Biology subjects

Garcia, A. A. F.

Publications and source records attributed to Garcia, A. A. F..

7 recordsLinked to original sources

bioRxiv2018

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

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.\n\nAuthor 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.

genetics↗

bioRxiv2018

QTL mapping and identification of corresponding genomic regions for black pod disease resistance to three Phytophthora species in Theobroma cacao L.

The cacao tree (Theobroma cacao L.) is a species of great importance because cacao beans are the raw material used in the production of chocolate. However, the economic success of cacao is largely limited by important diseases such as black pod, which is responsible for losses of up to 30-40% of the global cacao harvest. The discovery of resistance genes could extensively reduce these losses. Therefore, the aims of this study were to construct an integrated multipoint genetic map, align polymorphisms against the available cacao genome, and identify quantitative trait loci (QTLs) associated with resistance to black pod disease in cacao. The genetic map had a total length of 956.41 cM and included 186 simple sequence repeat (SSR) markers distributed among 10 linkage groups. The physical \"in silico\" map covered more than 200 Mb of the cacao genome. Based on the mixed model predicted means of Phytophthora evaluation, a total of 6 QTLs were detected for Phytophthora palmivora (1 QTL), Phytophthora citrophthora (1 QTL), and Phytophthora capsici (4 QTLs). Approximately 1.77% to 3.29% of the phenotypic variation could be explained by the mapped QTLs. Several SSR marker-flanking regions containing mapped QTLs were located in proximity to disease regions. The greatest number of resistance genes was detected in linkage group 6, which provides strong evidence for a QTL. This joint analysis involving multipoint and mixed-model approaches may provide a potentially promising technique for detecting genes resistant to black pod and could be very useful for future studies in cacao breeding.

plant biology↗

bioRxiv2018

Genetic variation in a complex polyploid: unveiling the dynamic allelic features of sugarcane

BackgroundSugarcane (Saccharum spp.) is highly polyploid and aneuploid. Modern cultivars are derived from hybridization between S. officinarum and S. spontaneum. This combination results in a genome exhibiting variable ploidy among different loci, a huge genome size (approximately 10 Gb) and a high content of repetitive regions. Gene expression mechanisms are poorly understood in these cultivars. An approach using genomic, transcriptomic and genetic mapping can improve our knowledge of the behavior of genetics in sugarcane.\n\nResultsThe hypothetical HP600 and centromere protein C (CENP-C) genes from sugarcane were used to elucidate the allelic expression and genomic and genetic behavior of this complex polyploid. The genomically side-by-side genes HP600 and CENP-C were found in two different homeologous chromosome groups with ploidies of eight and ten. The first region (Region01) was a Sorghum bicolor ortholog with all haplotypes of HP600 and CENP- C expressed, but HP600 exhibited an unbalanced haplotype expression. The second region (Region02) was a scrambled sugarcane sequence formed from different noncollinear genes containing duplications of HP600 and CENP-C (paralogs). This duplication occurred before the Saccharum genus formation and after the separation of sorghum and sugarcane, resulting in a nonexpressed HP600 pseudogene and a recombined fusion version of CENP-C and orthologous gene Sobic.003G299500 with at least two chimerical gene haplotypes expressed. The genetic map construction supported the difficulty of mapping markers located in duplicated regions of complex polyploid genomes.\n\nConclusionAll these findings describe a low synteny region in sugarcane, formed by events occurring in all members of the Saccharum genus. Additionally, evidence of duplicated and truncate gene expression and the behavior of genetic markers in a duplicated region was found. Thus, we describe the complexity involved in sugarcane genetics and genomics and allelic dynamics, which can be useful for understanding the complex polyploid genome.

plant biology↗

bioRxiv2018

Genetic mapping with allele dosage information in tetraploid Urochloa decumbens (Stapf) R.D. Webster reveals insights into spittlebug (Notozulia entreriana Berg) resistance

Urochloa decumbens (Stapf) R.D. Webster is one of the most important African forage grasses in Brazilian beef production. Currently available genetic-genomic resources for this species are restricted mainly due to polyploidy and apomixis. Therefore, crucial genomic-molecular studies such as the construction of genetic maps and the mapping of quantitative trait loci (QTLs) are very challenging and consequently affect the advancement of molecular breeding. The objectives of this work were to (i) construct an integrated U. decumbens genetic map for a full-sibling progeny using GBS-based markers with allele dosage information, (ii) detect QTLs for spittlebug (Notozulia entreriana) resistance, and (iii) seek putative candidate genes involved in resistance/defense against pathogens. We used the Setaria viridis genome as reference to align GBS reads and selected 4,240 high-quality SNPs markers with allele dosage information. Of these markers, 1,000 were distributed throughout nine homologous groups with a cumulative map length of 1,335.09 cM and an average marker density of 1.33 cM. We detected QTLs for resistance to spittlebug, an important pasture insect pest, that explained between 4.66% and 6.24% of the phenotypic variation. These QTLs are in regions containing putative candidate genes related to resistance/defense against pathogens. Because this is the first genetic map with SNP autotetraploid dosage data and QTL detection in U. decumbens, it will be useful for future evolutionary studies, genome assembly, and other QTL analyses in Urochloa spp. Moreover, the results might facilitate the isolation of spittlebug-related candidate genes and help clarify the mechanism of spittlebug resistance. These approaches will improve selection efficiency and accuracy in U. decumbens molecular breeding and shorten the breeding cycle.

