A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

Recently much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks - a type of network slightly more general than a phylogenetic tree - from triplets. Our algorithm has been made publicly available as the program LEV1ATHAN. It combines ideas from several known theoretical algorithms for phylogenetic tree and network reconstruction with two novel subroutines. Namely, an exponential-time exact and a greedy algorithm both of which are of independent theoretical interest. Most importantly, LEV1ATHAN runs in polynomial time and always constructs a level-1 network. If the data is consistent with a phylogenetic tree, then the algorithm constructs such a tree. Moreover, if the input triplet set is dense and, in addition, is fully consistent with some level-1 network, it will find such a network. The potential of LEV1ATHAN is explored by means of an extensive simulation study and a biological data set. One of our conclusions is that LEV1ATHAN is able to construct networks consistent with a high percentage of input triplets, even when these input triplets are affected by a low to moderate level of noise

Genome-Wide Association Study for Powdery Mildew and Rusts Adult Plant Resistance in European Spring Barley from Polish Gene Bank

Rusts and powdery mildew are diseases that have a major effect on yield loss in barley. Adult Plant Resistance (APR) is a post-seedling resistance mechanism and its expression is influenced by many factors, including host susceptibility and weather conditions, as well as the timing and severity of disease outbreaks. There are two mechanisms associated with APR: non-hypersensitive and minor gene APR. In this study, 431 European barley accessions were evaluated phenotypically over 2 years (2018–2019) under field conditions, scoring APR to powdery mildew (PM), barley brown rust (BBR), and stem rust (SR), and genotypically using DArTseq. Accessions were grouped into sub-collections by cultivation period (group A—cultivated prior 1985, B—cultivated after 1985, and C—Polish landraces) and by European country of origin or European region. GWAS was conducted for PM, BBR, and SR, and scored at the heading (HA) and milky-waxy (MW) seed stages in 2019 and maximum scores across all replicates were obtained 2018–2019. Disease severity was sufficient to differentiate the collection according to cultivation time and country of origin and to determine SNPs. Overall, the GWAS analysis identified 73 marker–trait associations (MTAs) with these traits. For PM resistance, we identified five MTAs at both the HA stage and when considering the maximal disease score across both growth stages and both years. One marker (3432490-28-T/C) was shared between these two traits; it is located on chromosome 4H. For BBR resistance, six MTAs at HA and one MTA at the MW stage in 2019 and seven MTAs, when considering the maximal disease score across both growth stages and both years, were identified. Of the 48 markers identified as being associated with SR resistance, 12 were on chromosome 7H, 1 was in the telomeric region of the short arm, and 7 were in the telomeric region of the long arm. Rpg1 has previously been mapped to 7HS. The results of this study will be used to create a Polish Gene Bank platform for precise breeding programs. The resistant genotypes and MTA markers will serve as a valuable resource for breeding for PM, BBR, and SR resistance in barley

Genome-Wide Association Study for Powdery Mildew and Rusts Adult Plant Resistance in European Spring Barley from Polish Gene Bank

Rusts and powdery mildew are diseases that have a major effect on yield loss in barley. Adult Plant Resistance (APR) is a post-seedling resistance mechanism and its expression is influenced by many factors, including host susceptibility and weather conditions, as well as the timing and severity of disease outbreaks. There are two mechanisms associated with APR: non-hypersensitive and minor gene APR. In this study, 431 European barley accessions were evaluated phenotypically over 2 years (2018–2019) under field conditions, scoring APR to powdery mildew (PM), barley brown rust (BBR), and stem rust (SR), and genotypically using DArTseq. Accessions were grouped into sub-collections by cultivation period (group A—cultivated prior 1985, B—cultivated after 1985, and C—Polish landraces) and by European country of origin or European region. GWAS was conducted for PM, BBR, and SR, and scored at the heading (HA) and milky-waxy (MW) seed stages in 2019 and maximum scores across all replicates were obtained 2018–2019. Disease severity was sufficient to differentiate the collection according to cultivation time and country of origin and to determine SNPs. Overall, the GWAS analysis identified 73 marker–trait associations (MTAs) with these traits. For PM resistance, we identified five MTAs at both the HA stage and when considering the maximal disease score across both growth stages and both years. One marker (3432490-28-T/C) was shared between these two traits; it is located on chromosome 4H. For BBR resistance, six MTAs at HA and one MTA at the MW stage in 2019 and seven MTAs, when considering the maximal disease score across both growth stages and both years, were identified. Of the 48 markers identified as being associated with SR resistance, 12 were on chromosome 7H, 1 was in the telomeric region of the short arm, and 7 were in the telomeric region of the long arm. Rpg1 has previously been mapped to 7HS. The results of this study will be used to create a Polish Gene Bank platform for precise breeding programs. The resistant genotypes and MTA markers will serve as a valuable resource for breeding for PM, BBR, and SR resistance in barley

