Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Population genetics analysis — allele frequencies (gnomAD, 1000 Genomes), Hardy-Weinberg equilibrium testing, Fst between populations, GWAS associations, evolutionary constraint scores. Use for cross-population variant comparison, ancestry-aware allele frequency lookups, and population-level evolutionary analysis.
disable-model-invocation
true
Population Genetics Analysis
MC Strategy: Population genetics MC questions often test whether you know a specific theorem or result. COMPUTE the answer first (use popgen_calculator.py or write Python), then match to options. Don't try to reason about which option "sounds right."
Analyze population-level genetic variation, allele frequencies, GWAS associations, clinical significance, and evolutionary constraints using ToolUniverse tools.
When to Use
Activate this skill when the user asks about:
Allele frequencies across populations (gnomAD, 1000 Genomes)
Selection, drift, linkage disequilibrium, or population structure
Variant annotation and functional consequences
LOOK UP, DON'T GUESS
Query gnomAD/1000Genomes/GWAS Catalog FIRST for allele frequencies and associations. Preferred: use the PopGen_hwe_test, PopGen_fst, PopGen_inbreeding, and PopGen_haplotype_count tools for HWE, Fst, inbreeding, and haplotype calculations. Fallback: run popgen_calculator.py directly. For theoretical problems (delta-q, drift, LD decay), apply the formulas in the Theoretical Reasoning section below.
Tool Quick Reference
Tool
Key Parameters
Notes
gnomad_search_variants
query (REQUIRED)
Resolve rsID to variant_id format "CHR-POS-REF-ALT"
gnomad_get_variant
variant_id (REQUIRED), dataset
Population frequencies. Default dataset: gnomad_r3; use gnomad_r4 for latest
Theoretical Reasoning (CRITICAL for computation problems)
These formulas are needed for quantitative population genetics problems. Work through step by step, showing intermediate values.
Allele Frequency Change Under Selection (delta-q)
For a recessive deleterious allele (fitness: AA=1, Aa=1, aa=1-s):
delta_q = -s * q^2 * p / (1 - s * q^2)
where p = freq(A), q = freq(a), s = selection coefficient.
For dominant deleterious (AA=1, Aa=1-s, aa=1-s):
delta_q = -s * q * p / (1 - s * q * (2 - q))
For heterozygote advantage (AA=1-s1, Aa=1, aa=1-s2):
equilibrium: q_hat = s1 / (s1 + s2)
Example: plug in s1 and s2 from the question; q_hat = s1/(s1+s2).
Selection against recessives is slow at low q because most a alleles hide in heterozygotes. Time to reduce q from q0 to qt: t ~ (1/qt - 1/q0) / s generations.
Genetic Drift in Small Populations
Variance in allele frequency per generation: Var(delta_p) = pq / (2Ne)
Probability of fixation of a new neutral mutation: 1/(2*Ne)
Time to fixation (given it fixes): ~4*Ne generations for neutral alleles
V_G includes ALL genetic variance: additive + dominance + epistasis. Trap: "broad-sense" is not just additive.
Under HWE with two alleles (p, q): genotype frequencies are p^2, 2pq, q^2
Phenotype frequency from genotype: sum(genotype_freq * penetrance) for each genotype class
For quantitative traits: V_P = V_G + V_E (no covariance assumed)
With dominance: assign genotypic values (e.g., AA=a, Aa=d, aa=-a), compute mean, then V_G from freq-weighted squared deviations
PGS vs SNP-h² trap: PGS R² is NOT necessarily ≤ h²_SNP. With large GWAS, PGS can exceed SNP-h² by tagging rare causal variants through LD with common SNPs. The word "necessarily" makes this claim False. h²_SNP is estimated from common variants; PGS can capture additional variance.
