Large-scale Single Nucleotide Polymorphism Genotyping Research Articles

Population structure at different minor allele frequency levels.

Inferring population genetic structure from large-scale genotyping of single-nucleotide polymorphisms or variants is an important technique for studying the history and distribution of extant human populations, but it is also a very important tool for adjusting tests of association. However, the structures inferred depend on the minor allele frequency of the variants; this is very important when considering the phenotypic association of rare variants.Using the Genetic Analysis Workshop 18 data set for 142 unrelated individuals, which includes genotypes for many rare variants, we study the following hypothesis: the difference in detected structure is the result of a "scale" effect; that is, rare variants are likely to be shared only locally (smaller scale), while common variants can be spread over longer distances. The result is similar to that of using kernel principal component analysis, as the bandwidth of the kernel is changed. We show how different structures become evident as we consider rare or common variants.

Simpute: An Efficient Solution for Dense Genotypic Data

Single nucleotide polymorphism (SNP) data derived from array-based technology or massive parallel sequencing are often flawed with missing data. Missing SNPs can bias the results of association analyses. To maximize information usage, imputation is often adopted to compensate for the missing data by filling in the most probable values. To better understand the available tools for this purpose, we compare the imputation performances among BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK with data generated by randomly masking the genotype data from the International HapMap Phase III project. In addition, we propose a new algorithm called simple imputation (Simpute) that benefits from the high resolution of the SNPs in the array platform. Simpute does not require any reference data. The best feature of Simpute is its computational efficiency with complexity of order (mw + n), where n is the number of missing SNPs, w is the number of the positions of the missing SNPs, and m is the number of people considered. Simpute is suitable for regular screening of the large-scale SNP genotyping particularly when the sample size is large, and efficiency is a major concern in the analysis.

A Nanoliter Fluidic Platform for Large-Scale Single Nucleotide Polymorphism Genotyping

Discovery, evaluation, and understanding the biological relevance of single nucleotide polymorphisms (SNPs) and their associated phenotypes is relevant to many applications, including human disease diagnostics, pathogen detection, and identification of genetic traits impacting agricultural practices, both in terms of food quality and production efficiency. Validation of putative SNP associations in large-scale cohorts is currently impeded by the technical challenges and high cost inherent in analyzing large numbers of samples using available SNP genotyping platforms. We describe in this report the implementation of the 5'-exonuclease, biallelic PCR assay for SNP genotyping (TaqMan) in a nanofluidic version of a high-density microplate. System performance was assessed using a panel of 32 TaqMan SNP genotyping assays targeted to human polymorphisms. This functional test of the nanoliter fluidic SNP genotyping platform delivered genotyping call rates and accuracies comparable to the same larger volume reactions in microplate systems.

Genome complexity reduction for SNP genotyping analysis.

Efficient single nucleotide polymorphism (SNP) genotyping methods are necessary to accomplish many current gene discovery goals. A crucial element in large-scale SNP genotyping is the number of individual biochemical reactions that must be performed. An efficient method that can be used to simultaneously amplify a set of genetic loci across a genome with high reliability can provide a valuable tool for large-scale SNP genotyping studies. In this paper we describe and characterize a method that addresses this goal. We have developed a strategy for reducing genome complexity by using degenerate oligonucleotide primer (DOP)-PCR and applied this strategy to SNP genotyping in three complex eukaryotic genomes; human, mouse, and Arabidopsis thaliana. Using a single DOP-PCR primer, SNP loci spread throughout a genome can be amplified and accurately genotyped directly from a DOP-PCR product mixture. DOP-PCRs are extremely reproducible. The DOP-PCR method is transferable to many species of interest. Finally, we describe an in silico approach that can effectively predict the SNP loci amplified in a given DOP-PCR, permitting the design of an efficient set of reactions for large-scale, genome-wide SNP studies.

Searching for susceptibility genes in schizophrenia

The existence of an important genetic contribution to the aetiology of schizophrenia is well established from genetic epidemiological studies. However, the mode of transmission is complex and non-Mendelian. The main approaches used to identify susceptibility genes are linkage and association studies and the study of cytogenetic abnormalities associated with or linked to schizophrenia. Many linkage studies have been reported but have failed as yet to produce unequivocal, replicated demonstrations of linkage. However, modest evidence for several regions has been reported in more than one data set. Areas implicated include chromosome 22q11-12, 6p24-22, 6q, 8p22-21, 13q14.1-q32 and 1q21-q22, but in every case there are positive as well as negative findings. Most candidate gene studies have been based upon neuropharmacological studies suggesting that abnormalities in monoamine neurotransmission play a role in the aetiology of schizophrenia. Overall, the results have been disappointing, but it should be noted that the sample sizes in many of the older studies would now generally be regarded as inadequate. Finally, recent work has suggested that velo-cardio-facial syndrome (VCFS) is associated with rates of psychosis possibly as high as 30%. VCFS is caused by small interstitial deletions of chromosome 22q11 in 80–85% of individuals. Work is now under way to try and identify whether a gene or genes within the deleted region are of more general relevance to schizophrenia. Future directions in schizophrenia research include collecting larger samples to increase power of findings and applying novel methods for large-scale genotyping of single-nucleotide polymorphisms.