A genotype calling algorithm for affymetrix SNP arrays
By N. Rabbee, Terence P. Speed. Bioinform., 2006
335 citations recorded by the source corpus.
MOTIVATION A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8610c15b38d12e5e235556f7c33fbdf833f78e3e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Brian L. Browning
- Sharon R. Browning
- Mark J. Daly
- David Altshuler
- Shaun Purcell
- Steven A. McCarroll
- Mark I. McCarthy
- Lon R. Cardon
Citations in the corpus
- A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits (2009, methodology): “Genotypes were called using the Bayesian Robust Linear Modeling using Mahalanobis Distance (BRLMM) algorith”
- Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs (2008, methodology): “Larry Bird (the software) utilizes inferences made by earlier steps in Birdsuite: (1) copy-number calls for each sample at each CNP and CNV, from Canary and Birdseye; (2) the imputed locations (in A/B intensity space) of clusters corresponding to copy-variable imputed SNP clusters provided by Birdse”
- Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. (2007, methodology): “The genotypes were called using the Bayesian robust linear model with Mahalanobis distance classifier (BRLMM) algorithm (an extension of RLMM).(27) Genotypes with a BRLMM score !0.”