Path Analysis (Causal Diagrams)
Trace ALL paths from cause to effect through the diagram (direct + indirect)
Each path's contribution = product of path coefficients along that path
Total effect (correlation) = sum of contributions from all paths
Indirect effects can mask (suppression) or inflate (confounding) the direct effect
Unanalyzed correlations (double-headed arrows) count as valid path segments
Never ignore indirect paths — the total is rarely just the direct arrow
Note any epistasis, suppressor, or modifier interactions between genes
Step 2: Write parental genotypes explicitly
Use standard notation (e.g., Aa Bb for autosomal; X^w X^+ for X-linked)
For X-linked genes, males are hemizygous (X^w Y), not homozygous
If parental genotypes are not given, deduce them from phenotypes and pedigree context
Step 3: Draw Punnett square(s) for each gene
For multi-gene crosses, handle each gene independently (if unlinked) then combine
For linked genes, use recombination frequency to adjust gamete ratios
For X-linked genes, remember that fathers pass X to all daughters and Y to all sons
Step 4: Calculate expected phenotypic ratios
Multiply independent gene ratios (e.g., 3:1 x 3:1 = 9:3:3:1)
For X-linked: calculate male and female ratios separately, then combine or report separately as required
Step 5: Verify ratios sum to 1.0
Convert all ratios to fractions and confirm they sum to 1
If they don't sum to 1, there is an error in the Punnett square or gamete calculation
Step 6: Apply phenotype modification rules AFTER computing genotypic ratios
For epistasis: first compute the full genotypic ratios (e.g., 9:3:3:1), then collapse genotype classes that produce the same phenotype
For suppressor genes: a suppressor homozygote (su/su) restores wild-type in an otherwise mutant background. Apply suppression AFTER determining which individuals carry the mutant allele
Example: 9 A_B_ : 3 A_bb : 3 aaB_ : 1 aabb with recessive epistasis (aa masks B) becomes 9:3:4
E. coli Hfr Mapping Framework
For bacterial conjugation and Hfr mapping problems:
Core Principles
In Hfr x F- crosses, the Hfr chromosome is transferred linearly starting from the origin of transfer (oriT)
Gene transfer order = chromosomal order from the origin
Early markers (entering first) are closest to the origin of transfer
Late markers (entering last) are farthest from the origin
Interrupted Mating Experiments
Genes that appear in recombinants at earlier time points are closer to oriT
The time of entry gives the order and approximate distance between genes
Recombinants require integration by homologous recombination (double crossover)
Recombination Frequency Between Markers
KEY TRAP: Highest recombination frequency occurs between markers that are FARTHEST APART on the transferred segment
This is because more time elapses between entry of distant markers, providing more opportunity for recombination events between them
Conversely, markers that enter close together in time show LOW recombination between them
Do NOT confuse "highest recombination frequency" with "first markers to enter" -- these are opposite concepts
Ordering Markers from Hfr Data
Use time-of-entry data to establish gene order relative to oriT
Use recombination frequency data between pairs of selected markers to confirm/refine order
Multiple Hfr strains with different origins can be used to build a circular map
MCQ Elimination Strategy for Genetics
General MCQ Protocol
ALWAYS evaluate ALL options before choosing an answer
Never select the first option that seems correct -- there may be a better or more precise answer
Read the question stem carefully for qualifiers: "MOST likely", "LEAST likely", "NOT true", "ALWAYS", "NEVER"
"Which is NOT true" Questions
Evaluate EACH statement independently as True or False
Mark each option with T or F before selecting
The answer is the statement marked F
Double-check: verify the "false" statement is genuinely false, not just misleadingly worded
"Which mechanism" Questions
Test each proposed mechanism against ALL observations given in the question
A correct mechanism must explain every observation, not just some
Eliminate mechanisms that contradict even one observation
Specific Traps to Watch For
Subfunctionalization vs neofunctionalization: Subfunctionalization = partitioning of EXISTING ancestral functions between duplicates (both copies needed to perform original function). Neofunctionalization = one copy acquires a genuinely NEW function not present in the ancestor
Copy-neutral LOH: Caused by mitotic recombination (segmental, affects part of a chromosome), NOT uniparental disomy (UPD, which is whole-chromosome). The question may try to conflate these
Penetrance vs expressivity: Penetrance = fraction of individuals with genotype who show ANY phenotype. Expressivity = degree/severity of phenotype among those who show it. These are distinct concepts
Complementation vs recombination: Complementation = two mutations in DIFFERENT genes restore wild-type in trans. Recombination = exchange between two mutations in the SAME or different genes. Complementation is tested in F1 (heterozygote); recombination is tested in progeny
Common Genetics Reasoning Traps
These are specific patterns that have caused reasoning failures in hard genetics questions. Review before answering genetics MCQs.
