BMI in a longitudinal sample of >7,000 children indicated that the genetic influence on BMI becomes progressively stronger, with heritability increasing from 0.48 at age 4 to 0.78 at age 11. In the same large twin sample, the association between a common variant in the FTO gene and BMI increased in parallel with the rise in heritability, going from R(2) < 0.001 at age 4 to R(2) = 0.01 at age 11. These findings suggest that expression of FTO may become stronger throughout childhood. Increases in heritability may also be due to children increasingly selecting environments correlated with their genetic propensities.

The SNPMaP package for R: a framework for genome-wide association using DNA pooling on microarrays

Summary: Large-scale genome-wide association (GWA) studies using thousands of high-density SNP microarrays are becoming an essential tool in the search for loci related to heritable variation in many phenotypes. However, the cost of GWA remains beyond the reach of many researchers. Fortunately, the majority of statistical power can still be obtained by estimating allele frequencies from DNA pools, reducing the cost to that of tens, rather than thousands of arrays. We present a set of software tools for processing SNPMaP (SNP microarrays and pooling) data from CEL files to Relative Allele Scores in the rich R statistical computing environment.

Availability: The SNPMaP package is available from http://cran.r-project.org/ under the GNU General Public License version 3 or later.

Contact:snpmap@iop.kcl.ac.uk

Supplementary information: Additional resources and test datasets are available at http://sgdp.iop.kcl.ac.uk/snpmap/

Missing data imputation and haplotype phase inference for genome-wide association studies

Imputation of missing data and the use of haplotype-based association tests can improve the power of genome-wide association studies (GWAS). In this article, I review methods for haplotype inference and missing data imputation, and discuss their application to GWAS. I discuss common features of the best algorithms for haplotype phase inference and missing data imputation in large-scale data sets, as well as some important differences between classes of methods, and highlight the methods that provide the highest accuracy and fastest computational performance.

No association between the protein tyrosine phosphatase, receptor-type, Z Polypeptide 1 (PTPRZ1) gene and schizophrenia in the Japanese population

NRG1-ERBB signaling influences the risk for schizophrenia pathology. A recent study has reported that MAGI1, MAGI2, and protein tyrosine phosphatase, receptor-type, Z polypeptide 1 (PTPRZ1; located on 7q31.3) gene products regulate the NRG1-ERBB4 signaling pathway, and PTPRZ1 is associated with schizophrenia in a Caucasian population. By applying a gene-based association concept, we analyzed any association between PTPRZ1 tagging SNPs and schizophrenia in the Japanese population (576 schizophrenics and 768 controls). After linkage disequilibrium analysis, 29 single nucleotide polymorphisms (SNPs) were genotyped using a 5'-exonuclease allelic discrimination assay. We found a significant association of one tagging SNP in a genotype-wise analysis (P = 0.007); however, this might be resulted from type I error due to multiple testing (P = 0.17 after SNPSpD correction). No association was observed between schizophrenic patients and controls in either allelic, genotypic, or haplotypic analyses. Our results therefore suggest that PTPRZ1 is unlikely to be related to the development of schizophrenia in the Japanese population.

Association study of GSK3 gene polymorphisms with schizophrenia and clozapine response

RATIONALE

A number of human and animal studies implicate GSK3 in the pathophysiology and genetics of schizophrenia. In general, the data suggest that phosphorylation levels of GSK3beta are reduced in schizophrenia, resulting in increased GSK3beta activity. Since GSK3beta regulation is altered in schizophrenia, polymorphic variation in this gene may affect susceptibility to schizophrenia or treatment response.

OBJECTIVE

To analyze GSK3beta genetic variants for association with schizophrenia and clozapine response.

MATERIALS AND METHODS

We examined GSK3beta markers in 185 matched case-control subjects, 85 small nuclear families, and 150 schizophrenia patients treated with clozapine for 6 months.

RESULTS

Three markers (rs7624540, rs4072520, and rs6779828) showed genotypic association with schizophrenia in the case-control sample. We did not observe any family and clozapine response association with a specific allele, genotype, or haplotype.

CONCLUSIONS

Our results suggest that GSK3beta polymorphisms might be involved in schizophrenia risk but do not appear to play a significant role in clozapine response.

Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited

MOTIVATION

This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown.

RESULTS

A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed. It is shown that they significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership. This is illustrated numerically with data simulated from the prior-likelihood model used in inference and also with data simulated from a Wright-Fisher model. Improvements are also illustrated on a real dataset of eighty-eight wolverines (Gulo gulo) genotyped at 10 microsatellites loci. The interest of the solutions presented here are not specific to any clustering model and are hence relevant to many settings in populations genetics where weakly differentiated populations are assumed or sought.

AVAILABILITY

The improvements implemented will be made available in version 3.0.0 of the R package Geneland. Informations on how to get and use the software are available from http://folk.uio.no/gillesg/Geneland.html.

SUPPLEMENTARY INFORMATION

http://folk.uio.no/gillesg/CFM/SuppMat.pdf.

Efficient association study design via power-optimized tag SNP selection

Discovering statistical correlation between causal genetic variation and clinical traits through association studies is an important method for identifying the genetic basis of human diseases. Since fully resequencing a cohort is prohibitively costly, genetic association studies take advantage of local correlation structure (or linkage disequilibrium) between single nucleotide polymorphisms (SNPs) by selecting a subset of SNPs to be genotyped (tag SNPs). While many current association studies are performed using commercially available high-throughput genotyping products that define a set of tag SNPs, choosing tag SNPs remains an important problem for both custom follow-up studies as well as designing the high-throughput genotyping products themselves. The most widely used tag SNP selection method optimizes the correlation between SNPs (r(2)). However, tag SNPs chosen based on an r(2) criterion do not necessarily maximize the statistical power of an association study. We propose a study design framework that chooses SNPs to maximize power and efficiently measures the power through empirical simulation. Empirical results based on the HapMap data show that our method gains considerable power over a widely used r(2)-based method, or equivalently reduces the number of tag SNPs required to attain the desired power of a study. Our power-optimized 100k whole genome tag set provides equivalent power to the Affymetrix 500k chip for the CEU population. For the design of custom follow-up studies, our method provides up to twice the power increase using the same number of tag SNPs as r(2)-based methods. Our method is publicly available via web server at http://design.cs.ucla.edu.

Appropriate data cleaning methods for genome-wide association study

Genome-wide association studies (GWAS) using a large number of single nucleotide polymorphisms (SNPs) have successfully been applied to identify genetic variants of common diseases. However, genotyping using the new array technologies is often associated with spurious results that could unfavorably affect analyses of GWAS. Consequently, data cleaning is of paramount importance in excluding spurious genotyping results. In this study, we investigated the criteria required for the appropriate cleaning of 389 unrelated healthy Japanese samples analyzed using the GeneChip Human Mapping 500K Array Set for GWAS. The samples were randomly subdivided into two groups, and the allele frequencies in the groups were compared for individual SNPs as a quasi-case-control study. Then, observed results were filtered by four parameters (SNP call rate, confidence score obtained using the Bayesian Robust Linear Model with Mahalanobis genotype-calling algorithm, Hardy-Weinberg equilibrium, and minor allele frequency) and assessed for deviation from the null hypothesis. We found that appropriate data cleaning could be achieved using these four parameters. Our findings offer an avenue for obtaining appropriate data from GWAS.

Genome-wide association studies: a new window into immune-mediated diseases

Given the recent explosion of genetic discoveries, 2007 is becoming known to human geneticists as the year of genome-wide association studies. In fact, more genetic risk factors for common diseases were identified in this one year than had been collectively reported before 2007. In particular, 2007 witnessed the discovery of many genes that influence susceptibility to individual immune-mediated diseases, as well as other genes that are associated with susceptibility to more than one disease. Although much work remains to be done, in this Review we discuss what effect these studies are having on our understanding of disease pathogenesis and their potential impact on future immunology studies.

A high-density association screen of 155 ion transport genes for involvement with common migraine

The clinical overlap between monogenic Familial Hemiplegic Migraine (FHM) and common migraine subtypes, and the fact that all three FHM genes are involved in the transport of ions, suggest that ion transport genes may underlie susceptibility to common forms of migraine. To test this leading hypothesis, we examined common variation in 155 ion transport genes using 5257 single nucleotide polymorphisms (SNPs) in a Finnish sample of 841 unrelated migraine with aura cases and 884 unrelated non-migraine controls. The top signals were then tested for replication in four independent migraine case-control samples from the Netherlands, Germany and Australia, totalling 2835 unrelated migraine cases and 2740 unrelated controls. SNPs within 12 genes (KCNB2, KCNQ3, CLIC5, ATP2C2, CACNA1E, CACNB2, KCNE2, KCNK12, KCNK2, KCNS3, SCN5A and SCN9A) with promising nominal association (0.00041 < P < 0.005) in the Finnish sample were selected for replication. Although no variant remained significant after adjusting for multiple testing nor produced consistent evidence for association across all cohorts, a significant epistatic interaction between KCNB2 SNP rs1431656 (chromosome 8q13.3) and CACNB2 SNP rs7076100 (chromosome 10p12.33) (pointwise P = 0.00002; global P = 0.02) was observed in the Finnish case-control sample. We conclude that common variants of moderate effect size in ion transport genes do not play a major role in susceptibility to common migraine within these European populations, although there is some evidence for epistatic interaction between potassium and calcium channel genes, KCNB2 and CACNB2. Multiple rare variants or trans-regulatory elements of these genes are not ruled out.

Promoter region of the bovine growth hormone receptor gene: single nucleotide polymorphism discovery in cattle and association with performance in Brangus bulls

Expression of the GH receptor (GHR) gene and its binding with GH is essential for growth and fat metabolism. A GT microsatellite exists in the promoter of bovine GHR segregating short (11 bp) and long (16 to 20 bp) allele sequences. To detect SNP and complete an association study of genotype to phenotype, we resequenced a 1,195-bp fragment of DNA including the GT microsatellite and exon 1A. Resequencing was completed in 48 familialy unrelated Holstein, Jersey, Brown Swiss, Simmental, Angus, Brahman, and Brangus cattle. Nine SNP were identified. Phylogeny analyses revealed minor distance (i.e., <5%) in DNA sequence among the 5 Bos taurus breeds; however, sequence from Brahman cattle averaged 27.4 +/- 0.07% divergence from the Bos taurus breeds, whereas divergence of Brangus was intermediate. An association study of genotype to phenotype was completed with data from growing Brangus bulls (n = 553 from 96 sires) and data from 4 of the SNP flanking the GT microsatellite. These SNP were found to be in Hardy-Weinberg equilibrium and in phase based on linkage disequilibrium analyses (r(2) = 0.84 and D'= 0.92). An A/G tag SNP was identified (ss86273136) and was located in exon 1A, which began 88 bp downstream from the GT microsatellite. Minor allele frequency of the tag SNP was greater than 10%, and Mendelian segregation was verified in 3 generation pedigrees. The A allele was derived from Brahman, and the G allele was derived from Angus. This tag SNP genotype was a significant effect in analyses of rib fat data collected with ultrasound when bulls were ~365 d of age. Specifically, bulls of the GG genotype had 6.1% more (P = 0.0204) rib fat than bulls of the AA and AG genotypes, respectively. Tag SNP (ss86273136), located in the promoter of GHR, appears to be associated with a measure of corporal fat in Bos taurus x Bos indicus composite cattle.

Genetic variation in the DAOA gene complex: impact on susceptibility for schizophrenia and on cognitive performance

INTRODUCTION

The genetic region coding for d-amino acid oxidase activator (DAOA) is considered an intriguing susceptibility locus for schizophrenia. However, association studies have often resulted in conflicting findings, and the risk-conferring variants and their biological impact remain elusive. Our aim in this study was to investigate the relationship between DAOA variation and schizophrenia, and the influence of DAOA on cognitive performance.

METHODS

We analyzed block structure and association patterns of an approximately 173 kb region on chromosome 13q33, applying genotype data of 55 SNPs derived from Caucasian North American sample (178 cases, 144 healthy controls). Haplotypes were assigned using the program PHASE and frequencies compared between cases and controls. We applied MANOVA to investigate the relationship between the identified risk haplotype on cognitive performance.

RESULTS

We identified multiple haplotypes within the region containing the DAOA gene. Of these, one was significantly associated with schizophrenia, being over-represented in schizophrenia versus healthy controls. This haplotype was also associated with one aspect of cognitive performance, semantic fluency. Carriers of the risk haplotype showed better semantic fluency than non-carriers.

CONCLUSIONS

We report a significant effect of DAOA variation on risk for schizophrenia. Moreover, we identified a relationship between DAOA genetic variation and specific aspects of neurocognitive function. As the identified DAOA risk haplotype was associated with better performance on a semantic fluency measure, further work is required to identify the mechanism of DAOA action on CNS function, including the possibility of a role for balanced selection at this locus.

Current and future directions in genomics of amyotrophic lateral sclerosis

New knowledge of the structure and function of the human genome and novel genomic technologies are being applied to the study of sporadic amyotrophic lateral sclerosis (ALS). These studies can examine tens to hundreds of thousands of items at once, and depend on sophisticated computer processing. Current studies are focused on genetic susceptibility and gene expression and future studies will likely focus on structural variation, gene regulation and non-protein coding regions. The hope is that they will lead to deeper understanding of molecular aspects of the disease and to rational therapeutic targets.

ExactFDR: exact computation of false discovery rate estimate in case-control association studies

Genome-wide association studies require accurate and fast statistical methods to identify relevant signals from the background noise generated by a huge number of simultaneously tested hypotheses. It is now commonly accepted that exact computations of association probability value (P-value) are preferred to chi(2) and permutation-based approximations. Following the same principle, the ExactFDR software package improves speed and accuracy of the permutation-based false discovery rate (FDR) estimation method by replacing the permutation-based estimation of the null distribution by the generalization of the algorithm used for computing individual exact P-values. It provides a quick and accurate non-conservative estimator of the proportion of false positives in a given selection of markers, and is therefore an efficient and pragmatic tool for the analysis of genome-wide association studies.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS.

