Development of High-Density SNP Markers and Their Application in Evaluating Genetic Diversity and Population Structure in Elaeis guineensis

Xia, Wei; Luo, Tingting; Zhang, Wei; Mason, Annaliese S.; Huang, Dong; Huang, Xiaolong; Tang, Wenqi; Dou, Yajing; Zhang, Chunyu; Xiao, Yong

doi:10.3389/fpls.2019.00130

Cited by 56 publications

(46 citation statements)

References 39 publications

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“…Previous studies that have examined the utility of the cross-species application of commercially available genotyping array technology have identified trends of decreasing ability to genotype loci as divergence time from the model organism increases as well [8,9]. This study is unique as it tests the array technology on a wide range of species spanning multiple millions of years divergence from the reference house mouse.…”

Section: Discussionmentioning

confidence: 99%

“…Single nucleotide polymorphisms (SNPs) are single base pair genome variations found in at least one percent of individuals in a population, and are an informative type of genomic diversity that is captured by genotyping arrays [6,7]. SNPs are found in abundance throughout the genome, and this variation can be used as a metric of genomic diversity when comparing different individuals in a population, or different species of interest [8].…”

Section: Introductionmentioning

confidence: 99%

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Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics

Hill

Kelly

Milojevic

et al. 2020

Preprint

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In the study of genetic diversity in non-model species there is a notable lack of the low-cost, high resolution tools that are readily available for model organisms. Genotyping microarray technology for model organisms is well-developed, affordable, and potentially adaptable for cross-species hybridization. The Mouse Diversity Genotyping Array (MDGA), a single nucleotide polymorphism (SNP) genotyping tool designed for Mus musculus, was tested as a tool to survey genomic diversity of wild species for inter-order, inter-genus, and intra-genus comparisons. Application of the MDGA cross-species provides genetic distance information that reflects known taxonomic relationships reported previously between non-model species, but there is an underestimation of genetic diversity for non-Mus samples, indicated by a plateau in loci genotyped beginning 10-15 millions of years divergence from the house mouse. The number and types of samples included in datasets genotyped together must be considered in cross-species hybridization studies. The number of loci with heterozygous genotypes mapped to published genome sequences indicates potential for cross-species MDGA utility. A case study of seven deer mice yielded 159,797 loci (32% of loci queried by the MDGA) that were genotyped in these rodents. For one species, Peromyscus maniculatus, 6,075 potential polymorphic loci were identified. Cross-species utility of the MDGA provides needed genetic information for non-model species that are lacking genomic resources. Genotyping arrays are widely available, developed tools that are capable of capturing large amounts of genetic information in a single application, and represent a unique opportunity to identify genomic variation in closely related species that currently have a paucity of genomic information available. A candidate list of MDGA loci that can be utilized in cross-species hybridization studies was identified and may prove to be informative for rodent species that are known as environmental sentinels. Future studies may evaluate the utility of candidate SNP loci in populations of non-model rodents.Author SummaryThere is a need for a tool that can assay DNA sequence differences in species for which there is little or no DNA information available. One method of analyzing differences in DNA sequences in species with well-understood genomes is through a genotyping microarray, which has demonstrated utility cross-species. The Mouse Diversity Genotyping Array (MDGA) is a tool designed to examine known differences across the genome of the house mouse, Mus musculus. Given that related organisms share genetic similarity, the MDGA was tested for utility in identifying genome variation in other wild mice and rodents. Variation identified from distantly related species that were not of the same genus as the house mouse was an underestimate of the true amount of variation present in the genomes of wild species. Utility of the MDGA for wild species is best suited to mice from the same genus as the house mouse, and candidate variation identified can be tested in rodent populations in future studies. Identifying changes in genetic variation within populations of wild rodents can help researchers understand the links between specific genome changes and the ability to adapt to pressures in the environment, as well as better understand the evolution of rodents.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics

Hill

Kelly

Milojevic

et al. 2020

Preprint

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“…They can reach much higher density than all other types of markers in genomes. Next-generation sequencing can generate large amounts of sequence data, enabling the detection of thousands of SNP [16].…”

