Individual Genotype Data Research Articles

Biological Pathway‐Based Genome‐Wide Association Analysis Identified the Vasoactive Intestinal Peptide (VIP) Pathway Important for Obesity

Recent genome-wide association (GWA) studies have identified a number of novel genes/variants predisposing to obesity. However, most GWA studies have focused on individual single-nucleotide polymorphism (SNPs)/genes with a strong statistical association with a phenotypic trait without considering potential biological interplay of the tested genes. In this study, we performed biological pathway-based GWA analysis for BMI and body fat mass. We used individual level genotype data generated from 1,000 unrelated US whites that were genotyped for ~500,000 SNPs. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm. A total of 963 pathways extracted from the BioCarta, Kyoto Encyclopedia of Genes and Genomes (KEGG), Ambion GeneAssist, and Gene Ontology (GO) databases were analyzed. Among all of the pathways analyzed, the vasoactive intestinal peptide (VIP) pathway was most strongly associated with fat mass (nominal P = 0.0009) and was the third most strongly associated pathway with BMI (nominal P = 0.0006). After multiple testing correction, the VIP pathway achieved false-discovery rate (FDR) q values of 0.042 and 0.120 for fat mass and BMI, respectively. Our study is the first to demonstrate that the VIP pathway may play an important role in development of obesity. The study also highlights the importance of pathway-based GWA analysis in identification of additional genes/variants for complex human diseases.

Evaluation of polymorphisms in predicted target sites for micro RNAs differentially expressed in endometriosis

Previous microarray analyses identified 22 microRNAs (miRNAs) differentially expressed in paired ectopic and eutopic endometrium of women with and without endometriosis. To investigate further the role of these miRNAs in women with endometriosis, we conducted an association study aiming to explore the relationship between endometriosis risk and single-nucleotide polymorphisms (SNPs) in miRNA target sites for these differentially expressed miRNAs. A panel of 102 SNPs in the predicted miRNA binding sites were evaluated for an endometriosis association study and an ingenuity pathway analysis was performed. Fourteen rare variants were identified in this study. We found SNP rs14647 in the Wolf-Hirschhorn syndrome candidate gene1 (WHSC1) 3'UTR (untranslated region) was associated with endometriosis-related infertility presenting an odds ratio of 12.2 (95% confidence interval = 2.4-60.7, P = 9.03 × 10(-5)). SNP haplotype AGG in the solute carrier family 22, member 23 (SLC22A23) 3'UTR was associated with endometriosis-related infertility and more severe disease. With the individual genotyping data, ingenuity pathways analysis identified the tumour necrosis factor and cyclin-dependant kinase inhibitor as major factors in the molecular pathways. Significant associations between WHSC1 alleles and endometriosis-related infertility and SLC22A23 haplotypes and the disease severe stage were identified. These findings may help focus future research on subphenotypes of this disease. Replication studies in independent large sample sets to confirm and characterize the involvement of the gene variation in the pathogenesis of endometriosis are needed.

A study of the efficiency of pooling in haplotype estimation

It has been claimed in the literature that pooling DNA samples is efficient in estimating haplotype frequencies. There is, however, no theoretical justification based on calculation of statistical efficiency. In fact, the limited evidence given so far is based on simulation studies with small numbers of loci. With rapid advance in technology, it is of interest to see if pooling is still efficient when the number of loci increases. Instead of resorting to simulation studies, we make use of asymptotic statistical theory to perform exact calculation of the efficiency of pooling relative to no pooling in the estimation of haplotype frequencies. As an intermediate step, we use the log-linear formulation of the haplotype probabilities and derive the asymptotic variance-covariance matrix of the maximum likelihood estimators of the canonical parameters of the log-linear model. Based on our calculations under linkage equilibrium, pooling can suffer huge loss in efficiency relative to no pooling when there are more than three independent loci and the alleles are not rare. Pooling works better for rare alleles. In particular, if all the minor allele frequencies are 0.05, pooling maintains an advantage over no pooling until the number of independent loci reaches 6. High linkage disequilibrium effectively reduces the number of independent loci by ruling out certain haplotypes from occurring. Similar calculations of efficiency for the case of no pooling justify the common belief that it is not worthwhile to use molecular methods to resolve the phase ambiguity of individual genotype data. The R codes for the calculation are available at http://www.stat.nus.edu.sg/∼staxj/pooling stakuka@nus.edu.sg.

