Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics
Bibliographic record
Abstract
Abstract 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 Summary There 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".