The Experts below are selected from a list of 196140 Experts worldwide ranked by ideXlab platform
Nathan L Tintle - One of the best experts on this subject based on the ideXlab platform.
-
assessing the impact of differential Genotyping errors on rare variant tests of association
PLOS ONE, 2013Co-Authors: Morgan Mayerjochimsen, Shannon M Fast, Nathan L TintleAbstract:Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential Genotyping errors, whereas Genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential Genotyping errors reduce power, while differential Genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of Genotyping errors. In this manuscript we use a comprehensive simulation study to explore the effects of numerous factors on the type I error rate of rare variant tests of association in the presence of differential Genotyping error. We find that increased sample size, decreased minor allele frequency, and an increased number of single nucleotide variants (SNVs) included in the test all increase the type I error rate in the presence of differential Genotyping errors. We also find that the greater the relative difference in case-control Genotyping error rates the larger the type I error rate. Lastly, as is the case for single marker tests, Genotyping errors classifying the common homozygote as the heterozygote inflate the type I error rate significantly more than errors classifying the heterozygote as the common homozygote. In general, our findings are in line with results from single marker tests. To ensure that type I error inflation does not occur when analyzing next-generation sequencing data careful consideration of study design (e.g. use of randomization), caution in meta-analysis and using publicly available controls, and the use of standard quality control metrics is critical.
-
assessing the impact of non differential Genotyping errors on rare variant tests of association
Human Heredity, 2011Co-Authors: Scott Powers, Shyam Gopalakrishnan, Nathan L TintleAbstract:Background/Aims: We aim to quantify the effect of non-differential Genotyping errors on the power of rare variant tests and identify those situations when Genotyping errors are most
-
incorporating duplicate genotype data into linear trend tests of genetic association methods and cost effectiveness
Statistical Applications in Genetics and Molecular Biology, 2009Co-Authors: Bryce Borchers, Marshall Brown, Brian Mclellan, Airat Bekmetjev, Nathan L TintleAbstract:The genome-wide association (GWA) study is an increasingly popular way to attempt to identify the causal variants in human disease. Duplicate Genotyping (or re-Genotyping) a portion of the samples in a GWA study is common, though it is typical for these data to be ignored in subsequent tests of genetic association. We demonstrate a method for including duplicate genotype data in linear trend tests of genetic association which yields increased power. We also consider the cost-effectiveness of collecting duplicate genotype data and find that when the relative cost of Genotyping to phenotyping and sample acquisition costs is less than or equal to the Genotyping error rate it is more powerful to duplicate genotype the entire sample instead of spending the same money to increase the sample size. Duplicate Genotyping is particularly cost-effective when SNP minor allele frequencies are low. Practical advice for the implementation of duplicate Genotyping is provided. Free software is provided to compute asymptotic and permutation based tests of association using duplicate genotype data as well as to aid in the duplicate Genotyping design decision.
Nicolas Peyret - One of the best experts on this subject based on the ideXlab platform.
-
the snplex Genotyping system a flexible and scalable platform for snp Genotyping
Journal of biomolecular techniques, 2005Co-Authors: Andreas R Tobler, Sabine Short, Mark R Andersen, Teodoro Paner, Jason C Briggs, Stephen M Lambert, Yiwen Wang, Alexander Y Spoonde, Ryan T Koehler, Nicolas PeyretAbstract:We developed the SNPlex Genotyping System to address the need for accurate Genotyping data, high sample throughput, study design flexibility, and cost efficiency. The system uses oligonucleotide ligation/polymerase chain reaction and capillary electrophoresis to analyze bi-allelic single nucleotide polymorphism genotypes. It is well suited for single nucleotide polymorphism Genotyping efforts in which throughput and cost efficiency are essential. The SNPlex Genotyping System offers a high degree of flexibility and scalability, allowing the selection of custom-defined sets of SNPs for medium- to high-throughput Genotyping projects. It is therefore suitable for a broad range of study designs. In this article we describe the principle and applications of the SNPlex Genotyping System, as well as a set of single nucleotide polymorphism selection tools and validated assay resources that accelerate the assay design process. We developed the control pool, an oligonucleotide ligation probe set for training and quality-control purposes, which interrogates 48 SNPs simultaneously. We present performance data from this control pool obtained by testing genomic DNA samples from 44 individuals. in addition, we present data from a study that analyzed 521 SNPs in 92 individuals. Combined, both studies show the SNPlex Genotyping system to have a 99.32% overall call rate, 99.95% precision, and 99.84% concordance with genotypes analyzed by TaqMan probe-based assays. The SNPlex Genotyping System is an efficient and reliable tool for a broad range of Genotyping applications, supported by applications for study design, data analysis, and data management.