Suppressor Genetics
A suppressor mutation, when homozygous, restores wild-type phenotype in an otherwise mutant background
In F2 crosses involving both the original mutation and an autosomal recessive suppressor:
Treat as a dihybrid cross — the primary mutation and the suppressor segregate independently
Only 1/4 of F2 are homozygous for the suppressor
The suppressor only acts in individuals that are also homozygous for the primary mutation
Use a Punnett square to enumerate all genotypic classes, then apply the suppression rule to determine phenotypes
Non-disjunction (Bridges' Experiments)
Bridges used non-disjunction to prove the chromosome theory of inheritance
X0 males arise from female meiosis non-disjunction events
Meiosis I non-disjunction: both X chromosomes go to one pole -> XX egg + O egg (nullo-X)
Meiosis II non-disjunction: sister chromatids fail to separate -> XX egg from one secondary oocyte
The classic Bridges result: exceptional white-eyed females (X^w X^w) and red-eyed males (from nullo-X eggs + Y sperm = X0, but these are typically sterile)
Key distinction: know which type of non-disjunction (MI vs MII) produces which specific gamete types
GWAS LD Blocks
SNPs WITHIN the same LD block are correlated and can inflate false positive associations (one causal SNP drags along non-causal tag SNPs)
SNPs ACROSS different LD blocks are largely independent and do NOT create misleading cross-locus associations
LD block structure varies by population (shorter in African populations due to larger historical Ne)
Fine-mapping within an LD block is needed to distinguish the causal variant from hitchhiking tag SNPs
Gene Retention After Whole-Genome Duplication
Neofunctionalization: One copy acquires a NEW function -> most commonly cited reason for gene RETENTION after duplication (preserves both copies because each is now essential)
Subfunctionalization: Ancestral functions are PARTITIONED between copies -> explains DIVERGENCE of duplicate copies, but both copies must be retained to maintain the full ancestral function
Dosage balance: Some genes are retained in duplicate to maintain stoichiometric balance in protein complexes
Trap: Questions may ask "what explains retention" vs "what explains divergence" -- these have different best answers
For retention: neofunctionalization (new function makes both copies essential)
For divergence of expression/function: subfunctionalization (partitioning of ancestral roles)
Advanced Genetics Traps v2
PGS vs Heritability: "Necessarily True" Logic
For "necessarily true" questions about PGS and heritability: a statement is necessarily true only if it holds when V_D=0 AND when V_D=V_G. Test the extremes.
Path Diagram Sign Assignment Protocol
Do NOT guess path signs from general knowledge. Signs may differ from well-known systems. Follow this protocol:
Establish reference direction: What varies? What is increasing?
For each path X→Y: Ask ONLY "when X increases, does Y increase (+) or decrease (-)?"
Use the question's experimental context (knockout/control comparisons, provided data) to determine signs — not intuition
Expect negative paths: Path diagrams test your ability to identify negative relationships. All-positive is almost always wrong. Direct residual paths (e) often have opposite sign from expectation.
Chi-Square: "Most Likely to Reject" Protocol
Compute chi-square from the expected ratio given in the question. Compare to chi-square-critical at df = (number of phenotype classes - 1). Pick the answer choice with the highest chi-square, but also check which pattern is biologically diagnostic of the alternative hypothesis.
LD and Misleading GWAS Associations
LD block boundaries at recombination hotspots are a source of GWAS false localization — strong signal in the block does not guarantee the causal variant is in the block.
Low-Frequency Allele Detection
Duplex sequencing (unique molecular identifiers + double-strand consensus) detects alleles at 0.01% frequency — far below standard NGS even at 80X depth. Simply increasing read depth does NOT help for ultra-rare variants because the Illumina error rate (~0.1%) masks variants rarer than ~1% regardless of depth. Error correction methods (UMIs, duplex consensus) are needed to distinguish true rare variants from sequencing errors.