RESULTS

The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements.

CONCLUSION

The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware.

Human betaine-homocysteine methyltransferase (BHMT) and BHMT2: common gene sequence variation and functional characterization

Betaine-homocysteine methyltransferase (BHMT) catalyzes the remethylation of homocysteine. BHMT2 encodes a protein 73% identical in amino acid sequence to BHMT, but the function of BHMT2 remains unclear. We set out to identify and functionally characterize common genetic variation in BHMT and BHMT2. Specifically, we sequenced exons, exon-intron splice junctions and the 5'-flanking regions (5'-FRs) of BHMT and BHMT2 using 240 DNA samples from four ethnic groups. Twenty-five single nucleotide polymorphisms (SNPs), including 4 nonsynonymous SNPs, and 39 SNPs, including 4 nonsynonymous, were observed in BHMT and BHMT2, respectively. BHMT wild type (WT) and variant allozymes were expressed in COS-1 cells. Variant allozymes showed no significant differences from WT in levels of enzyme activity or immunoreactive protein, but there were statistically significant differences in apparent K(m) values. Luciferase reporter gene constructs were created for the three most common BHMT 5'-FR haplotypes, and significant variation was observed in the ability of these constructs to drive transcription. Although BHMT2 mRNA has been observed in human liver and kidney, expression of the protein has not been reported. We were unable to express BHMT2 in mammalian cells, and the protein aggregated after bacterial expression. Furthermore, BHMT2 was rapidly degraded in a rabbit reticulocyte lysate, but it could be stabilized by cotransfection of COS-1 cells with BHMT and, after cotransfection, it coprecipitated with BHMT. These studies have defined common genetic variation in BHMT and BHMT2 and functionally characterized BHMT SNPs. They may also help to explain why BHMT2 has not previously been defined functionally.

Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits

Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.

Genomewide association for schizophrenia in the CATIE study: results of stage 1

Little is known for certain about the genetics of schizophrenia. The advent of genomewide association has been widely anticipated as a promising means to identify reproducible DNA sequence variation associated with this important and debilitating disorder. A total of 738 cases with DSM-IV schizophrenia (all participants in the CATIE study) and 733 group-matched controls were genotyped for 492,900 single-nucleotide polymorphisms (SNPs) using the Affymetrix 500K two-chip genotyping platform plus a custom 164K fill-in chip. Following multiple quality control steps for both subjects and SNPs, logistic regression analyses were used to assess the evidence for association of all SNPs with schizophrenia. We identified a number of promising SNPs for follow-up studies, although no SNP or multimarker combination of SNPs achieved genomewide statistical significance. Although a few signals coincided with genomic regions previously implicated in schizophrenia, chance could not be excluded. These data do not provide evidence for the involvement of any genomic region with schizophrenia detectable with moderate sample size. However, a planned genomewide association study for response phenotypes and inclusion of individual phenotype and genotype data from this study in meta-analyses hold promise for eventual identification of susceptibility and protective variants.

Polymorphisms in the IGF1 and IGF1R genes and children born small for gestational age: results of large population studies

Small for gestational age (SGA) is the term used to describe a group of children born with a birth weight and/or birth length below the normal range of a reference population, corrected for their gestational age. Although animal models have shown that insulin-like growth factor 1 (IGF1) and insulin-like growth factor 1 receptor (IGF1R) genes are important candidates for reduced pre- and postnatal growth, only limited case reports have been published describing mutations. This might suggest that IGF1 and IGF1R are such crucial growth factors that only common genetic polymorphisms are allowed to survive. Common IGF1 and IGF1R gene polymorphisms, such as single nucleotide polymorphisms and variable number of tandem repeats, have been investigated with conflicting results with respect to SGA-related outcomes. The exact contribution of these polymorphisms to clinical practice remains to be elucidated.

Novel variants in human Aquaporin-4 reduce cellular water permeability

Cerebral edema contributes significantly to morbidity and mortality after brain injury and stroke. Aquaporin-4 (AQP4), a water channel expressed in astrocytes, plays a key role in brain water homeostasis. Genetic variants in other aquaporin family members have been associated with disease phenotypes. However, in human AQP4, only one non-synonymous single-nucleotide polymorphism (nsSNP) has been reported, with no characterization of protein function or disease phenotype. We analyzed DNA from an ethnically diverse cohort of 188 individuals to identify novel AQP4 variants. AQP4 variants were constructed by site-directed mutagenesis and expressed in cells. Water permeability assays in the cells were used to measure protein function. We identified 24 variants in AQP4 including four novel nsSNPs (I128T, D184E, I205L and M224T). We did not observe the previously documented M278T in our sample. The nsSNPs found were rare ( approximately 1-2% allele frequency) and heterozygous. Computational analysis predicted reduced function mutations. Protein expression and membrane localization were similar for reference AQP4 and the five AQP4 mutants. Cellular assays confirmed that four variant AQP4 channels reduced normalized water permeability to between 26 and 48% of the reference (P < 0.001), while the M278T mutation increased normalized water permeability (P < 0.001). We identified multiple novel AQP4 SNPs and showed that four nsSNPs reduced water permeability. The previously reported M278T mutation resulted in gain of function. Our experiments provide insight into the function of the AQP4 protein. These nsSNPs may have clinical implications for patients with cerebral edema and related disorders.

Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment

BACKGROUND

Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.

METHODS

Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001-2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001-2003.

RESULTS

During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.

CONCLUSION

We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

MAX-rank: a simple and robust genome-wide scan for case-control association studies

In genome-wide association studies (GWAS), single-marker analysis is usually employed to identify the most significant single nucleotide polymorphisms (SNPs). The trend test has been proposed for analysis of case-control association. Three trend tests, optimal for the recessive, additive and dominant models respectively, are available. When the underlying genetic model is unknown, the maximum of the three trend test results (MAX) has been shown to be robust against genetic model misspecification. Since the asymptotic distribution of MAX depends on the allele frequency of the SNP, using the P-value of MAX for ranking may be different from using the MAX statistic. Calculating the P-value of MAX for 300,000 (300 K) or more SNPs is computationally intensive and the software and program to obtain the P-value of MAX are not widely available. On the other hand, the MAX statistic is very easy to calculate without complex computer programs. Thus, we study whether or not one could use the MAX statistic instead of its P-value to rank SNPs in GWAS. The approaches using the MAX and its P-value to rank SNPs are referred to as MAX-rank and P-rank. By applying MAX-rank and P-rank to simulated and four real datasets from GWAS, we found the ranks of SNPs with true association are very similar using both approaches. Thus, we recommend to use MAX-rank for genome-wide scans. After the top-ranked SNPs are identified, their P-values based on MAX can be calculated and compared with the significance level.

Olfactory receptor gene polymorphisms and nonallergic vasomotor rhinitis

We sought a genotype-phenotype association: between single-nucleotide polymorphisms (SNPs) in olfactory receptor (OR) genes from the two largest OR gene clusters and odor-triggered nonallergic vasomotor rhinitis (nVMR). In the initial pedigree screen, using transmission disequilibrium test (TDT) analysis, six SNPs showed "significant" p-values between 0.0449 and 0.0043. In a second case-control population, the previously identified six SNPs did not re-emerge, whereas four new SNPs showed p-values between 0.0490 and 0.0001. Combining both studies, none of the SNPs in the TDT analysis survived the Bonferroni correction. In the population study, one SNP showed an empirical p-value of 0.0066 by shuffling cases and controls with 10(5) replicates; however, the p-value for this SNP was 0.83 in the pedigree study. This study emphasizes that underpowered studies having p-values between < 0.05 and 0.0001 should be regarded as inconclusive and require further replication before concluding the study is "informative." However, we believe that our hypothesis that an association between OR genotypes and the nVMR phenotype remains feasible. Future studies using either a genomewide association study of all OR gene-pseudogene regions throughout the genome--at the current recommended density of 2.5 to 5 kb per tag SNP--or studies incorporating microarray analyses of the entire "OR genome" in well-characterized nVMR patients are required.

Estimating odds ratios in genome scans: an approximate conditional likelihood approach

In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.

Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1

To identify risk variants for lung cancer, we conducted a multistage genome-wide association study. In the discovery phase, we analyzed 315,450 tagging SNPs in 1,154 current and former (ever) smoking cases of European ancestry and 1,137 frequency-matched, ever-smoking controls from Houston, Texas. For replication, we evaluated the ten SNPs most significantly associated with lung cancer in an additional 711 cases and 632 controls from Texas and 2,013 cases and 3,062 controls from the UK. Two SNPs, rs1051730 and rs8034191, mapping to a region of strong linkage disequilibrium within 15q25.1 containing PSMA4 and the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5, were significantly associated with risk in both replication sets. Combined analysis yielded odds ratios of 1.32 (P < 1 x 10(-17)) for both SNPs. Haplotype analysis was consistent with there being a single risk variant in this region. We conclude that variation in a region of 15q25.1 containing nicotinic acetylcholine receptors genes contributes to lung cancer risk.

