Ancestry informative markers for fine-scale individual assignment to worldwide populations

By Peristera Paschou, Jamey Lewis, Asif Javed, Petros Drineas. 2010

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In Genetics

Citations in the corpus

Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018, background): “The ﬁrst is biological data (such as gene expression data), where mislabeling or measurement errors can create systematic outliers [RPW + 02, LAT + 08] that require painstaking manual eﬀort to remove [PLJD10].”
A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog (2018, methodology): “Reference populations or ancestry informative markers [22] that allow populations to be distinguished have been characterized, and methods have been developed to adjust for population stratification and separate samples into clusters.”
Handbook of Big Data (2016, background): “In order to simply see some AN running, the reader may paste the following code into an R interpreter: source("http://stat.wharton.upenn.edu/~buja/association-navigator.R") p -200 mymatrix -matrix(rnorm(20000),ncol=p) colnames(mymatrix) - paste("V", 1:p, "_", c(rep("A",p/2),rep("B",p/2)), sep="")…”
A statistical perspective on algorithmic leveraging (2013, methodology): “, on Amazon Elastic Cloud Compute clusters; the solution to the `1 regression or least absolute deviations problem as well as to quantile regression problems can be approximated for problems with billions of constraints [6, 44]; and CUR-based low-rank matrix approximations [28] have been used for st”
Fast approximation of matrix coherence and statistical leverage (2011, background): “In genetics, dense matrices of size thousands by hundreds of thousands (a size scale at which even traditional deterministic QR algorithms fail to run) constructed from DNA Single Nucleotide Polymorphisms (SNP) data are increasingly common, and the statistical leverage scores can correlate strongly ”