[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAVP392EV8QW6Z9172Y2EQ":3,"skill-same:01M6DAVP392EV8QW6Z9172Y2EQ":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":71,"commitSha":72,"skillDirectory":73,"forksCount":14,"repositoryHtmlUrl":74,"repositoryHomepageUrl":6,"ownerHtmlUrl":75,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":76,"install":77,"resources":90},"01M6DAVP392EV8QW6Z9172Y2EQ","tooluniverse-population-genetics-1000genomes",null,"Population genetics research using the 1000 Genomes Project (IGSR) -- search populations by superpopulation ancestry (AFR, AMR, EAS, EUR, SAS), retrieve samples by population code, list available data collections, and integrate with GWAS tools for population stratification analysis. Use when users ask about 1000 Genomes populations, sample ancestry, allele frequency variation across continental groups, population-specific GWAS interpretation, or IGSR data collections like the 30x high-coverage resequencing or HGSVC.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-population-genetics-1000genomes\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"tooluniverse population genetics 1000genomes","genetics","coverage","collections","populations","sample","project","across","integrate","use","research","list","ask","analysis","Tools","data","search","code","interpretation","2026-11-10 10:12:30",false,"---\nname: tooluniverse-population-genetics-1000genomes\ndescription: >\n  Population genetics research using the 1000 Genomes Project (IGSR) -- search populations by\n  superpopulation ancestry (AFR, AMR, EAS, EUR, SAS), retrieve samples by population code,\n  list available data collections, and integrate with GWAS tools for population stratification\n  analysis. Use when users ask about 1000 Genomes populations, sample ancestry, allele frequency\n  variation across continental groups, population-specific GWAS interpretation, or IGSR data\n  collections like the 30x high-coverage resequencing or HGSVC.\ntriggers:\n  - keywords: [1000 Genomes, IGSR, population, superpopulation, AFR, AMR, EAS, EUR, SAS, YRI, GBR, CHB, population stratification, ancestry, admixture, allele frequency, population genetics]\n  - patterns: [\"population code\", \"population stratification\", \"ancestral population\", \"1000 Genomes sample\", \"continental ancestry\", \"superpopulation filter\"]\n---\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n# Population Genetics with 1000 Genomes (IGSR)\n\nUse IGSR tools to search 1000 Genomes populations and samples, explore data collections, and\ncombine with GWAS tools for population-stratified analysis.\n\n## When to Use\n\n- \"List all African (AFR) populations in the 1000 Genomes Project\"\n- \"Find samples from the YRI (Yoruba) population\"\n- \"What 1000 Genomes data collections are available?\"\n- \"Which GWAS SNPs for type 2 diabetes have population-specific effects?\"\n- \"Find all SNPs mapped to TCF7L2 in GWAS studies\"\n\n## NOT for (use other skills instead)\n\n- Allele frequencies from gnomAD -> Use `tooluniverse-population-genetics`\n- ClinVar \u002F OMIM variant interpretation -> Use `tooluniverse-variant-interpretation`\n- GWAS fine-mapping -> Use `tooluniverse-gwas-finemapping`\n\n---\n\n## Phase 1: Search 1000 Genomes Populations\n\n**IGSR_search_populations**: `superpopulation` (string\u002Fnull, one of AFR\u002FAMR\u002FEAS\u002FEUR\u002FSAS), `query` (string\u002Fnull, free-text search by name), `limit` (int).