Interpolated positions of the Affymetrix and Illumina GWA SNP panels

If you use these maps please cite our papers:

Rutgers Map v2 (second generation):
Matise, TC et al. 2007. A second-generation combined linkage physical map of the human genome. Genome Res 17:1783-6. PMID: 17989245 PMCID: PMC2099587.

Rutgers Map v3 (third generation) - main manuscript is in preparation for submission in 2017:
Nato, AQ, Buyske, S, and Matise, TC. 2012. The Rutgers map: A third-generation combined linkage-physical map of the human genome. Human Genetics Institute of New Jersey Second Research Day. Life Sciences Building, Rutgers University, Piscataway, NJ, USA. 18 April 2012.

Note: If your analysis requires interpolated positions based on the Haldane map function, you may download, parse the markers in your interpolated panel that are also in Rutgers Map v.3, and linearly interpolate the Haldane map positions of the markers that are not present in our map based on the flanking markers with Haldane map positions. For the linkage panels below, we have converted the interpolated Kosambi map positions into their respective positions based on the Haldane map function. These are therefore panel-specific, i.e., the Haldane map position of a particular marker in a panel would be almost but not exactly the same across the different panels (and may also be slightly different from its Haldane map position in since it was calculated based on the existing markers within that panel. Larger differences may be observed for the male Haldane map position of a marker within the pseudoautosomal region 2 (PAR 2) on chromosome X when compared with its corresponding male Haldane map position in depending on the markers in a specific panel. We are currently working on merging the markers in Rutgers Map v.3 with the markers from all of the panels that we interpolated so that each marker will have exactly the same Haldane map position across the different panels.

Download the files through the links below:

Build 37

Build 36

© 2014 Matise Laboratory of Computational Genetics