Section: Molecular Markers and Population Structure Studiesmentioning

confidence: 99%

Landscape Genetics: From Classic Molecular Markers to Genomics

Konzen¹,

Zucchi²

2021

Methods in Molecular Medicine

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Landscape genetics combines population genetics and landscape ecology to understand processes that shape the distribution and organization of human, animal, or plant populations. This field of genetics emerged from the availability of several studies with classical molecular markers, such as isozymes, RAPD, AFLP, and microsatellites. Population genetic studies enabled the detection of population structure with those markers, but a more comprehensive analysis of natural populations was only possible with the development of statistical methods that combined both molecular data and environmental variables. Ultimately, the rapid development of sequencing technologies allowed studies at the genomic level, augmenting the resolution of association with environment factors. This chapter outlines basic concepts in landscape genetics, the main statistical methods used so far, and the perspectives of this field of knowledge into strategies for conservation of natural populations of plant and animal species. Moreover, we briefly describe the application of the field to understand historical human migration processes as well as how some diseases are spread throughout the world.

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“…Based on 1,261,501 reliable SNPs markers (minor allele frequency >0.05 and more than 80% oil palm individuals had sequences information for each SNP marker) derived from SLAF sequencing in a diversity panel of 200 oil palm individuals in our previous research (Xia et al, 2019b), SNPs within lncRNA regions were screened by a Perl script. The contents of lauric acid (12:0), myristic acid (C14:0) , palmitic acid (16:0), tripalmitelaidin acid (16:1), Hexadecadienoic acid (16:2), stearic acid (18:0), oleic acid (18:1), linoleic aicd (18:2), and oil for 160 individual out of 200 oil palm used in this study were the same set of data from our previous research (Xia et al, 2019a).…”

Section: Single Nucleotide Polymorphisms (Snps) Detection For Lncrnasmentioning

confidence: 99%

Genome-wide discovery and characterization of long noncoding RNAs in African oil palm (Elaeis guineensisJacq.)

Xia

Dou

et al. 2020

PeerJ

Self Cite

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Long noncoding RNAs (lncRNAs) are an important class of genes and play important roles in a range of biological processes. However, few reports have described the identification of lncRNAs in oil palm. In this study, we applied strand specific RNA-seq with rRNA removal to identify 1,363 lncRNAs from the equally mixed tissues of oil palm spear leaf and six different developmental stages of mesocarp (8–24 weeks). Based on strand specific RNA-seq data and 18 released oil palm transcriptomes, we systematically characterized the expression patterns of lncRNA loci and their target genes. A total of 875 uniq target genes for natural antisense lncRNAs (NAT-lncRNA, 712), long intergenic noncoding RNAs (lincRNAs, 92), intronic-lncRNAs (33), and sense-lncRNAs (52) were predicted. A majority of lncRNA loci (77.8%–89.6%) had low expression in 18 transcriptomes, while only 89 lncRNA loci had medium to high expression in at least one transcriptome. Coexpression analysis between lncRNAs and their target genes indicated that 6% of lncRNAs had expression patterns positively correlated with those of target genes. Based on single nucleotide polymorphism (SNP) markers derived from our previous research, 6,882 SNPs were detected for lncRNAs and 28 SNPs belonging to 21 lncRNAs were associated with the variation of fatty acid contents. Moreover, seven lncRNAs showed expression patterns positively correlated expression pattern with those of genes in de novo fatty acid synthesis pathways. Our study identified a collection of lncRNAs for oil palm and provided clues for further research into lncRNAs that may regulate mesocarp development and lipid metabolism.

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Development of High-Density SNP Markers and Their Application in Evaluating Genetic Diversity and Population Structure in Elaeis guineensis

Cited by 56 publications

References 39 publications

Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics

Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics

Landscape Genetics: From Classic Molecular Markers to Genomics

Genome-wide discovery and characterization of long noncoding RNAs in African oil palm (Elaeis guineensisJacq.)

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