title: Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis.
abstract: BACKGROUND
  Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2).
  
  
  RESULTS
  Notably, MicroMerge v2 includes a new one-to-one alignment option that creates merged pedigree and locus files that can be handled by most genetic analysis software. Other features in MicroMerge v2 enhance the following aspects of control: 1) optimizing the algorithm for different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 2) merging small data sets when a reliable set of allele frequencies are available, and 3) improving the quantity and 4) quality of merged data. We present results from simulated and real microsatellite genotype data sets, and conclude with an association analysis of three familial dyslipidemia (FD) study samples genotyped at different laboratories. Independent analysis of each FD data set did not yield consistent results, but analysis of the merged data sets identified strong association at locus D11S2002.
  
  
  CONCLUSIONS
  The MicroMerge v2 features will enable merging for a variety of genotype data sets, which in turn will facilitate meta-analyses for powering association analysis.
authors.affiliation: Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu
authors.forename: Angela P
authors.initials: AP
authors.lastname: Presson
authors.forename: Eric M
authors.initials: EM
authors.lastname: Sobel
authors.forename: Paivi
authors.initials: P
authors.lastname: Pajukanta
authors.forename: Christopher
authors.initials: C
authors.lastname: Plaisier
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Weeks
authors.forename: Karolina
authors.initials: K
authors.lastname: Aberg
authors.forename: Jeanette C
authors.initials: JC
authors.lastname: Papp
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005787:Gene Frequency; D008040:Genetic Linkage; D005838:Genotype; D008722:Methods; D018895:Microsatellite Repeats / Q000235:genetics*; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods; D012984:Software