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Record W2911720118 · doi:10.1182/blood-2018-99-120229

Comprehensive Integrated Genomic Perturbations Reveal Molecular Mechanisms of Red Blood Cell Trait Associations

2018· article· en· W2911720118 on OpenAlexaff
Yuxuan Wu, Mitchel A. Cole, Abdou Mousas, Jing Zeng, Qiuming Yao, Divya S. Vinjamur, Ryo Kurita, Yukio Nakamura, Luca Pinello, G. Lettre, Daniel E. Bauer

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsBiologyGeneticsGenome-wide association studySingle-nucleotide polymorphismChromatinGeneComputational biologyGenotype

Abstract

fetched live from OpenAlex

Abstract Discovery of molecular mechanisms responsible for trait associations as discovered by genome-wide association studies (GWAS) is hampered by difficulty in identifying causal genetic variants due to linkage disequilibrium. Typical assays of genetic function are low throughput or evaluate sequences in heterologous ectopic settings. Genome editing enables perturbation of trait-associated genetic sequences within relevant genomic, chromatin and cellular context. Here we perform comprehensive analysis of genetic variants associated with red blood cell traits by pooled CRISPR screening. We performed a genome-wide Cas9 gene knockout screen in immortalized erythroid precursors (HUDEP-2 cells) during erythroid maturation to define functional erythroid genes required for cell growth or differentiation. We evaluated 952 loci associated with nine red blood cell traits (Astle et al, Cell 2016) comprising 24,843 SNPs. We linked 7,187 (28.9%) of these SNPs to genes by at least one of four routes: sharing topological associated domain, physical proximity (<20 kb), long-range chromatin interaction (promoter HiC), or eQTL with a functional erythroid gene. We designed ~5 guide RNAs per SNP requiring cleavage position within at least 50 bp and exceeding an off-target score threshold, resulting in 32,710 sgRNAs testing 5,592 SNPs at 481 loci. We utilized four editors: Cas9 nuclease to produce indels, dCas9-VP64 for gene activation, dCas9-KRAB for gene repression, and dCas9 as a DNA targeting control. By pooled lentiviral transduction, erythroid differentiation culture, and guide RNA library deep sequencing, we found reproducible results across biological replicates, with guide count clustered by Cas9 protein type. We performed fine-mapping of association results by Bayesian inference to calculate posterior probability of inclusion (PPI). We found a strong correlation between PPI and CRISPR significance score indicating agreement between CRISPR screening and genetic fine-mapping, despite examples of validated CRISPR signals with low PPI scores. We identified numerous CRISPR-implicated functional SNPs at regulatory elements including promoters and enhancers but also at noncoding sequences lacking chromatin marks. The editing of CRISPR -implicated functional SNPs correlated well with expression of linked genes. Editing of CRISPR-implicated functional SNPs caused altered proliferation and/or differentiation of both HUDEP-2 cells and CD34+ HSPC-derived primary erythroid precursors. We validated a functional SNP at the BCL2L1 enhancer, where both Cas9 disruption and dCas9-KRAB inhibition resulted in decreased expression of BCL2L1,and reduced cell survival during erythropoiesis. We validated several CRISPR-implicated functional SNPs at GFI1B that controlled erythroid cell differentiation, including one at a distal enhancer element and another that impacted exon splicing. These results demonstrate the potential and challenges of comprehensive integrated genomic perturbation to complement genetic, biochemical, and statistical approaches to uncover molecular underpinnings of human blood cell traits. Disclosures No relevant conflicts of interest to declare.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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