\nReturns `{status, data: {total, populations: [{code, name, description, sample_count, superpopulation_code, superpopulation_name, latitude, longitude}]}, metadata: {source, filter_superpopulation, filter_query}}`.\n\nSuperpopulation codes:\n| Code | Ancestry |\n|------|----------|\n| AFR | African |\n| AMR | Admixed American |\n| EAS | East Asian |\n| EUR | European |\n| SAS | South Asian |\n\n```json\n\u002F\u002F List all AFR populations\n{\"superpopulation\": \"AFR\", \"limit\": 10}\n\n\u002F\u002F Search by name (free-text)\n{\"query\": \"Yoruba\", \"limit\": 5}\n\n\u002F\u002F List all populations\n{\"limit\": 26}\n```\n\nResponse example:\n```json\n{\n  \"status\": \"success\",\n  \"data\": {\n    \"total\": 3,\n    \"populations\": [\n      {\"code\": \"YRI\", \"name\": \"Yoruba\", \"description\": \"Yoruba in Ibadan, Nigeria\",\n       \"sample_count\": 188, \"superpopulation_code\": \"AFR\", \"superpopulation_name\": \"African Ancestry\"}\n    ]\n  }\n}\n```\n\n---\n\n## Phase 2: Search Samples by Population\n\n**IGSR_search_samples**: `population` (string\u002Fnull, population code e.g. \"YRI\"), `data_collection` (string\u002Fnull, collection title), `sample_name` (string\u002Fnull, specific sample e.g. \"NA12878\"), `limit` (int).\nReturns `{status, data: {total, samples: [{name, sex, biosample_id, populations: [{code, name, superpopulation}], data_collections: [...]}]}}`.\n\n```json\n\u002F\u002F Find all YRI samples\n{\"population\": \"YRI\", \"limit\": 10}\n\n\u002F\u002F Look up the reference sample NA12878\n{\"sample_name\": \"NA12878\", \"limit\": 1}\n\n\u002F\u002F Find samples in the 30x high-coverage collection\n{\"data_collection\": \"1000 Genomes 30x on GRCh38\", \"limit\": 5}\n```\n\nNOTE: `population` takes a population code (e.g. \"YRI\", \"GBR\", \"CHB\"), not a superpopulation code. Use IGSR_search_populations first to get population codes if starting from a superpopulation.\n\n---\n\n## Phase 3: List Data Collections\n\n**IGSR_list_data_collections**: `limit` (int).\nReturns `{status, data: {total, collections: [{code, title, short_title, sample_count, population_count, data_types, website}]}}`.\n\n```json\n{\"limit\": 20}\n```\n\nKey collections available (18 total):\n| Collection | Description | Data Types |\n|------------|-------------|------------|\n| 1000 Genomes on GRCh38 | 2709 samples, 26 populations | sequence, alignment, variants |\n| 1000 Genomes 30x on GRCh38 | High-coverage resequencing | sequence, alignment, variants |\n| 1000 Genomes phase 3 release | Original phase 3 | sequence, alignment, variants |\n| Human Genome Structural Variation Consortium | HGSVC SV discovery | sequence, alignment |\n| MAGE RNA-seq | RNA-seq data | - |\n| Geuvadis | Expression + genotype | - |\n\n---\n\n## Phase 4: GWAS Context for Population Stratification\n\n### Search GWAS associations for a trait\n\n**gwas_search_associations**: `trait` (string, free text), `limit` (int).\nReturns GWAS associations with rsID, p-value, mapped genes, EFO trait IDs.\n\n```json\n{\"trait\": \"type 2 diabetes\", \"limit\": 10}\n```\n\n### Get variants for a specific trait (by EFO ID)\n\n**gwas_get_variants_for_trait**: `trait` (string, EFO ID e.g. \"EFO_0001645\"), `limit` (int).\n\n```json\n{\"trait\": \"EFO_0001645\", \"limit\": 10}\n```\n\n### Find SNPs in a gene from GWAS catalog\n\n**gwas_get_snps_for_gene**: `gene_symbol` (string), `limit` (int).\nReturns SNPs mapped to the gene with rsIDs, genomic positions, functional classes.