Resequencing of pooled DNA for detecting disease associations with rare variants

A combination of common and rare variants is thought to contribute to genetic susceptibility to complex diseases. Recently, next-generation sequencers have greatly lowered sequencing costs, providing an opportunity to identify rare disease variants in large genetic epidemiology studies. At present, it is still expensive and time consuming to resequence large number of individual genomes. However, given that next-generation sequencing technology can provide accurate estimates of allele frequencies from pooled DNA samples, it is possible to detect associations of rare variants using pooled DNA sequencing. Current statistical approaches to the analysis of associations with rare variants are not designed for use with pooled next-generation sequencing data. Hence, they may not be optimal in terms of both validity and power. Therefore, we propose here a new statistical procedure to analyze the output of pooled sequencing data. The test statistic can be computed rapidly, making it feasible to test the association of a large number of variants with disease. By simulation, we compare this approach to Fisher's exact test based either on pooled or individual genotypic data. Our results demonstrate that the proposed method provides good control of the Type I error rate, while yielding substantially higher power than Fisher's exact test using pooled genotypic data for testing rare variants, and has similar or higher power than that of Fisher's exact test using individual genotypic data. Our results also provide guidelines on how various parameters of the pooled sequencing design affect the efficiency of detecting associations.

Rapid Assessment of Genetic Ancestry in Populations of Unknown Origin by Genome-Wide Genotyping of Pooled Samples

As we move forward from the current generation of genome-wide association (GWA) studies, additional cohorts of different ancestries will be studied to increase power, fine map association signals, and generalize association results to additional populations. Knowledge of genetic ancestry as well as population substructure will become increasingly important for GWA studies in populations of unknown ancestry. Here we propose genotyping pooled DNA samples using genome-wide SNP arrays as a viable option to efficiently and inexpensively estimate admixture proportion and identify ancestry informative markers (AIMs) in populations of unknown origin. We constructed DNA pools from African American, Native Hawaiian, Latina, and Jamaican samples and genotyped them using the Affymetrix 6.0 array. Aided by individual genotype data from the African American cohort, we established quality control filters to remove poorly performing SNPs and estimated allele frequencies for the remaining SNPs in each panel. We then applied a regression-based method to estimate the proportion of admixture in each cohort using the allele frequencies estimated from pooling and populations from the International HapMap Consortium as reference panels, and identified AIMs unique to each population. In this study, we demonstrated that genotyping pooled DNA samples yields estimates of admixture proportion that are both consistent with our knowledge of population history and similar to those obtained by genotyping known AIMs. Furthermore, through validation by individual genotyping, we demonstrated that pooling is quite effective for identifying SNPs with large allele frequency differences (i.e., AIMs) and that these AIMs are able to differentiate two closely related populations (HapMap JPT and CHB).

Public Access to Genome-Wide Data: Five Views on Balancing Research with Privacy and Protection

Just over twelve months ago, PLoS Genetics published a paper [1] demonstrating that, given genome-wide genotype data from an individual, it is, in principle, possible to ascertain whether that individual is a member of a larger group defined solely by aggregate genotype frequencies, such as a forensic sample or a cohort of participants in a genome-wide association study (GWAS). As a consequence, the National Institutes of Health (NIH) and Wellcome Trust agreed to shut down public access not just to individual genotype data but even to aggregate genotype frequency data from each study published using their funding. Reactions to this decision span the full breadth of opinion, from “too little, too late—the public trust has been breached” to “a heavy-handed bureaucratic response to a practically minimal risk that will unnecessarily inhibit scientific research.” Scientific concerns have also been raised over the conditions under which individual identity can truly be accurately determined from GWAS data. These concerns are addressed in two papers published in this month's issue of PLoS Genetics [2],[3]. We received several submissions on this topic and decided to assemble these viewpoints as a contribution to the debate and ask readers to contribute their thoughts through the PLoS online commentary features. Five viewpoints are included. The Public Population Project in Genomics (P3G) is calling for a universal researcher ID with an access permit mechanism for bona fide researchers. The contribution by Catherine Heeney, Naomi Hawkins, Jantina de Vries, Paula Boddington, and Jane Kaye of the University of Oxford Ethox Centre outlines some of the concerns over possible misuse of individual identification in conjunction with medical and family history data, and points out that if geneticists mishandle public trust, it will backfire on their ability to conduct further research. George Church posits that actions directed toward restricting data access are likely to exclude researchers who might provide the most novel insights into the data and instead makes the argument that full disclosure and consent to the release of genomic information should be sought from study participants, rather than making difficult-to-guarantee promises of anonymity. Martin Bobrow weighs the risks and benefits and proposes four steps that represent a middle ground: Retain restricted access for now, make malicious de-identification practices illegal, increase public awareness of the issues, and encourage recognition that scientists have a special professional relationship of trust with study participants. Finally, Bruce Weir provides a commentary on the contribution of the two research articles from Braun et al. [2] and Visscher and Hill [3].

