Efficient multipoint mapping: making use of dominant repulsion-phase markers

Abstract

The paper is devoted to the problem of multipoint gene ordering with a particular focus on "dominance" complication that acts differently in conditions of coupling-phase and repulsion-phase markers. To solve the problem we split the dataset into two complementary subsets each containing shared codominant markers and dominant markers in the coupling-phase only. Multilocus ordering in the proposed algorithm is based on pairwise recombination frequencies and using the well-known travelling salesman problem (TSP) formalization. To obtain accurate results, we developed a multiphase algorithm that includes synchronized-marker ordering of two subsets assisted by re-sampling-based map verification, combining the resulting maps into an integrated map followed by verification of the integrated map. A new synchronized Evolution-Strategy discrete optimization algorithm was developed here for the proposed multilocus ordering approach in which common codominant markers facilitate stabilization of the marker order of the two complementary maps. High performance of the employed algorithm allows systematic treatment for the problem of verification of the obtained multilocus orders, based on computing-intensive bootstrap and jackknife technologies for detection and removing unreliable marker scores. The efficiency of the proposed algorithm was demonstrated on simulated and real data.

A comparison of recombination frequencies in intraspecific versus interspecific mapping populations of Nasonia

“…Marker segregation data were analyzed with the software Multipoint (http://multiqtl.com), which infers marker order and map distances using a multipoint likelihood approach (Mester et al, 2003a(Mester et al, ,b, 2004. We calculated pairwise recombination fractions for all pairs of markers using the Kosambi mapping function (Kosambi, 1944).…”

A New Strategy for Estimating Recombination Fractions Between Dominant Markers From an F2 Population

“…An important issue in the estimation of the recombination fraction is how to efficiently deal with different linkage phases between a pair of dominant loci (Mester et al 2003a). Two different linkage phases for a double heterozygote are well recognized.…”

“…One is known as the repulsion phase, which corresponds to the situation in which these two dominant alleles reside on different chromosomes; otherwise, it is known as the coupling phase. In a twopoint analysis that considers two markers at a time, the repulsion phase provides much less information about linkage than the coupling phase (Allard 1956;Knapp et al 1995;Liu 1998;Mester et al 2003a). This is especially true for double heterozygotes from the F 2 population (Liu 1998).…”

“…In reality, about half of the markers are in the coupling phase and the remaining markers are in the other coupling phase. The phase between two couplings is repulsion (Liu 1998;Mester et al 2003a). This leads in practice to the construction of two separate partner linkage maps: one is called the paternal map on which markers are derived from the paternal parent and the other is called the maternal map consisting of the maternal markers (Knapp et al 1 1995;Peng et al 2000;Mester et al 2003a).…”

R Package ASMap: Efficient Genetic Linkage Map Construction and Diagnosis

“…Schiex and Gaspin (1997) and Liu (1998) recognized a more efficient approach to marker ordering could be attained from finding solutions to the traveling salesman problem. Computational methods that exploited this knowledge were quickly adopted in construction algorithms including the evolution-strategy algorithm (Mester, Ronin, Hu, Peng, Nevo, and Korol 2003a;Mester, Ronin, Minkov, Nevo, and Korol 2003b) implemented in Multipoint and the RECORD algorithm (Van Os, Stam, Visser, and Van Eck 2005) available as command line software and later implemented in software packages IciMapping (Meng, Li, Zhang, and Wang 2015) and onemap (Margarido and Mollinari 2015). Other construction algorithm variants involving efficient solutions of the traveling salesman problem included the unidirectional growth (UG) algorithm (Tan and Fu 2006), the AntMap algorithm (Iwata and Ninomiya 2006), the MSTmap algorithm (Wu et al 2008) and Lep-MAP (Rastas, Paulin, Hanski, Lehtonen, and Auvinen 2013).…”