\n\n```json\n{\"gene_symbol\": \"TCF7L2\", \"limit\": 10}\n```\n\n---\n\n## Workflow: Population Stratification in GWAS\n\nStep 1 -- Find populations of interest:\n```json\n\u002F\u002F Get all EUR populations\n{\"superpopulation\": \"EUR\", \"limit\": 10}\n\u002F\u002F -> Returns codes like GBR, FIN, CEU, TSI, IBS\n```\n\nStep 2 -- Get samples from target population:\n```json\n\u002F\u002F Get YRI samples (AFR)\n{\"population\": \"YRI\", \"limit\": 100}\n```\n\nStep 3 -- Get GWAS SNPs for the gene or trait:\n```json\n\u002F\u002F GWAS hits for TCF7L2 (T2D gene)\n{\"gene_symbol\": \"TCF7L2\", \"limit\": 20}\n```\n\nStep 4 -- Cross-reference with population data for stratification analysis.\n\n---\n\n## Common Population Codes\n\n| Code | Population | Superpopulation |\n|------|-----------|-----------------|\n| YRI | Yoruba in Ibadan, Nigeria | AFR |\n| LWK | Luhya in Webuye, Kenya | AFR |\n| GWD | Gambian Mandinka | AFR |\n| CEU | Utah residents (CEPH) | EUR |\n| GBR | British in England\u002FScotland | EUR |\n| FIN | Finnish in Finland | EUR |\n| TSI | Toscani in Italia | EUR |\n| CHB | Han Chinese in Beijing | EAS |\n| JPT | Japanese in Tokyo | EAS |\n| CHS | Southern Han Chinese | EAS |\n| MXL | Mexican Ancestry in LA | AMR |\n| PUR | Puerto Rican in Puerto Rico | AMR |\n| GIH | Gujarati Indian in Houston | SAS |\n| PJL | Punjabi from Lahore | SAS |\n\n---\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | AF difference > 0.2 across superpopulations, GWAS p \u003C 5e-8, replicated in multiple cohorts | rs7903146 (TCF7L2) with AF = 0.30 EUR vs 0.05 EAS, GWAS p = 1e-40 |\n| **Moderate** | AF difference 0.05-0.2, GWAS p \u003C 5e-8 in one ancestry, nominal in others | Variant with AF = 0.15 AFR vs 0.08 EUR, GWAS p \u003C 5e-8 in EUR only |\n| **Weak** | AF difference \u003C 0.05, GWAS p \u003C 5e-8 but single study, no cross-ancestry replication | Common variant with similar AF across populations, significant in one cohort |\n| **Population-specific** | Variant common (AF > 0.01) in one superpopulation, rare (AF \u003C 0.01) in others | Sickle cell variant (rs334) AF ~0.10 in AFR, \u003C 0.001 elsewhere |\n\n### Interpretation Guidance\n\n- **Allele frequency interpretation by ancestry**: Allele frequencies vary across superpopulations (AFR, AMR, EAS, EUR, SAS) due to genetic drift, selection, and demographic history. AFR populations have the highest genetic diversity and longest haplotypes broken by recombination. Disease-risk alleles may be common in one ancestry and rare in another, leading to differential genetic risk across populations.\n- **Fst significance thresholds**: Fst measures population differentiation (0 = no differentiation, 1 = complete fixation of different alleles). Global Fst for human populations averages ~0.12. Locus-specific Fst > 0.3 suggests strong differentiation (possible selection). Fst > 0.5 is extreme and rare in humans outside known selection targets (e.g., SLC24A5 for skin pigmentation). Compare locus Fst against genome-wide distribution to identify outliers.\n- **LD interpretation**: Linkage disequilibrium (LD) patterns differ by ancestry. AFR populations have shorter LD blocks due to older demographic history, requiring denser genotyping for fine-mapping. EUR and EAS populations have longer LD blocks. When a GWAS hit is in LD with multiple variants, the causal variant is more likely to be resolved in AFR-ancestry data. Report r-squared values: r2 > 0.8 = strong LD, 0.2-0.8 = moderate, \u003C 0.2 = weak.