Genome-wide association database developed in the Japanese Integrated Database Project

The establishment of high-throughput single-nucleotide polymorphism (SNP)-typing technologies has enabled astonishing progress to be made in genome-wide association studies (GWAS), and various novel genetic factors associated with complex diseases have been discovered. Our organization has created a public repository database (DB) to achieve a continuous and intensive management of GWAS data and to facilitate data sharing among researchers. In the GWAS DB, information on study design, quality control protocols, allele frequencies, genotype frequencies and statistical genetic analysis results are stored as publicly available data and can be accessed freely, whereas individual genotyping data and raw data are stored as restricted data and can only be accessed with authorization. All data are presented by a graphic viewer, which is designed to be user friendly for researchers who are not familiar with GWAS to accelerate disease-related studies. Furthermore, the DB allows users to compare various study results obtained by different institutions and on different platforms. The same data are also managed as a distributed annotation system to call up useful data from other DBs and to superimpose them on the GWAS data for help in interpretation. The DB is accessible at https://gwas.lifesciencedb.jp/.

A Classifier-based approach to identify genetic similarities between diseases

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease.Results: We apply this approach using a decision tree classifier to the genotype data of seven common diseases and two shared control sets provided by the Wellcome Trust Case Control Consortium. We show that this approach identifies the known genetic similarity between type 1 diabetes and rheumatoid arthritis, and identifies a new putative similarity between bipolar disease and hypertension.Contact: serafim@cs.stanford.edu

Issues concerning feedback about genetic testing and risk of depression

Recent studies show that adverse life events have a significantly greater impact on depression onset for those with the s/s allele of the genotype for the 5-HT gene-linked promoter region. Research in genes related to risk of depression leads to the question of how this information is received by individuals. To investigate factors related to the response to receiving one's own serotonin transporter genotype results. Predictors of the impact of receiving individual genotype data were assessed in 128 participants in a study of gene-environment interaction in depression onset. Two-thirds decided to learn their individual genotype results (receivers) and prior to disclosure this decision was associated with a perception of greater benefit from receipt of the information (P=0.001). Receivers completing the 2-week (n=76) and 3-month follow-up (n=78) generally reported feeling pleased with the information and having had a more positive experience than distress. However, distress was related to genotype, with those with the s/s allele being most affected. There was high interest in, and satisfaction with, learning about one's serotonin transporter genotype. Participants appeared to understand that the gene conferred susceptibility to depression rather than a direct causal effect.

Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach.

OBJECTIVERecent genome-wide association studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.RESEARCH DESIGN AND METHODSWe used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases.RESULTSAfter correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top Padj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.CONCLUSIONSCommon variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.

An informative set of SSLP markers and genomic profiles in the rat MHC, the RT1 complex

Almost 10,000 single nucleotide polymorphisms (SNPs) had been identified in the RT1 complex, the major histocompatibility complex of the rat, but less than approximately 0.5% have been characterized. In the context of the incomplete characterization of most SNPs, simple sequence length polymorphism (SSLP) marker development is still valuable for understanding the involvement of genes in the RT1 in controlling disease susceptibility, since SSLPs are user-friendly and cost-effective genetic markers in rat genome analysis. In this study, we developed a set of 67 SSLP markers, including 57 novel markers, to cover the entire RT1 complex and then created genetic profiles across 67 rat strains. These markers are located almost every 50 kb in the RT1 complex and show comparable polymorphism; the average number of alleles was 8.04 +/- 3.44 and the average polymorphic rate was 71 +/- 23%. Interestingly, markers failing to amplify polymerase chain reaction products were highly observed in all strains except for BN/SsNHsd, which suggests the existence of highly variable genomic sequences or genomic rearrangements in the RT1 region across rat strains. Based on the phylogenic tree and individual genotyping data, we identified 28 SSLP marker haplotypes in the RT1 region that roughly consisted of three genomic regions. These findings provided new insight into the genomic organization of the RT1 complex and we recognized the need of additional RT1 genome sequences in different strains. Owing to the accuracy and ease of determination, PCR-based SSLP genotyping could replace serological typing in genetic analyses and characterization of rat major histocompatibility.

