Statistical Genomics and Bioinformatics

Linkage analysis in tetraploid potato using dosage information

In the past linkage maps have generally been constructed from binary data indicating the presence or absence of short sequences of DNA in parents and offspring. Modern technologies can give more quantitative measurements, for example the Illumina technology measures the ratio of fluorescence intensities for the two different alleles of a single nucleotide polymorphism site (SNP). In a tetraploid species, such as the cultivated potato, this ratio (referred to as the theta score) can be used to identify the five possible genotypes at a SNP: AAAA, AAAB, AABB, ABBB and BBBB. In a mapping population, the expected genotype frequencies among the offspring can be derived from the parental genotype dosages (the number of A or B alleles) for comparison with the observed frequencies. Many more SNPs provide useful information about genotype frequencies based on dosage than using presence/absence information alone. Likewise, recombination frequencies between pairs of SNPs can be estimated from their joint dosages with higher precision than just using presence/absence information, thus giving greater precision to the ordering of SNPs on a linkage map. By making use of the potential of dosage information we have increased the density and resolution of the potato linkage map, with more than 3800 SNP locations known. QTL mapping of the theta scores provided a confirmation of the location and dosage.

theta scores The theta scores for SNP c1_10069 show five genotype classes, with the two parents (black and red stars) in the middle category. The offspring proportions are in the 1:8:18:8:1 ratio consistent with both parents having a doubleduplex markers (AABB x AABB).


chromosome V QTL mapping The top 20 cM of potato chromosome V. QTL mapping of the intensity ratio for SNP c2_23831 confirms its location at 6cM.

This work has been published in

Software to run these analyses can be obtained from:


Further details from: Christine Hackett

Article date 2013


Statistical Genomics and Bioinformatics

Process and Systems Modelling

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