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Record W4251164524 · doi:10.21203/rs.2.11349/v2

Analysis of Five Deep-sequenced Trio-genomes of the Peninsular Malaysia Orang Asli and North Borneo Populations

2019· preprint· en· W4251164524 on OpenAlexafffund
Lian Deng, Haiyi Lou, Xiaoxi Zhang, Bhooma Thiruvahindrapuram, Dongsheng Lu, Christian Marshall, Chang Liu, Bo Xie, Wanxing Xu, Lai-Ping Wong, Chee-Wei Yew, Farhang Aghakhanian, Rick Twee‐Hee Ong, Mohammad Zahirul Hoque, Abdul Rahman Thuhairah, Bhak Jong, Maude E. Phipps, Stephen W. Scherer, Yik‐Ying Teo, Vijay Kumar Subbiah, Boon‐Peng Hoh, Shuhua Xu

Bibliographic record

VenueResearch Square · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Key Research and Development Program of ChinaHospital for Sick ChildrenMinistério da Ciência, Tecnologia e InovaçãoScience and Technology Commission of Shanghai MunicipalityNational Science Fund for Distinguished Young ScholarsChinese Academy of SciencesYouth Innovation Promotion AssociationKementerian Sains, Teknologi dan InovasiNational Natural Science Foundation of ChinaNational University of SingaporeNational Research Foundation
KeywordsGeographyGenealogyEthnologyBiologyEvolutionary biologyHistory

Abstract

fetched live from OpenAlex

Abstract Background Recent advances in genomic technologies have facilitated genome-wide investigation of human genetic variations. However, most efforts have focused on the major populations, yet trio genomes of indigenous populations from Southeast Asia have been under-investigated. Results We analyzed the whole-genome deep sequencing data (~30×) of five native trios from Malaysia, and discovered approximately 6.9 million single nucleotide variants (SNVs), 1.2 million small insertions and deletions (indels), and 9,000 copy number variants (CNVs) in the 15 samples. We found 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify autosomal de novo variants and estimated the mutation rates to be 0.81×10-8–1.33×10-8 , 1.0×10-9–2.9×10-9, and ~0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for accurate haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example was a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples. Conclusion Our study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.344
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes2
Has abstractyes

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