Estimating the effects of population size and type on the accuracy of genetic maps

Abstract

Based on simulation studies, it was shown that the type and size of experimental populations can exert an influence on the accuracy of genetic maps. A hypothetical genome map (one chromosome with nine equidistant molecular markers) was generated for the following population types: F 2 with dominant and co-dominant markers, backcrossing, recombinant inbred lines (RIL) and double-haploid. The population sizes were 50, 100, 150, 200, 500 and 1000 individuals and 100 simulations were made for each population. The inaccuracies of the populations with the lowest number of individuals were shown by inversions in the order of the markers and the establishment of more than one linkage group in up to 38% of the simulations, depending on the population type. Stress and variance values of the distances between adjacent markers were significantly reduced with the increased size of the population. More accurate maps were obtained for the co-dominant F 2 and RIL whereas the maps for the dominant F 2 population were less accurate. The higher the number of individuals, the more precise was the map. In all populations, a total of 200 individuals were considered as being sufficient for the construction of reasonably accurate genetic maps. Although this paper deals with plant populations this approach is equally applicable to other organisms.

Towards a unified genetic map for diploid roses

“…Thus for our F 1 populations, the accuracy of marker order and distance strongly depends on the population size, especially when dominant markers are used. This has previously been reported for F 2 and RIL populations (Ferreira et al 2006;Huhn and Piepho 2008 Doligez et al 2006), which means that few recombination events exist in large regions of the map, in combination with scoring errors and/or artifacts of the consensus mapping algorithm (Isobe et al 2009). Based on these experiences, we are currently increasing the number of genotyped individuals to several hundred in the populations 97/7 and HW for the sequence-based markers and some of the AFLP markers.…”

“…This coherence could also be shown by simulation studies using diVerent population structures (BC, F 2 and RIL) with dominant and codominant markers and progeny sizes from 50 to 1,000 individuals by Ferreira et al (2006), concluding that the reliability of a genetic map is strongly depending on the size of the experimental populations and therefore on the number of crossing over events which could be studied. Ferreira et al (2006) also reviews, that marker order inversions in genetic maps of all population types are a general problem in population sizes of about 100 individuals, generated both by the too small size of the population and the marker saturation of the map. This eVect of population size versus number of markers is also expressed in the reduction of gap sizes up to 48% in the 94/1, 97/7 and HW linkage maps compared to the LG1…”

Evaluation of SRAP markers for mapping of Pisum sativum L.

“…Both, the size of the mapping population and their origin affect the marker coverage and map length because an increasing divergence between parents generates a greater number of possible recombinations and the possibility of finding recombinant plants is higher when populations are large. Then, the small size of our F 2 population (45 plants) could be the cause of the smaller size of the map obtained in this study (Ferreira et al 2006). To enhance accuracy and reduce the statistical error, a great number of plants should be evaluated.…”

Chromosomal Evolution and Patterns of Introgression in Helianthus

“…Inverted segments were identified as two or more independent markers that exhibited reversed ordering between species; inverted segments at the terminal ends of LGs required the misordering of at least one terminal marker and two or more internal markers. Because establishing the correct map position of tightly linked markers in high-density linkage maps can be difficult due to duplicated loci, genotyping errors, segregation distortion, and chiasma interference (Hackett and Broadfoot 2003;Ferreira et al 2006;Cheema and Dicks 2009;Collard et al 2009), minor ordering errors can arise. As such, a 2-cM threshold was applied for declaring noncollinearity (Hudson et al 2011).…”