Zachariah M Peery - One of the best experts on this subject based on the ideXlab platform.
-
finding the right coverage the impact of coverage and sequence quality on single nucleotide polymorphism Genotyping error rates
Molecular Ecology Resources, 2016Co-Authors: Emily D Fountain, Jonathan N Pauli, Brendan N Reid, Per J Palsboll, Zachariah M PeeryAbstract:Restriction-enzyme-based sequencing methods enable the Genotyping of thousands of single nucleotide polymorphism (SNP) loci in nonmodel organisms. However, in contrast to traditional genetic markers, Genotyping error rates in SNPs derived from restriction-enzyme-based methods remain largely unknown. Here, we estimated Genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother-offspring dyads of Hoffman's two-toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference-aligned and de novo-assembled data sets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo-assembled data sets. For example, coverage ≥5 yielded median Genotyping error rates of ≥0.03 and ≥0.11 in reference-aligned and de novo-assembled data sets, respectively. Genotyping error rates declined to ≤0.01 in reference-aligned data sets with a coverage ≥30, but remained ≥0.04 in the de novo-assembled data sets. We observed approximately 10- and 13-fold declines in the number of loci sampled in the reference-aligned and de novo-assembled data sets when coverage was increased from ≥5 to ≥30 at quality score ≥30, respectively. Finally, we assessed the effects of Genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the trade-off between sample size and Genotyping error rates be considered prior to building sequencing libraries, reporting Genotyping error rates become standard practice, and that effects of Genotyping errors on inference be evaluated in restriction-enzyme-based SNP studies.
Willem J. G. Melchers - One of the best experts on this subject based on the ideXlab platform.
-
Evaluation of the SPF10-INNO LiPA human papillomavirus (HPV) Genotyping test and the roche linear array HPV Genotyping test
Journal of Clinical Microbiology, 2006Co-Authors: Dennis Van Hamont, Maaike A.p.c. Van Ham, Judith M.j.e. Bakkers, Leon F A G Massuger, Willem J. G. MelchersAbstract:The need for accurate Genotyping of human papillomavirus (HPV) infections is becoming increasingly important, since (i) the oncogenic potential among the high-risk HPV genotypes varies in the pathogenesis of cervical cancer, (ii) monitoring multivalent HPV vaccines is essential to investigate the efficiency of the vaccines, and (iii) Genotyping is crucial in epidemiologic studies evaluating HPV infections worldwide. Various Genotyping assays have been developed to meet this demand. Comparison of different studies that use various HPV Genotyping tests is possible only after a performance assessment of the different assays. In the present study, the SPF(10) LiPA version 1 and the recently launched Roche Linear Array HPV Genotyping assays are compared. A total of 573 liquid-based cytology samples were tested for the presence of HPV by a DNA enzyme immunoassay; 210 were found to be positive for HPV DNA and were evaluated using both Genotyping assays (163 with normal cytology, 22 with atypical squamous cells of undetermined significance, 20 with mild/moderate dysplasia, and 5 with severe dysplasia). Comparison analysis was limited to the HPV genotype probes common to both assays. Of the 160 samples used for comparison analysis, 129 (80.6%) showed absolute agreement between the assays (concordant), 18 (11.2%) showed correspondence for some but not all genotypes detected on both strips (compatible), and the remaining 13 (8.2%) samples did not show any similarity between the tests (discordant). The overall intertest comparison agreement for all individually detectable genotypes was considered very good (kappa value, 0.79). The Genotyping assays were therefore highly comparable and reproducible.
Puiyan Kwok - One of the best experts on this subject based on the ideXlab platform.
-
Methods for Genotyping single nucleotide polymorphisms
Annual Review of Genomics and Human Genetics, 2001Co-Authors: Puiyan KwokAbstract:One of the fruits of the Human Genome Project is the discovery of millions ofDNAsequence variants in thehumangenome.Themajority of these variants are single nucleotide polymorphisms (SNPs).Adense set of SNP markers opens up the possibility of studying the genetic basis of complex diseases by population approaches. In all study designs, a large number of individuals must be genotyped with a large number of markers. In this review, the current status of SNP Genotyping is discussed in terms of the mechanisms of allelic discrimination, the reaction formats, and the detection modalities.Anumber of Genotyping methods currently in use are described to illustrate the approaches being taken. Although no single Genotyping method is ideally suited for all applications, a number of good Genotyping methods are available to meet the needs of many study designs. The challenges for SNP Genotyping in the near future include increasing the speed of assay development, reducing the cost of the assays, and performing multiple assays in parallel. Judging from the accelerated pace of new method development, it is hopeful that an ideal SNP Genotyping method will be developed soon.
