Inside Genebench-Pro
Quick Answer
GeneBench-Pro showcases 10 diverse case studies in genomics, including somatic oncology and CRISPR target validation, emphasizing the importance of tailored data analysis for effective treatment decisions.
Quick Take
GeneBench-Pro showcases 10 diverse case studies in genomics, including somatic oncology and CRISPR target validation, emphasizing the importance of tailored data analysis for effective treatment decisions. Each study highlights specific methodologies and datasets used to derive insights on clinical utility and genetic dependencies.
Key Points
- Case studies cover areas like oncology, functional genomics, and single-cell genomics.
- Each study includes original prompts, datasets, and supporting materials for analysis.
- Focus on practical applications such as estimating clinical utility and genetic dependencies.
- Methodologies include Mendelian randomization and eQTL modeling for precise insights.
- Results aim to enhance decision-making in personalized medicine and genetic screening.
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~6 min readCase studies
These 10 case studies showcase representative questions from GeneBench-Pro. Each case study includes the original prompt, datasets, and supporting materials. For an overview of the benchmark and key findings, see the announcement blog.
Note: File previews show excerpts from the full datasets.
Case study 1
Somatic oncology: Structural variant-guided tumor therapy benefit-risk decision
Estimate whether a synthetic TXR1-directed inhibitor has positive clinical utility in tumors whose target activation is driven by a structural variant. TXR1, TXR1i, DLR1, and star-allele labels are synthetic benchmark labels.
The target subgroup has to be recovered from long-read, expression, tumor-quality, and pharmacogenomic evidence before benefit and toxicity can be interpreted as a treatment decision.
Files provided to the model
| patient_id | analysis_set | age | sex | site | calendar_period | ecog | tumor_burden | prior_lines | prior_resistance | lineage_class | therapy_class | assessed16 | benefit16 | tox_stop_8wk | time_zero_day |
| MTB0001 | 1 | 73.8 | M | S1 | P2 | 2 | 0.787 | 3 | 1 | A | TXR1i | 0 | 1 | 0 | |
| MTB0002 | 1 | 55.2 | M | S3 | P1 | 1 | 2.637 | 0 | 1 | A | TXR1i | 1 | 0 | 0 | 0 |
| MTB0003 | 1 | 68.8 | F | S4 | P2 | 0 | 0.891 | 2 | 1 | A | TXR1i | 1 | 1 | 1 | 0 |
| MTB0004 | 1 | 82.8 | F | S2 | P2 | 2 | 4.101 | 0 | 0 | B | TXR1i | 1 | 0 | 0 | 0 |
| MTB0005 | 1 | 65.5 | F | S1 | P3 | 1 | 7.0 | 1 | 1 | A | TXR1i | 1 | 0 | 0 | 0 |
Registry covariates, therapy, week-16 assessment, benefit, and early toxicity.
Case study 2
Functional genomics: CRISPR target validation: lncRNA transcript or genomic locus?
Decide whether an apparent lncRNA dependency is transcript-specific or driven by nearby-locus and neighbor-gene effects.
Transcript-directed evidence has to survive controls for local DNA-locus perturbation, neighbor-gene repression, guide swaps, GC toxicity, and plate effects.
Files provided to the model
| guide_id | nominal_target | chr | coord | strand | dist_lnc_tss_bp | dist_neighbor_tss_bp | guide_gc_frac |
| g001 | LINC473 | chr7 | 100014 | + | 14 | 30 | 0.624 |
| g002 | LINC473 | chr7 | 100035 | - | 43 | 67 | 0.584 |
| g003 | LINC473 | chr7 | 100051 | + | 116 | 56 | 0.622 |
| g004 | LINC473 | chr7 | 100066 | - | 59 | 66 | 0.617 |
| g005 | LINC473 | chr7 | 100088 | + | 74 | 77 | 0.715 |
Guide coordinates, targets, distances, and GC features.
Case study 3
Statistical genetics: Prioritizing protein drug targets in a linked genetic locus
Estimate direct disease effects for two nearby proteins using cis multivariable Mendelian randomization (cis-MVMR) while handling assay scale, allele orientation, winner's curse, LD, and residual local pleiotropy.
The two proteins share a correlated locus. The analysis has to move from marginal associations to conditional, LD-aware disease effects on a common protein scale.
Files provided to the model
| snp | pos_bp | effect_allele | other_allele | maf | beta | se | pval |
| rs200000 | 50000000 | A | C | 0.42215 | 0.006438668310706808 | 0.003267330091203412 | 0.04876727714241972 |
| rs200001 | 50010126 | A | C | 0.05709 | 0.011008993337581301 | 0.006955239208750407 | 0.11345916603941006 |
| rs200002 | 50020253 | G | T | 0.09021 | 0.009922014757116319 | 0.005633023027015518 | 0.07817048492026045 |
| rs200003 | 50030379 | G | T | 0.48399 | 0.010569215614164573 | 0.0032291419740237445 | 0.0010638520681901973 |
| rs200004 | 50040506 | A | G | 0.37703 | 0.007036551378238654 | 0.0033297592321269802 | 0.034580976884336506 |
Screening-stage protein association summaries for PROTA.
Case study 4
Clinical genomics / carrier screening: DRX1 carrier-screening residual risk under CNV and pseudogene calibration
Estimate ancestry-specific carrier frequencies, residual risk after a negative screen, partner carrier frequency, and affected-conceptus risk from carrier-screening assay data.
The residual-risk estimate depends on pseudogene-aware carrier calls, founder-haplotype collapse, ancestry-specific assay calibration, and standardization from tested partners back to the full partner roster.
Files provided to the model
| sample_id | collection | ancestry | family_history_tier |
| S_EUR_0001 | screening | EUR | 0 |
| S_EUR_0002 | screening | EUR | 0 |
| S_EUR_0003 | screening | EUR | 0 |
| S_EUR_0004 | screening | EUR | 0 |
| S_EUR_0005 | screening | EUR | 1 |
Screening-roster adults with ancestry and screening context.
Case study 5
Single-cell genomics: Activated-monocyte eQTL after ambient RNA correction
Estimate a genotype effect on activated-monocyte expression after removing ambient RNA and technical contamination from single-cell RNA-seq data.
Ambient RNA affects both target expression and the marker panel used to call activation state, so correction has to occur before the eQTL model.
Files provided to the model
| cell_id | donor | total_umi | HBB | IFI6 | ISG15 | LST1 | CXCL10 |
| D01_C001 | D01 | 1113 | 7 | 3 | 4 | 83 | 5 |
| D01_C002 | D01 | 1103 | 6 | 3 | 3 | 112 | 10 |
| D01_C003 | D01 | 1141 | 9 | 8 | 12 | 63 | 9 |
| D01_C004 | D01 | 1250 | 7 | 60 | 43 | 2 | 17 |
| D01_C005 | D01 | 1045 | 9 | 1 | 2 | 51 | 15 |
Per-cell UMI counts for marker genes, contamination markers, and the target gene.
Case study 6
Structural genetics: Nested structural variant: expression support and clinical association
Estimate whether a nested structural subhaplotype inside an anonymous inversion-like locus has a calibrated clinical association and credible expression support.
A nested copy-dosage signal can be confounded by the broader inversion orientation, so dosage calibration, expression support, and clinical modeling have to remain distinct.
Files provided to the model
| sample_id | case | age | age_band | sex | pc1 | pc2 | pc3 | ancestry_group | clinic_stratum | recruitment_stream |
| Q00012 | 1 | 50.45 | 50_64 | 0 | -1.01514 | -0.21032 | -0.08849 | EUR | tertiary | clinic |
| Q00028 | 0 | 57.39 | 50_64 | 0 | -1.25987 | -0.12498 | 0.2344 | EUR | regional | registry |
| Q00029 | 1 | 68.4 | 65_plus | 0 | 0.91598 | 0.62177 | 0.01891 | AFR | tertiary | clinic |
| Q00030 | 1 | 74.07 | 65_plus | 1 | 0.21125 | -0.59634 | -0.08197 | EAS | community | registry |
| Q00032 | 1 | 82.82 | 65_plus | 0 | -1.12034 | -0.24372 | 0.14665 | EUR | community | clinic |
Clinical and covariate data for the full cohort.
Case study 7
Regulatory genomics: Measuring chromatin loop strength after structural-variant and mapping artifact masking
Quantify a focal case-control Hi-C loop-strength difference after removing low-mappability and structural-variant artifacts from the expected-contact background.
The target loop is defined at 20 kb resolution, but the expected-contact model is distorted unless low-mappability contacts and a case-only SV stripe are masked first.
Files provided to the model
| bin_id | chrom | start | end | gc_content | mappability | re_sites |
| 0 | chr8 | 400000 | 420000 | 0.46199033821572594 | 0.9787574214704273 | 5 |
| 1 | chr8 | 420000 | 440000 | 0.5044124208534677 | 0.8901084943498397 | 5 |
| 2 | chr8 | 440000 | 460000 | 0.43218451584938194 | 0.9056879289326712 | 3 |
| 3 | chr8 | 460000 | 480000 | 0.4733197282681218 | 0.9376529840664789 | 3 |
| 4 | chr8 | 480000 | 500000 | 0.4444956062150748 | 0.8682565517981877 | 4 |
Target-resolution bin annotations.
Case study 8
Statistical genetics: Multi-parent QTL mapping with founder reconstruction
Map a chromosome-1 quantitative-trait locus in an eight-founder recombinant population by reconstructing founder ancestry before testing the phenotype association.
The visible marker data are biallelic, but the biological signal is founder ancestry. A defensible analysis therefore has to reconstruct founder state, check marker orientation, and separate the QTL from a batch-aligned nuisance peak.
Files provided to the model
| marker_id | chr | pos_cM |
| m2_065 | 2 | 59.762431265596575 |
| m2_103 | 2 | 94.52656615104739 |
| m2_107 | 2 | 98.18761427503033 |
| m2_079 | 2 | 72.20130244108847 |
| m1_054 | 1 | 49.907510212292195 |
Marker identifiers, chromosomes, and genetic-map positions.
Case study 9
Population genetics: Parent-specific ancestry and recent admixture timing
Infer parent-specific ancestry proportions and recent admixture timing from phased local-ancestry tracts after repairing reciprocal artifacts and a chromosome-specific label inversion.
Ancestry fractions and pulse times both change if reciprocal tract artifacts, chromosome-local label inversion, or map denominators are handled incorrectly.
Files provided to the model
| chrom | hap | start_morgan | end_morgan | anc | posterior | low_complexity_frac |
| chr1 | h1 | 0.03 | 0.505 | A | 0.985 | 0.08 |
| chr1 | h1 | 0.505 | 0.535 | B | 0.62 | 0.92 |
| chr1 | h1 | 0.535 | 1.478849 | A | 0.985 | 0.08 |
| chr1 | h1 | 1.503727 | 1.852681 | B | 0.985 | 0.08 |
| chr1 | h1 | 1.852681 | 2.422373 | A | 0.985 | 0.08 |
Phased local-ancestry tracts with coordinates, ancestry labels, posterior values, and QC annotations.
Case study 10
Population genetics: Estimating selection from noisy ancient-DNA time series
Infer which of two haploid loci is under stronger positive selection from ancient allele-frequency time series while accounting for allele orientation, directional error, drift, and changing population size.
Noisy ancient trajectories are not directly comparable until both loci are placed on the same derived-allele scale and the provided sample-level sequencing-error values are modeled directly.
Files provided to the model
| generation | alt_reads | total_reads | seq_error | sample_year |
| 6 | 36 | 40 | 0.16 | -4500 |
| 12 | 34 | 45 | 0.16 | -4278 |
| 18 | 41 | 55 | 0.16 | -4056 |
| 24 | 38 | 70 | 0.16 | -3833 |
| 30 | 36 | 90 | 0.16 | -3611 |
Read-count time series for locus A.
— Originally published at openai.com
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