Network-guided genetic screening: building, testing and using gene networks to predict gene function

A challenge facing nearly all biologists is to identify the complete set of genes that are important for a process or disease. This applies to scientists investigating fundamental pathways in model organisms, but also to clinicians trying to understand human disease. There are many different types of experimental data that can be used to predict the genes that are important for a process, but these data are normally dispersed across numerous publications and databases, and are of varying and unknown quality. Integrated functional gene networks aim to gather functional information from all of these data into a single intuitive graph model that can be used to predict gene functions. In this approach, the ability of each data set to predict functional associations between genes is first measured using a standard benchmark, and then the scored predictions by each data set are combined. The resulting integrated probabilistic gene network can be used by all researchers to predict gene function, with much greater coverage and accuracy than any individual data set. In this review, we discuss how such integrated gene networks are constructed, how their predictive power for gene function can be tested, and how experimental biologists can use these networks to guide their research. We pay particular attention to such networks constructed for Caenorhabditis elegans, because in this complex multicellular model system functional predictions for genes can be rapidly tested in vivo using RNAi. The approach is, however, widely applicable to any system, and might soon be a common method used to dissect the genetics of human complex diseases.

Genetic variation in the one-carbon transfer pathway and ovarian cancer risk

Dysfunction in enzymes involved in one-carbon (1-C) metabolism can lead to increased chromosomal strand breaking and abnormal methylation patterns, which are both associated with cancer risk. Availability of 1-C units may modify risk. We investigated the association of single-nucleotide polymorphisms (SNP) in 21 genes in the 1-C transfer pathway among 829 Caucasian cases with primary epithelial ovarian cancer and 941 frequency-matched unaffected controls enrolled at Mayo Clinic (Rochester, MN) and Duke University (Durham, NC) and examined risk modification by multivitamin supplement use. Multivariable-adjusted SNP-specific logistic regression and haplotype analyses were done for 180 SNPs and false positive report probabilities (FPRP) were calculated. Each copy of the minor allele in SHMT1 intron 5 A>G (rs9909104) was associated with epithelial ovarian cancer [odds ratio (OR), 1.2; 95% confidence interval (95% CI), 1.0-1.4; P trend = 0.02; FPRP = 0.16] and a 5-SNP SHMT1 haplotype was associated with decreased risk (P = 0.01; FPRP = 0.09). Three SNPs in DNMT3A were associated with risk among multivitamin supplement users: 3' untranslated region (UTR) C>G (rs13420827: OR, 0.8; 95% CI, 0.6-1.0; P interaction = 0.006; FPRP = 0.54), intron 6 G>A (rs11887120: OR, 0.8; 95% CI, 0.7-1.0; P interaction = 0.007; FPRP = 0.57), and intron 22 A>T (rs11695471: OR, 1.2; 95% CI, 1.0-1.5; P interaction = 0.01; FPRP = 0.66). These data extend previous findings from other cancers of a role for SHMT1 in ovarian cancer, and provide evidence that SNPs in methylation and DNA synthesis reactions are associated with risk of ovarian cancer. Interventions with modifiable factors such as multivitamin intake may reduce risk.

A genetic association study of the FXYD domain containing ion transport regulator 6 (FXYD6) gene, encoding phosphohippolin, in susceptibility to schizophrenia in a Japanese population

The FXYD domain containing ion transport regulator 6 (FXYD6) gene is located within a region of chromosome 11 (11q23.3) that has been shown by a number of genome scans to be one of the most well-established linkages to schizophrenia. FXYD6 encodes the protein phosphohippolin, which is primarily expressed in the brain. Phosphohippolin modulates the kinetic activity of Na,K-ATPase and has long-term physiological importance in maintaining cation homeostasis. A recent study reported that FXYD6 was associated with schizophrenia in the United Kingdom samples. Applying the gene-based association concept, we carried out an association study regarding FXYD6 and schizophrenia in a Japanese population, with a sample consisting of 2026 subjects (906 schizophrenics and 1120 controls). After linkage disequilibrium analysis, 23 single nucleotide polymorphisms (SNPs) were genotyped using 5'-exonuclease allelic discrimination assay. We found a significant association of two SNPs (rs11216573; genotypic P value: 0.022 and rs555577; genotypic P value: 0.026, allelic P value: 0.011, uncorrected). Nominal P values did not survive correction for multiple testing (rs11216573; genotypic P value: 0.47 and rs555577; genotypic P value: 0.55, allelic P value: 0.24, after SNPSpD correction). No association was observed between schizophrenia patients and controls in allelic, genotypic and haplotypic analyses. Our findings suggest that FXYD6 is unlikely to be related to the development of schizophrenia in a Japanese population.