Computing a consensus of multilabeled trees

In this paper we consider two challenging problems that arise in the context of computing a consensus of a collection of multilabeled trees, namely (1) selecting a compatible collection of clusters on a multiset from an ordered list of such clusters and (2) optimally refining high degree vertices in a multilabeled tree. Forming such a consensus is part of an approach to reconstruct the evolutionary history of a set of species for which events such as genome duplication and hybridization have occurred in the past. We present exact algorithms for solving (1) and (2) that have an exponential run- time in the worst case. To give some impression of their performance in practice, we apply them to simulated input and to a real biological data set highlighting the impact of several structural properties of the input on the performanc

Kukri WGS vs IWGSC RefSeq v1.0 Genome Assembly

WGS reads of Kukri (http://dx.doi.org/10.1111/j.1467-7652.2012.00717.x) were aligned against the IWGSC <i>Triticum aestivum</i> Chinese Spring RefSeq v1.0 genome assembly using Minimap2. These BAM files (.bam) and index files (.bam.bai) are provided together with a summary of read alignment coverage bigWig files (.bam.bw). SNP variants were called from these BAM files to generate VCF files (.bam.vcf.gz) and index files (.bam.vcf.gz.tbi) and are provided together with a summary of SNP density (SNPs per 10 kbp) bigWig files (.bam.vcf.w10000_s10000.bw). VCF files contain the following filter values and corresponding meaning: PASS = high quality (Q>=30) homozygous; Het = high quality (Q>=30) heterozygous; LowQualHom = low quality (Q<30) homozygous; LowQualHet = low quality (Q<30) heterozygous. Files are provided separately for each chromosome part

Drysdale WGS vs IWGSC RefSeq v1.0 Genome Assembly

WGS reads of Drysdale (http://dx.doi.org/10.1111/j.1467-7652.2012.00717.x) were aligned against the IWGSC <i>Triticum aestivum</i> Chinese Spring RefSeq v1.0 genome assembly using Minimap2. These BAM files (.bam) and index files (.bam.bai) are provided together with a summary of read alignment coverage bigWig files (.bam.bw). SNP variants were called from these BAM files to generate VCF files (.bam.vcf.gz) and index files (.bam.vcf.gz.tbi) and are provided together with a summary of SNP density (SNPs per 10 kbp) bigWig files (.bam.vcf.w10000_s10000.bw). VCF files contain the following filter values and corresponding meaning: PASS = high quality (Q>=30) homozygous; Het = high quality (Q>=30) heterozygous; LowQualHom = low quality (Q<30) homozygous; LowQualHet = low quality (Q<30) heterozygous. Files are provided separately for each chromosome part

Chara,AUS WGS vs IWGSC RefSeq v1.0 Genome Assembly

WGS reads of Chara,AUS (http://dx.doi.org/10.1111/j.1467-7652.2012.00717.x) were aligned against the IWGSC <i>Triticum aestivum</i> Chinese Spring RefSeq v1.0 genome assembly using Minimap2. These BAM files (.bam) and index files (.bam.bai) are provided together with a summary of read alignment coverage bigWig files (.bam.bw). SNP variants were called from these BAM files to generate VCF files (.bam.vcf.gz) and index files (.bam.vcf.gz.tbi) and are provided together with a summary of SNP density (SNPs per 10 kbp) bigWig files (.bam.vcf.w10000_s10000.bw). VCF files contain the following filter values and corresponding meaning: PASS = high quality (Q>=30) homozygous; Het = high quality (Q>=30) heterozygous; LowQualHom = low quality (Q<30) homozygous; LowQualHet = low quality (Q<30) heterozygous. Files are provided separately for each chromosome part