LASER stands for "Locating Ancestry from SEquence Reads". This program can estimate individual ancestry by directly analyzing shotgun
sequence reads without calling genotypes. LASER uses principal components analysis (PCA) and Procrustes analysis to analyze sequence
reads of each sample and place the sample into a reference PCA space constructed using genotypes of a set of reference individuals.
With an appropriate reference panel, the estimated coordinates of the sequence samples reflect their ancestral background and can be
used to correct for population stratification in association studies. LASER can accurately estimate ancestry even with modest amounts
of data, such as the off-target sequence data generated by targeted sequencing experiments.
The LASER program and a detailed manual can be downloaded here.
Citation for LASER is:
C Wang*, X Zhan*, J Bragg-Gresham, HM Kang, D Stambolian, E Chew, K Branham, J Heckenlively, The FUSION Study, RS Fulton, RK Wilson, ER Mardis, X Lin, A Swaroop, S Zöllner, GR Abecasis (2014). Ancestry estimation and control of population stratification for sequence-based association studies. Nature Genetics, 46: 409-415. (* Joint first author) [link]
MicroDrop is a C++ program for estimating and correcting for allelic dropout in microsatellite data when replicated genotypes are
not available. Based on an allele frequency model, the program implements an expectation-maximization algorithm to search for
maximum-likelihood estimates of the allele frequencies, sample-specific and locus-specific dropout rates, and an inbreeding coefficient.
With the estimated parameter values, an empirical Bayesian strategy is used to prepare multiple imputed data sets to circumvent allelic
dropout in downstream data analyses.
The MicroDrop program and a detailed manual can be downloaded here.
Citation for MicroDrop is:
C Wang, KB Schroeder, NA Rosenberg (2012). A maximum-likelihood method to correct for allelic dropout in microsatellite data with no replicate genotypes. Genetics 192: 651-669. [link]