Investigation of the fine structure of European populations with applications to disease association studies

An investigation into fine-scale European population structure was carried out using high-density genetic variation on nearly 6000 individuals originating from across Europe. The individuals were collected as control samples and were genotyped with more than 300 000 SNPs in genome-wide association studies using the Illumina Infinium platform. A major East-West gradient from Russian (Moscow) samples to Spanish samples was identified as the first principal component (PC) of the genetic diversity. The second PC identified a North-South gradient from Norway and Sweden to Romania and Spain. Variation of frequencies at markers in three separate genomic regions, surrounding LCT, HLA and HERC2, were strongly associated with this gradient. The next 18 PCs also accounted for a significant proportion of genetic diversity observed in the sample. We present a method to predict the ethnic origin of samples by comparing the sample genotypes with those from a reference set of samples of known origin. These predictions can be performed using just summary information on the known samples, and individual genotype data are not required. We discuss issues raised by these data and analyses for association studies including the matching of case-only cohorts to appropriate pre-collected control samples for genome-wide association studies.

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.

AFLPs reveal an absence of geographical genetic structure among remnant populations of pohutukawa (Metrosideros excelsa, Myrtaceae)

Ten populations spanning the natural geographical range of Metrosideros excelsa (pohutukawa) were analysed using AFLP. All populations were highly variable showing levels of genetic diversity similar to other long‐lived outcrossing tree species. UPGMA analysis of interpopulation genetic distances and multivariate analysis of individual multilocus genotype data showed genetic differentiation among populations, but there was no obvious relationship between genetic structure and geographical location. There was also no evidence of greater genetic variation in areas of New Zealand that have been proposed to be glacial refugia. These data provide a contrast to previously published observations on M. excelsa using chloroplast DNA, which showed that plants from putative glacial refugia exhibited higher levels of variation than those from non‐refugial areas.

A genome-wide association study of sporadic ALS in a homogenous Irish population

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive limb or bulbar weakness. Efforts to elucidate the disease-associated loci have to date produced conflicting results. One strategy to improve power in genome-wide studies is to genotype a genetically homogenous population. Such a population exhibits extended linkage disequilibrium (LD) and lower allelic heterogeneity to facilitate disease gene mapping. We sought to identify associated variants for ALS in the Irish, a stable population of relatively homogenous genetic background, and to replicate these findings in larger genetically out-bred populations. We conducted a genome-wide association study in 432 Irish individuals using Illumina HumanHap 550K single nucleotide polymorphism chips. We demonstrated extended LD and increased homogeneity in the Irish sample when compared to an out-bred population of mixed European ancestry. The Irish scan identified 35 loci associated with P-values below 0.0001. For replication, we identified seven chromosomal regions commonly associated in a joint analysis of genome-wide data on 958 ALS cases and 932 controls from Ireland and the previously published datasets from the US and The Netherlands. When pooled, the strongest association was a variant in the gene encoding DPP6, a component of type A neuronal transmembrane potassium channels. Further confirmation of the candidate loci is warranted in additional genome-wide datasets. We have made our individual genotyping data publicly available, contributing to a powerful world-wide resource to refine our understanding of the genetics of sporadic ALS.

Measuring European Population Stratification with Microarray Genotype Data

A proper understanding of population genetic stratification--differences in individual ancestry within a population--is crucial in attempts to find genes for complex traits through association mapping. We report on genomewide typing of approximately 10,000 single-nucleotide polymorphisms in 297 individuals, to explore population structure in Europeans of known and unknown ancestry. The results reveal the presence of several significant axes of stratification, most prominently in a northern-southeastern trend, but also along an east-west axis. We also demonstrate the selection and application of EuroAIMs (European ancestry informative markers) for ancestry estimation and correction. The Coriell Caucasian and CEPH (Centre d'Etude du Polymorphisme Humain) Utah sample panels, often used as proxies for European populations, are found to reflect different subsets of the continent's ancestry.