\n- **Population stratification**: Uncontrolled population structure in GWAS inflates false positives. The 1000 Genomes superpopulation labels provide a framework for stratified analysis. Mixed-ancestry samples (e.g., AMR) require local ancestry deconvolution for accurate interpretation.\n- **Sample size context**: 1000 Genomes has ~2500 samples across 26 populations. Population-specific allele frequencies have limited precision for smaller populations (N \u003C 100). For rare variants (AF \u003C 0.01), larger resources like gnomAD provide more reliable estimates.\n\n### Synthesis Questions\n\n1. Does the allele frequency of the variant of interest differ meaningfully (> 5%) across superpopulations, and could this explain differential disease prevalence or GWAS effect sizes?\n2. Is the GWAS association replicated across ancestries, or is it population-specific, potentially due to LD structure differences or population-specific selection?\n3. For fine-mapping, does the LD pattern in AFR populations narrow the association signal compared to EUR, helping identify the likely causal variant?\n4. Are the population labels and sample sizes in the 1000 Genomes dataset adequate for the analysis, or is the target population underrepresented?\n5. Could population stratification (uncontrolled ancestry differences between cases and controls) explain the observed association, rather than a true genetic effect?\n\n---\n\n## Tool Parameter Quick Reference\n\n| Tool | Key Parameters | Notes |\n|------|---------------|-------|\n| IGSR_search_populations | superpopulation, query, limit | superpopulation: AFR\u002FAMR\u002FEAS\u002FEUR\u002FSAS |\n| IGSR_search_samples | population, data_collection, sample_name, limit | population = population code (e.g. YRI) |\n| IGSR_list_data_collections | limit | 18 collections total |\n| gwas_search_associations | trait, limit | free-text trait search |\n| gwas_get_variants_for_trait | trait, limit | trait = EFO ID |\n| gwas_get_snps_for_gene | gene_symbol, limit | returns mapped SNPs |\n","## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n# Population Genetics with 1000 Genomes (IGSR)\n\nUse IGSR tools to search 1000 Genomes populations and samples, explore data collections, and\ncombine with GWAS tools for population-stratified analysis.\n\n## When to Use\n\n- \"List all African (AFR) populations in the 1000 Genomes Project\"\n- \"Find samples from the YRI (Yoruba) population\"\n- \"What 1000 Genomes data collections are available?\"\n- \"Which GWAS SNPs for type 2 diabetes have population-specific effects?\"\n- \"Find all SNPs mapped to TCF7L2 in GWAS studies\"\n\n## NOT for (use other skills instead)\n\n- Allele frequencies from gnomAD -> Use `tooluniverse-population-genetics`\n- ClinVar \u002F OMIM variant interpretation -> Use `tooluniverse-variant-interpretation`\n- GWAS fine-mapping -> Use `tooluniverse-gwas-finemapping`\n\n---\n\n## Phase 1: Search 1000 Genomes Populations\n\n**IGSR_search_populations**: `superpopulation` (string\u002Fnull, one of AFR\u002FAMR\u002FEAS\u002FEUR\u002FSAS), `query` (string\u002Fnull, free-text search by name), `limit` (int).\nReturns `{status, data: {total, populations: [{code, name, description, sample_count, superpopulation_code, superpopulation_name, latitude, longitude}]}, metadata: {source, filter_superpopulation, filter_query}}`.