Failure to replicate the association between NRG1 and schizophrenia using Japanese large sample

Systematic linkage disequilibrium (LD) mapping of 8p12-21 in the Icelandic population identified neuregulin 1 (NRG1) as a prime candidate gene for schizophrenia. However, results of replication studies have been inconsistent, and no large sample analyses have been reported. Therefore, we designed this study with the aim of assessing this putative association between schizophrenia and NRG1 (especially HAP(ICE) region and exon region) using a gene-based association approach in the Japanese population. This study was a two-stage association analysis with a different panel of samples, in which the significant association found in the first-set screening samples (1126 cases and 1022 controls) was further assessed in the confirmation samples (1262 cases and 1172 controls, and 166 trio samples). In the first-set scan, 60 SNPs (49 tagging SNPs from HapMap database, four SNPs from other papers, and seven SNPs detected in the mutation scan) were examined. One haplotype showed a significant association in the first-set screening samples (Global P-value=0.0244, uncorrected). However, we could not replicate this association in the following independent confirmation samples. Moreover, we could not find sufficient evidence for association of the haplotype identified as being significant in the first-set samples by imputing ungenotyped SNPs from HapMap database. These results indicate that the positionally and functionally attractive regions of NRG1 are unlikely to contribute to susceptibility to schizophrenia in the Japanese population. Moreover, the nature of our results support that two-stage analysis with large sample size is appropriate to examine the susceptibility genes for common diseases.

Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data

Missing data occur in genetic association studies for several reasons including missing family members and uncertain haplotype phase. Maximum likelihood is a commonly used approach to accommodate missing data, but it can be difficult to apply to family-based association studies, because of possible loss of robustness to confounding by population stratification. Here a novel likelihood for nuclear families is proposed, in which distinct sets of association parameters are used to model the parental genotypes and the offspring genotypes. This approach is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. It also allows a novel conditioning step that gives valid analysis for multiple offspring in the presence of linkage. Unrelated subjects are included by regarding them as the children of two missing parents. Simulations and theory indicate similar operating characteristics to TRANSMIT, but with no bias with missing data in the presence of linkage. In comparison with FBAT and PCPH, the proposed model is slightly less robust to population structure but has greater power to detect strong effects. In comparison to APL and MITDT, the model is more robust to stratification and can accommodate sibships of any size. The methods are implemented for binary and continuous traits in software, UNPHASED, available from the author.

The nature of nurture: a genomewide association scan for family chaos

Widely used measures of the environment, especially the family environment of children, show genetic influence in dozens of twin and adoption studies. This phenomenon is known as gene-environment correlation in which genetically driven influences of individuals affect their environments. We conducted the first genome-wide association (GWA) analysis of an environmental measure. We used a measure called CHAOS which assesses 'environmental confusion' in the home, a measure that is more strongly associated with cognitive development in childhood than any other environmental measure. CHAOS was assessed by parental report when the children were 3 years and again when the children were 4 years; a composite CHAOS measure was constructed across the 2 years. We screened 490,041 autosomal single-nucleotide polymorphisms (SNPs) in a two-stage design in which children in low chaos families (N = 469) versus high chaos families (N = 369) from 3,000 families of 4-year-old twins were screened in Stage 1 using pooled DNA. In Stage 2, following SNP quality control procedures, 41 nominated SNPs were tested for association with family chaos by individual genotyping an independent representative sample of 3,529. Despite having 99% power to detect associations that account for more than 0.5% of the variance, none of the 41 nominated SNPs met conservative criteria for replication. Similar to GWA analyses of other complex traits, it is likely that most of the heritable variation in environmental measures such as family chaos is due to many genes of very small effect size.

Genetic analysis of the gene coding for DARPP-32 (PPP1R1B) in Japanese patients with schizophrenia or bipolar disorder

Several lines of evidence, including genome-wide linkage scans and postmortem brain studies of patients with schizophrenia or bipolar disorder, have suggested that DARPP-32 (dopamine- and cAMP-regulated phosphoprotein, 32 kDa), a key regulatory molecule in the dopaminergic signaling pathway, is involved in these disorders. After evaluating the linkage disequilibrium pattern of the gene encoding DARPP-32 (PPP1R1B; located on 17q12), we conducted association analyses of this gene with schizophrenia and bipolar disorder. Single-marker and haplotypic analyses of four single nucleotide polymorphisms (SNPs; rs879606, rs12601930, rs907094, and rs3764352) in a sample set (subjects with schizophrenia=384, subjects with bipolar disorder=318, control subjects=384) showed that PPP1R1B polymorphisms were not significantly associated with schizophrenia, whereas, even after Bonferroni corrections, significant associations with bipolar disorder were observed for rs12601930 (corrected genotypic p=0.00059) and rs907094 (corrected allelic p=0.040). We, however, could not confirm these results in a second independent sample set (subjects with bipolar disorder=366, control subjects=370). We now believe that the significant association observed with the first sample set was a result of copy number aberrations in the region surrounding these SNPs. Our findings suggest that PPP1R1B SNPs are unlikely to be related to the development of schizophrenia and bipolar disorder in the Japanese population.