plant biology↗

bioRxiv2018

Linkage Disequilibrium and Population Structure in Wild and Cultivated Populations of Rubber Tree (Hevea brasiliensis)

Among rubber tree species, which belong to the Hevea genus of the Euphorbiaceae family, Hevea brasiliensis (Willd. ex Adr.de Juss.) Muell. Arg. is the main commercial source of natural rubber production worldwide. Knowledge of the population structure and linkage disequilibrium (LD) of this species is essential for the efficient organization and exploitation of genetic resources. Here, we obtained single-nucleotide polymorphisms (SNPs) using a genotyping-by-sequencing (GBS) approach and then employed the SNPs for the following objectives: (i) to identify the positions of SNPs on a genetic map of a segregating mapping population, (ii) to evaluate the population structure of a germplasm collection, and (iii) to detect patterns of LD decay among chromosomes for future genetic association studies in rubber tree. A total of 626 genotypes, including both germplasm accessions (368) and individuals from a genetic mapping population (254), were genotyped. A total of 77,660 and 21,283 SNPs were detected by GBS in the germplasm and mapping populations, respectively. The mapping population, which was previously mapped, was constructed with 1,062 markers, among which only 576 SNPs came from GBS, reducing the average interval between two adjacent markers to 4.4 cM. SNPs from GBS genotyping were used for the analysis of genetic structure and LD estimation in the germplasm accessions. Two groups, which largely corresponded to the cultivated and wild populations, were detected using STRUCTURE and via principal coordinate analysis (PCoA). LD analysis, also using the mapped SNPs, revealed that non-random associations varied along chromosomes, with regions of high LD interspersed with regions of low LD. Considering the length of the genetic map (4,693 cM) and the mean LD (0.49 for cultivated and 0.02 for wild populations), a large number of evenly spaced SNPs would be needed to perform genome-wide association studies in rubber tree, and the wilder the genotypes used, the more difficult the mapping saturation.

plant biology↗

bioRxiv2018

High-resolution genetic map and QTL analysis of growth-related traits of Hevea brasiliensis cultivated under suboptimal temperature and humidity conditions

Rubber tree (Hevea brasiliensis) cultivation is the main source of natural rubber worldwide and has been extended to areas with suboptimal climates and lengthy drought periods; this transition affects growth and latex production. High-density genetic maps with reliable markers support precise mapping of quantitative trait loci (QTL), which can help reveal the complex genome of the species, provide tools to enhance molecular breeding, and shorten the breeding cycle. In this study, QTL mapping of the stem diameter, tree height, and number of whorls was performed for a full-sibling population derived from a GT1 and RRIM701 cross. A total of 225 simple sequence repeats (SSRs) and 186 single-nucleotide polymorphism (SNP) markers were used to construct a base map with 18 linkage groups and to anchor 671 SNPs from genotyping by sequencing (GBS) to produce a very dense linkage map with small intervals between loci. The final map was composed of 1,079 markers, spanned 3,779.7 cM with an average marker density of 3.5 cM, and showed collinearity between markers from previous studies. Significant variation in phenotypic characteristics was found over a 59-month evaluation period with a total of 38 QTLs being identified through a composite interval mapping method. Linkage group 4 showed the greatest number of QTLs (7), with phenotypic explained values varying from 7.67% to 14.07%. Additionally, we estimated segregation patterns, dominance, and additive effects for each QTL. A total of 53 significant effects for stem diameter were observed, and these effects were mostly related to additivity in the GT1 clone. Associating accurate genome assemblies and genetic maps represents a promising strategy for identifying the genetic basis of phenotypic traits in rubber trees. Then, further research can benefit from the QTLs identified herein, providing a better understanding of the key determinant genes associated with growth of Hevea brasiliensis under limiting water conditions.

plant biology↗

bioRxiv2018

Harnessing Empirical Bayes and Mendelian Segregation for Genotyping Autopolyploids from Messy Sequencing Data

Detecting and quantifying the differences in individual genomes (i.e. genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. We provide two main contributions in this paper: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. We assess the accuracy of our method in simulations and apply it to a dataset of hexaploid sweet potatoes (Ipomoea batatas). An R package implementing our method is available at https://github.com/dcgerard/updog.

bioinformatics↗