Dataset Information

Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

ABSTRACT: In a genetic association study, it is often desirable to perform an overall test of whether any or all single-nucleotide polymorphisms (SNPs) in a gene are associated with a phenotype. Several such tests exist, but most of them are powerful only under very specific assumptions about the genetic effects of the individual SNPs. In addition, some of the existing tests assume that the direction of the effect of each SNP is known, which is a highly unlikely scenario. Here, we propose a new kernel-based association test of joint association of several SNPs. Our test is non-parametric and robust, and does not make any assumption about the directions of individual SNP effects. It can be used to test multiple correlated SNPs within a gene and can also be used to test independent SNPs or genes in a biological pathway. Our test uses an analysis of variance paradigm to compare variation between cases and controls to the variation within the groups. The variation is measured using kernel functions for each marker, and then a composite statistic is constructed to combine the markers into a single test. We present simulation results comparing our statistic to the U-statistic-based method by Schaid et al. ([2005] Am. J. Hum. Genet. 76:780-793) and another statistic by Wessel and Schork ([2006] Am. J. Hum. Genet. 79:792-806). We consider a variety of different disease models and assumptions about how many SNPs within the gene are actually associated with disease. Our results indicate that our statistic has higher power than other statistics under most realistic conditions.

SUBMITTER: Mukhopadhyay I

PROVIDER: S-EPMC3272581 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

Mukhopadhyay Indranil I Feingold Eleanor E Weeks Daniel E DE Thalamuthu Anbupalam A

Genetic epidemiology 20100401 3

In a genetic association study, it is often desirable to perform an overall test of whether any or all single-nucleotide polymorphisms (SNPs) in a gene are associated with a phenotype. Several such tests exist, but most of them are powerful only under very specific assumptions about the genetic effects of the individual SNPs. In addition, some of the existing tests assume that the direction of the effect of each SNP is known, which is a highly unlikely scenario. Here, we propose a new kernel-bas ...[more]

PMID: 19697357

Similar Datasets

Project description:ImportanceRetinoblastoma (RB) is the most common pediatric intraocular neoplasm. RB is a complex model in which atypical pathogenic variants, modifier genes, imprinting, and mosaicism are known to be associated with the phenotype. In-depth understanding of RB therefore requires large genotype-phenotype studies.ObjectiveTo assess the association between genotype and phenotype in patients with RB.Design, setting, and participantsThis single-center, retrospective cohort study, conducted from January 1, 2000, to September 30, 2017, enrolled 1404 consecutive ascertained patients with RB who consulted an oncogeneticist. All patients had their genotype and phenotype recorded. Statistical analysis was performed from July 1, 2018, to December 31, 2018.Main outcomes and measuresRB1 germline and somatic pathogenic variant types, family history, and disease presentation characteristics (ie, age at diagnosis, sex, laterality, and International Intraocular Retinoblastoma Classification group).ResultsAmong 1404 patients with RB (734 [52.3%] female; mean [SD] age, 20.2 [21.2] months), 866 cases (61.7%) were unilateral and 538 cases (38.3%) were bilateral. Loss of function variants were found throughout the coding sequence, with 259 of 272 (95.2%) somatic pathogenic variants and 537 of 606 (88.6%) germline pathogenic variants (difference, 6.6%; 95% CI, 4.0%-9.2%; P < .001) after excluding tumor-specific pathogenic variants (ie, promoter methylation and loss of heterozygosity); a novel low-penetrance region was identified in exon 24. Compared with germline pathogenic variants estimated to retain RB protein expression, germline pathogenic variants estimated to abrogate RB protein expression were associated with an earlier mean (SD) age at diagnosis (12.3 [11.3] months among 457 patients vs 16.3 [13.2] months among 55 patients; difference, 4 months; 95% CI, 1.9-6.1 months; P = .01), more frequent bilateral involvement (84.2% among 452 patients vs 65.2% among 45 patients; difference, 18.9%; 95% CI, 14.5%-23.3%; P < .001), and more advanced International Intraocular Retinoblastoma Classification group (85.3% among 339 patients vs 73.9% among 34 patients; difference: 11.4%; 95% CI, 6.5%-16.3%; P = .047). Among the 765 nongermline carriers of an RB1 pathogenic variant, most were female (419 females [54.8%] vs 346 males [45.2%]; P = .008), and males were more likely to have bilateral RB (23 males [71.4%] vs 12 females [34.3%]; P = .01).Conclusions and relevanceThese results suggest that RB risk is associated with the germline pathogenic variant and with maintenance of RB protein and that there is a sex-linked mechanism for nongermline carriers.

Project description:BackgroundStudies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods.ResultsIn this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions.ConclusionsOur KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.

Project description:BackgroundTwo single-nucleotide polymorphisms, rs1051730 and rs16969968, located within the nicotinic acetylcholine receptor gene cluster on chromosome 15q25 locus, are associated with heaviness of smoking, risk for lung cancer, and other smoking-related health outcomes. Previous studies have typically relied on self-reported smoking behavior, which may not fully capture interindividual variation in tobacco exposure.MethodsWe investigated the association of rs1051730 and rs16969968 genotype (referred to as rs1051730-rs16969968, because these are in perfect linkage disequilibrium and interchangeable) with both self-reported daily cigarette consumption and biochemically measured plasma or serum cotinine levels among cigarette smokers. Summary estimates and descriptive statistical data for 12 364 subjects were obtained from six independent studies, and 2932 smokers were included in the analyses. Linear regression was used to calculate the per-allele association of rs1051730-rs16969968 genotype with cigarette consumption and cotinine levels in current smokers for each study. Meta-analysis of per-allele associations was conducted using a random effects method. The likely resulting association between genotype and lung cancer risk was assessed using published data on the association between cotinine levels and lung cancer risk. All statistical tests were two-sided.ResultsPooled per-allele associations showed that current smokers with one or two copies of the rs1051730-rs16969968 risk allele had increased self-reported cigarette consumption (mean increase in unadjusted number of cigarettes per day per allele = 1.0 cigarette, 95% confidence interval [CI] = 0.57 to 1.43 cigarettes, P = 5.22 × 10(-6)) and cotinine levels (mean increase in unadjusted cotinine levels per allele = 138.72 nmol/L, 95% CI = 97.91 to 179.53 nmol/L, P = 2.71 × 10(-11)). The increase in cotinine levels indicated an increased risk of lung cancer with each additional copy of the rs1051730-rs16969968 risk allele (per-allele odds ratio = 1.31, 95% CI = 1.21 to 1.42).ConclusionsOur data show a stronger association of rs1051730-rs16969968 genotype with objective measures of tobacco exposure compared with self-reported cigarette consumption. The association of these variants with lung cancer risk is likely to be mediated largely, if not wholly, via tobacco exposure.

Dataset Information

Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

Publications

Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets