OneKGPd

## Examples ### Table of Content - [Metabolic Pathway Redundancy](#metabolic-pathway-redundancy) - [Regulatory Variant Impact on Known Disease Genes](#regulatory-variant-impact-on-known-disease-genes) - [Incomplete Penetrance & Genetic Resilience](#incomplete-penetrance--genetic-resilience) - [Reclassification & AlphaMissense Integration](#reclassification--alphamissense-integration) - [Protein-Protein Interactions](#protein-protein-interactions) - [Macromolecular structural complexes](#macromolecular-structural-complexes) - [Structural Proteogenomics](#structural-proteogenomics) - [Structural Intolerance](#structural-intolerance) - [Oligogenic Burden](#oligogenic-burden) --- #### Metabolic Pathway Redundancy > _Cellular redox homeostasis is maintained by two parallel antioxidant systems: the glutathione system and the thioredoxin system. Complete loss of either GSR or TXNRD1 is incompatible with mammalian development, yet population databases contain individuals carrying variants predicted to impair enzyme function._ > > _Identify clusters of individuals in the KGP cohort who carry multiple 'Moderate' impact VEP variants across both systems. Reasoning through the AlphaMissense structural implications, can you detect a 'balancing act' where a loss of efficiency in Glutathione reductase is consistently paired with high-confidence benign or potentially activating variants in the Thioredoxin system ? Synthesize a model of 'Redox Robustness' based on the co-occurrence of these variants across the cohort._ --- #### Regulatory Variant Impact on Known Disease Genes > _Identify healthy individuals in the KGP dataset carrying ClinVar-validated pathogenic variants in SCN5A, KCNH2, or LDLR. For each carrier, conduct a cis-regulatory scan within a 70kb window to identify variants in high linkage disequilibrium that are statistically associated with Haplotype-Specific Expression (HSE) or Allelic Imbalance. Analyze if these secondary variants disrupt Transcription Factor Binding Sites (TFBS) in proximal promoters, alter uORF-mediated translation kinetics, or modify mRNA stability motifs in the 3'UTR. Evaluate the hypothesis that resilience is achieved through a "Transcriptional Damping" mechanism, where the pathogenic allele is preferentially silenced or the wild-type allele is hyper-activated, ensuring the total pool of functional protein remains above the critical phenotypic threshold._ > > _Building on the SCN5A damping findings, investigate if resilience in 150 heterozygous carriers is also mediated by trans-acting chaperones that stabilize the functional 50% of sodium channels. Task Statement: Identify healthy SCN5A pathogenic carriers from the previous "Transcriptional Damping" cohort and perform a genome-wide scan for Trans-acting Proteostatic Modifiers. Search for enrichment of gain-of-function variants or high-expression eQTLs in cardiac-specific ion channel chaperones and trafficking regulators, specifically SNTA1 (Syntrophin-alpha 1), GPD1L, and RANGRF (MOG1)._ --- #### Incomplete Penetrance & Genetic Resilience > _Identify samples in the KGP dataset that are homozygous for variants classified as 'Pathogenic' in ClinVar for severe autosomal recessive metabolic disorders. For these specific samples, scan their exomes for enrichment of variants in known suppressor genes or alternative metabolic pathways that might compensate for the primary defect. Propose a mechanism of compensation based on pathway analysis._ - . > _Select samples carrying known dominant-negative variants in KRT5 or KRT14 genes (Epidermolysis Bullosa) in the KGP. Search for potential cis- or trans-acting rescue modifiers. Specifically, check if these samples carry variants that promote the upregulation of the homologous KRT6 or KRT16 genes (paralog compensation). Can you detect a statistically significant enrichment of 'paralog-boosting' promoter variants in these resilient carriers ?_ --- #### Reclassification & AlphaMissense Integration > _Retrieve all variants in KGP dataset in the voltage-gated sodium channel gene family (SCN1A, SCN2A, SCN5A) currently classified as 'VUS' in ClinVar. Correlate their 'Likely Pathogenic' AlphaMissense classification with their frequency in this cohort. Synthesize a reasoned argument to reclassify a subset of these as 'Likely Benign' based on the logic that pathogenic predictions by AlphaMissense are incompatible with the observed allele frequency in this healthy population._ - . > _Analyze the distribution of missense variants in the BRCA1 and BRCA2 genes found in the KGP dataset. Map these variants to the 3D protein structures. Identify specific structural domains (e.g. the BRCT domain vs. unstructured loops) that tolerate a high density of AlphaMissense-predicted 'ambiguous' variants in this healthy cohort, effectively creating a 'map of benign tolerance' for future clinical reference._ - . > _Retrieve all variants in the KGP dataset annotated as 'High Impact' by VEP but 'Benign' by AlphaMissense. Analyze the Sequence Ontology context of these variants. Are they predominantly located in alternatively spliced exons that are non-essential for the canonical transcript ? Synthesize a report on which specific gene isoforms are dispensable for health based on this discordance._ --- #### Protein-Protein Interactions > _Analyze samples in the KGP dataset with missense variants located at the 'hinge' or 'head' domains in Cohesin complex genes (SMC1A, SMC3, RAD21). Perform a 'co-evolution' analysis - do samples with a destabilizing mutation in the SMC1A head domain tend to carry a complementary variant in the SMC3 head domain that restores electrostatic compatibility (e.g., a charge swap from Glu->Lys in one and Lys->Glu in the other) ?_ - . > _Analyze the Actin-Myosin motor complex. For variants in ACTA1 that map to the interface with Myosin compare the frequency of these variants against variants that map to the internal structure of the actin monomer. Do the interface variants show a higher constraint (lower frequency) than internal variants, suggesting that disrupting the force-generating interaction is less tolerable than minor changes to the monomer's stability ?_ - . > _Focus on the RAS-RAF interaction in the MAPK signaling pathway. Query KGP dataset for variants in the KRAS effector-binding loop. For samples carrying variants here, check for 'compensatory' variants in the RAS-binding domain (RBD) of BRAF, ARAF, or CRAF. Use structural modeling to infer if these variants represent co-evolutionary 'charge-swaps' that maintain a stable binding energy (ΔG) despite the primary mutation._ --- #### Macromolecular structural complexes > _The human RNA Exosome (Exo-9 core) is a "dead machine" that acts as a scaffold. In lower organisms the ring itself can degrade RNA. In humans, the 9-subunit ring has lost all its catalytic teeth and is purely a structural tunnel that guides RNA into the catalytic subunits (DIS3 or EXOSC10) attached at the bottom. Since RNA is a highly negatively charged polymer, the residues lining this pore are typically positively charged (Lysine, Arginine), but not too "sticky" or RNA will jam. So, to reach the "shredder" at the bottom it must slide through a narrow pore formed by the Exo-9 ring._ > > _The task: analyse all missense variants in the KGP cohort that map to the internal pore-lining residues of the Exo-9 ring. Look for 'charge-swap' variants where a positive residue (K, R) is replaced by a negative one (D, E). If an individual is healthy despite having a 'negative patch' in the tunnel that should repel RNA, do they carry a compensatory variant in the cap subunits (EXOSC1, 2, 3) that widens the entrance? Use a 3D electrostatic surface map to determine if the 'healthy' cohort maintains a specific electrostatic gradient._ - . > _The MCM2-7 Complex (The "DNA Helicase Motor") is a molecular masterpiece. It’s a heterohexameric ring where each subunit is a distinct "gear" in the DNA-unzipping motor. Unlike homomeric rings (where every subunit is the same), this complex is asymmetric. Each interface between subunits is unique, and they don't all burn ATP at the same rate. The MCM2/5 interface is the "gate" that must physically open to allow DNA to enter the ring and then snap shut. This is a high-stress mechanical point._ > > _Identify individuals in the KGP cohort carrying missense variants at the MCM2/5 interface. Specifically, look for 'charge-reversal' variants (e.g., Aspartate to Lysine). In these specific samples, analyze the 'compensatory coupling': do they carry a secondary, reciprocal charge-reversal variant on the opposing subunit interface that restores the electrostatic 'latch' ?_ > > _Identify individuals in the KGP cohort who carry high-pathogenicity variants in the Walker A or Walker B motifs (the ATP-burning heart) of any MCM subunit in MCM2-7 Complex. For these individuals, perform a 'Systemic Flux' analysis: look at their variants in the leading-strand polymerase (POLE) and the sliding clamp (PCNA). Do you detect a signature of 'Coordinated Deceleration' where the motor, the clamp, and the polymerase all carry variants that suggest a slower but highly-accurate replication fork ?_ - . > _Investigate the genes involved in the Desmosome complex (DSG, DSC, PKP, DSP). For samples in the KGP with a heterozygous LoF variant in one key component (e.g., DSP), are they depleted of rare missense variants in the interacting components (e.g., PKP1) compared to the general population ? This would suggest that even a single hit requires the rest of the complex to be "pristine" when one component is already compromised to maintain mechanical integrity._ --- #### Structural Proteogenomics > _Treat the 26S Proteasome as a mechanically redundant 3D machine and map every missense variant from the KGP individuals across all 33 subunits. Perform a spatial analysis to determine if pathogenic variation is statistically partitioned toward the distal 'Lid' (Zone C) rather than the evolutionary constrained 'Core' (Zone A) or 'Gating' (Zone B) interfaces. Identify individuals with a high cumulative burden (3+ 'Likely Pathogenic' variants) to investigate inter-subunit compensation, searching for paired 'weakening' and 'stabilizing' mutations at protein-protein hinges. Finally, define the 'mechanical tolerance' of the proteasome by establishing the maximum cumulative structural disruption observed in a single healthy individual based on AlphaMissense scores and calculated ΔΔG values._ --- #### Structural Intolerance > _Which regions in XXXX gene are most likely disease-critical, with strong purifying selection, based on available variation patterns across functional domains in KGP ? Do statistical evaluation._ - . > _Identify variants in the Trigger Loop of POLR2A. Since this loop must move precisely to add nucleotides, does the healthy population exclusively carry variants that preserve the 'flexibility' of the loop while the 'catalytic' residues remain untouched ?_ - . > _Analyze variants in the CCT/TRiC Chaperonin (The Folding Cage) - a barrel-shaped folding machine that helps proteins like actin and tubulin reach their final shape. It consists of two rings of eight different subunits (CCT1-CCT8). Is there a difference in variant pattern in the KGP cohort in these subunits ? E.g., map variants to the ATP-binding pocket vs. the Substrate-binding pocket across all 8 subunits. Do we see a pattern where 'Healthy' variation is allowed in the substrate pocket (allowing the cage to adapt to new proteins) but strictly forbidden in the ATP pocket (the power source)?_ - . > _Examine the GBA gene (Gaucher disease). Map all missense variants found in the KGP cohort to the Glucocerebrosidase active site (residues E235 and E340 and the surrounding pocket). Calculate the minimum distance of each variant to the catalytic glutamate residues. Is there a defined 'sphere of intolerance' (e.g., < 5 Angstroms) around the active site where zero variants are observed ? At what radial distance does the variant density return to the background rate ?_ - . > _In what cardiac related genes, e.g. ion channels, variants in KGP dataset near catalytic residues or ligand-binding pockets show strong depletion compared to flanking residues (±20 amino acids) ?_ --- #### Oligogenic Burden > _Calculate the 'Ciliary Mutational Load' for every individual in the KGP dataset. Aggregate all rare, non-synonymous variants across the entire Bardet-Biedl Syndrome (BBS) gene panel (BBS1 through BBS21). Is there a clear 'cliff' or maximum mutational burden observed in healthy individuals ? Determine if the healthy cohort contains any 'triallelic' carriers (homozygous at one locus, heterozygous at another) and model why they do not display the BBS phenotype._