Primers from the orders Osteoglossiform and Siluriform detect polymorphic microsatellite loci in sun-catfish, Horabagrus brachysoma (Teleostei: Bagridae)

Summary Horabagrus brachysoma (sun-catfish, Bagridae, Siluriformes) is a valuable ornamental and food fish. The stock structure of H. brachysoma, necessary to conserve its declining natural populations, is not known. Twenty-five primers developed for four fish species belonging to the orders Siluriform (3) and Osteoglossiform (1) were tested and eight primers amplified microsatellite loci in H. brachysoma. The results demonstrate that cross-priming between fish species belonging to different families and even to different orders can yield microsatellite loci. Five of eight primers each amplified two loci. However, the loci that had repeat motifs after sequencing were considered only for genotyping. Finally, eight loci were polymorphic with hree to seven alleles. Individual fish genotype data (n = 42; 21 each in two rivers) at each locus was analysed. Significant genetic heterogeneity was detected at six loci. The identified loci exhibited potential for use in population genetics application in H. brachysoma.

Global Pharmacogenetics: Giving the Genome to the Masses

With pharmacogenetics comes the promise of individualized therapy selection for many common diseases where multiple treatment options are available. Recent advances including the Human Genome Project, the International HapMap project, advances in throughput and reduction in cost of genetic testing, and the inclusion of genotype-related dosing recommendations into package inserts all point to the integration of pharmacogenetics into clinical practice. However, many countries will not have access to pharmacogenetics resources in the near future. Generation of global genotype profiles will provide a useful, but not perfect resource for incorporating pharmacogenetics into national drug formularies in the form of prioritization or surveillance where individual genotype data would not be attainable. The PharmacoGenetics for Every Nation Initiative is a first step to making pharmacogenetics applicable on a global level.

APOE epsilon variation in multiple sclerosis susceptibility and disease severity

Previous studies have examined the role of APOE variation in multiple sclerosis (MS), but have lacked the statistical power to detect modest genetic influences on risk and disease severity. The meta- and pooled analyses presented here utilize the largest collection, to date, of MS cases, controls, and families genotyped for the APOE epsilon polymorphism. Studies of MS and APOE were identified by searches of PubMed, Biosis, Web of Science, Cochrane Review, and Embase. When possible, authors were contacted for individual genotype data. Meta-analyses of MS case-control data and family-based analyses were performed to assess the association of APOE epsilon genotype with disease risk. Pooled analyses of MS cases were also performed to assess the influence of APOE epsilon genotype on disease severity. A total of 22 studies (3,299 MS cases and 2,532 controls) were available for meta-analysis. No effect of epsilon2 or epsilon4 status on MS risk was observed (summary OR 1.14, 95% CI 0.96-1.34 and OR 0.89, 95% CI 0.78-1.01). Results obtained from analyses of APOE genotypes in 1,279 MS families were also negative (p = 0.61). Finally, results from pooled analyses of 4,048 MS cases also argue strongly that APOE epsilon status does not distinguish a relapsing-remitting from primary progressive disease course, or influence disease severity, as measured by the Expanded Disability Status Scale and disease duration. Overall, these findings do not support a role for APOE in multiple sclerosis, and underscore the importance of using large sample sizes to detect modest genetic effects, particularly in studies of genotype-phenotype relationships.

Multiple Imputation of Missing Genotype Data for Unrelated Individuals

The objective of this study was to investigate the performance of multiple imputation of missing genotype data for unrelated individuals using the polytomous logistic regression model, focusing on different missingness mechanisms, percentages of missing data, and imputation models. A complete dataset of 581 individuals, each analysed for eight biallelic polymorphisms and the quantitative phenotype HDL-C, was used. From this dataset one hundred replicates with missing data were created, in different ways for different scenarios. The performance was assessed by comparing the mean bias in parameter estimates, the root mean squared standard errors, and the genotype-imputation error rates. Overall, the mean bias was small in all scenarios, and in most scenarios the mean did not differ significantly from 'no bias'. Including polymorphisms that are highly correlated in the imputation model reduced the genotype-imputation error rate and increased precision of the parameter estimates. The method works well for data that are missing completely at random, and for data that are missing at random. In conclusion, our results indicate that multiple imputation with the polytomous logistic regression model can be used for association studies to deal with the problem of missing genotype data, when attention is paid to the imputation model and the percentage of missing data.