Fetal hemoglobin in sickle cell anemia: Bayesian modeling of genetic associations

We genotyped single nucleotide polymorphisms (SNPs) in: (1) the beta-globin gene-like cluster, (2) quantitative trait loci (QTL) previously associated with fetal hemoglobin (HbF) concentration on chromosomes 6q, 8q, and Xp, and (3) candidate genes that could effect HbF levels, in sickle cell anemia subjects. HbF concentration was modeled as a continuous variable with values in a finite interval using a novel Bayesian approach. We first tested the associations of SNPs with HbF in a group of 1,518 adults and children (CSSCD study), and validated the results in a second independent group of 211 adults (MSH study). In subjects aged >or=24 years, 5 SNPs in TOX (8q12.1), 2 SNPs in the beta-globin gene-like cluster, 2 SNPs in the Xp QTL, and 1 SNP in chromosome 15q22 were associated with HbF in the CSSCD and also validated in the MSH. Four other SNPs in 15q22 were associated with HbF only in the larger CSSCD data. When patients aged <24 years in the CSSCD were examined, additional genes, including 4 with roles in nitric oxide metabolism, were associated with HbF level. These studies confirm prior analyses using traditional analytical approaches showing associations of SNPs in TOX, GPM6B, and the beta-globin gene-like cluster with HbF levels. We also identified an additional candidate regulatory region in chromosome 15q22 that is associated with HbF level. By stratifying patients by age, our results also suggest that different genes might modulate the rate of decline of HbF and the final level of HbF levels in sickle cell anemia.

Pharmacogenetics of glucose-lowering drug treatment: a systematic review

Intensive blood glucose lowering can significantly reduce the risk of micro- and macrovascular complications in patients with diabetes mellitus. However, 30% of all treated patients do not achieve optimal blood glucose levels. Genetic factors may influence the response to glucose-lowering medication. A search of MEDLINE-indexed literature published between January 1966 and July 2007 revealed 37 studies reporting data on genetic polymorphisms and response to glucose-lowering drugs. Most studies involving cytochrome P450 (CYP) genes had small sample sizes (21 studies <50 subjects) and were among healthy volunteers. Multiple studies indicated that the CYP2C9 *3 allele (Ile359Leu polymorphism) was associated with decreased clearance of sulfonylurea drugs. Supporting this, one study reported an increased insulin secretion in CYP2C9*3 allele carriers when using the sulfonylurea agent glyburide. The CYP2C9*3 allele was also associated with a decreased clearance of meglitinides, whereas the CYP2C8*3 (Arg139Lys; Lys399Arg) variant increased the clearance of meglitinides. Polymorphisms in genes encoding the inwardly rectifying potassium channel Kir6.2 (KCNJ11) and the insulin receptor substrate-1 (IRS1) were reported to be associated with an increased risk of (secondary) failure to respond to sulfonylurea therapy. A significant decrease in fasting plasma glucose and hemoglobin A(1c) (HbA(1c)) in response to rosiglitazone was seen in subjects carrying the Pro12Ala polymorphism of the peroxisome proliferator-activated receptor-gamma (PPARG) gene. Conversely, carriers of this polymorphism also had a higher conversion to diabetes mellitus when treated with acarbose; this effect was also seen in adiponectin (ADIPOQ) gene polymorphism carriers. Future studies with adequate sample sizes in which several SNPs in multiple candidate genes are genotyped in patients with diabetes should provide reliable information on genetic variants and response to glucose-lowering drugs.

Toll-like receptor 2 (TLR2) polymorphisms are associated with reversal reaction in leprosy

BACKGROUND

Leprosy is characterized by a spectrum of clinical manifestations that depend on the type of immune response against the pathogen. Patients may undergo immunological changes known as "reactional states" (reversal reaction and erythema nodosum leprosum) that result in major clinical deterioration. The goal of the present study was to assess the effect of Toll-like receptor 2 (TLR2) polymorphisms on susceptibility to and clinical presentation of leprosy.

METHODS

Three polymorphisms in TLR2 (597C-->T, 1350T-->C, and a microsatellite marker) were analyzed in 431 Ethiopian patients with leprosy and 187 control subjects. The polymorphism-associated risk of developing leprosy, lepromatous (vs. tuberculoid) leprosy, and leprosy reactions was assessed by multivariate logistic regression models.

RESULTS

The microsatellite and the 597C-->T polymorphisms both influenced susceptibility to reversal reaction. Although the 597T allele had a protective effect (odds ratio [OR], 0.34 [95% confidence interval {CI}, 0.17-0.68]; P= .002 under the dominant model), homozygosity for the 280-bp allelic length of the microsatellite strongly increased the risk of reversal reaction (OR, 5.83 [95% CI, 1.98-17.15]; P= .001 under the recessive model). These associations were consistent among 3 different ethnic groups.

CONCLUSIONS

These data suggest a significant role for TLR-2 in the occurrence of leprosy reversal reaction and provide new insights into the immunogenetics of the disease.