Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models

Abstract

Modern SNP genotyping technologies allow to measure the relative abundance of different alleles for a given locus and consequently to estimate their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels in a sequence of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters using the maximum-likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results demonstrate the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations. Author summaryIn this paper, we present a complete multilocus solution based on hidden Markov models to estimate recombination fractions and infer the linkage phase configuration in full-sib mapping populations with even ploidy levels under random chromosome segregation. We also present an efficient pairwise loci analysis to be used in cases were the multilocus analysis becomes compute-intensive. Introduction 1Polyploids are organisms with more than two sets of chromosomes. They are very 2 important in agriculture and play a fundamental role in evolutionary processes, such as 3 differentiation of species [48]. The number of sets of chromosomes in an organism is 4 called ploidy level. These multiple chromosome sets can originate from the combination 5 of genomes from different, but related species, or from duplicated genomes from the 6 same species [4,10]. In the first scenario, they are called allopolyploids; in the second, 7 1/30 autopolyploids. Polyploid organisms are also characterized according to their pattern of 8 inheritance. In general, allopolyploids exhibit diploid-like (or disomic) segregation, since 9 homologous chromosomes tend to form bivalents within each sub-genome. 10Autopolyploids, however, have more than two homologous chromosomes per homology 11 group, forming either random bivalents or multivalents during the meiosis resulting in 12 polysomic segregation [39,49,52]. Since the molecular mechanics of polyploid organisms 13 are quite complex, this dichotomy is often broken, and polyploids can display 14 intermediate modes of inheritance [39,40]. Throughout this paper, the term 15 autopolyploid (or autotetraploid, autohexaploid, etc.) will refer to polyploid organisms 16 that exhibit polysomic segregation. 17Despite all advances in genetic studies in autotetraploids 18 [13,14,20,21,27,30,32,34,61,64,65], there is still a shortage of statistical met...

Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping

“…For each SNP pair, we obtained the recombination fractions and the associated likelihood for each possible linkage phase 10 . We used the most likely linkage phase as the baseline to compute LOD Scores associated with all linkage phases, or LOD ph , (i.e., the most likely configuration has a LOD equal to 0.00).…”

“…Parental allelic variants were phased using the procedure presented in Mollinari and Garcia (2019) 10 . In brief, assume a sequence of n ordered markers M 1 , M 2 , · · · , M n .…”

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

“…An appropriate strategy for unraveling the genetic architecture and genomic organization of brown rust resistance would be the use of linkage maps followed by quantitative trait locus (QTL) identification. However, existing methodologies for the construction of saturated linkage maps with high resolution are not suitable for sugarcane and other autopolyploid species due to their genomic complexity 5,22,23 . Using simplification strategies based on the population expected segregation ratio, such as the selection of a subset of single-dose markers, leads to impaired linkage groups and thus compromises the identification of reliable QTLs 22 .…”

“…However, existing methodologies for the construction of saturated linkage maps with high resolution are not suitable for sugarcane and other autopolyploid species due to their genomic complexity 5,22,23 . Using simplification strategies based on the population expected segregation ratio, such as the selection of a subset of single-dose markers, leads to impaired linkage groups and thus compromises the identification of reliable QTLs 22 . A linkage map depicting QTLs associated with brown rust resistance has been published 5 , but as observed in previous studies 11,24 , adjustments of existing methods resulted in gaps, a poorly saturated map and a large number of unlinked markers, mainly due to the high probability of meiotic behaviors in the cultivars and the aneuploidy of sugarcane 7,22 .…”

“…Using simplification strategies based on the population expected segregation ratio, such as the selection of a subset of single-dose markers, leads to impaired linkage groups and thus compromises the identification of reliable QTLs 22 . A linkage map depicting QTLs associated with brown rust resistance has been published 5 , but as observed in previous studies 11,24 , adjustments of existing methods resulted in gaps, a poorly saturated map and a large number of unlinked markers, mainly due to the high probability of meiotic behaviors in the cultivars and the aneuploidy of sugarcane 7,22 . Different software programs have been developed to build linkage maps for polyploids 22,[25][26][27] ; however, none address sugarcane genomic organization.…”