\n\nSuperpopulation codes:\n| Code | Ancestry |\n|------|----------|\n| AFR | African |\n| AMR | Admixed American |\n| EAS | East Asian |\n| EUR | European |\n| SAS | South Asian |\n\n```json\n\u002F\u002F List all AFR populations\n{\"superpopulation\": \"AFR\", \"limit\": 10}\n\n\u002F\u002F Search by name (free-text)\n{\"query\": \"Yoruba\", \"limit\": 5}\n\n\u002F\u002F List all populations\n{\"limit\": 26}\n```\n\nResponse example:\n```json\n{\n  \"status\": \"success\",\n  \"data\": {\n    \"total\": 3,\n    \"populations\": [\n      {\"code\": \"YRI\", \"name\": \"Yoruba\", \"description\": \"Yoruba in Ibadan, Nigeria\",\n       \"sample_count\": 188, \"superpopulation_code\": \"AFR\", \"superpopulation_name\": \"African Ancestry\"}\n    ]\n  }\n}\n```\n\n---\n\n## Phase 2: Search Samples by Population\n\n**IGSR_search_samples**: `population` (string\u002Fnull, population code e.g. \"YRI\"), `data_collection` (string\u002Fnull, collection title), `sample_name` (string\u002Fnull, specific sample e.g. \"NA12878\"), `limit` (int).\nReturns `{status, data: {total, samples: [{name, sex, biosample_id, populations: [{code, name, superpopulation}], data_collections: [...]}]}}`.\n\n```json\n\u002F\u002F Find all YRI samples\n{\"population\": \"YRI\", \"limit\": 10}\n\n\u002F\u002F Look up the reference sample NA12878\n{\"sample_name\": \"NA12878\", \"limit\": 1}\n\n\u002F\u002F Find samples in the 30x high-coverage collection\n{\"data_collection\": \"1000 Genomes 30x on GRCh38\", \"limit\": 5}\n```\n\nNOTE: `population` takes a population code (e.g. \"YRI\", \"GBR\", \"CHB\"), not a superpopulation code. Use IGSR_search_populations first to get population codes if starting from a superpopulation.\n\n---\n\n## Phase 3: List Data Collections\n\n**IGSR_list_data_collections**: `limit` (int).\nReturns `{status, data: {total, collections: [{code, title, short_title, sample_count, population_count, data_types, website}]}}`.\n\n```json\n{\"limit\": 20}\n```\n\nKey collections available (18 total):\n| Collection | Description | Data Types |\n|------------|-------------|------------|\n| 1000 Genomes on GRCh38 | 2709 samples, 26 populations | sequence, alignment, variants |\n| 1000 Genomes 30x on GRCh38 | High-coverage resequencing | sequence, alignment, variants |\n| 1000 Genomes phase 3 release | Original phase 3 | sequence, alignment, variants |\n| Human Genome Structural Variation Consortium | HGSVC SV discovery | sequence, alignment |\n| MAGE RNA-seq | RNA-seq data | - |\n| Geuvadis | Expression + genotype | - |\n\n---\n\n## Phase 4: GWAS Context for Population Stratification\n\n### Search GWAS associations for a trait\n\n**gwas_search_associations**: `trait` (string, free text), `limit` (int).\nReturns GWAS associations with rsID, p-value, mapped genes, EFO trait IDs.\n\n```json\n{\"trait\": \"type 2 diabetes\", \"limit\": 10}\n```\n\n### Get variants for a specific trait (by EFO ID)\n\n**gwas_get_variants_for_trait**: `trait` (string, EFO ID e.g. \"EFO_0001645\"), `limit` (int).\n\n```json\n{\"trait\": \"EFO_0001645\", \"limit\": 10}\n```\n\n### Find SNPs in a gene from GWAS catalog\n\n**gwas_get_snps_for_gene**: `gene_symbol` (string), `limit` (int).\nReturns SNPs mapped to the gene with rsIDs, genomic positions, functional classes.\n\n```json\n{\"gene_symbol\": \"TCF7L2\", \"limit\": 10}\n```\n\n---\n\n## Workflow: Population Stratification in GWAS\n\nStep 1 -- Find populations of interest:\n```json\n\u002F\u002F Get all EUR populations\n{\"superpopulation\": \"EUR\", \"limit\": 10}\n\u002F\u002F -> Returns codes like GBR, FIN, CEU, TSI, IBS\n```\n\nStep 2 -- Get samples from target population:\n```json\n\u002F\u002F Get YRI samples (AFR)\n{\"population\": \"YRI\", \"limit\": 100}\n```\n\nStep 3 -- Get GWAS SNPs for the gene or trait:\n```json\n\u002F\u002F GWAS hits for TCF7L2 (T2D gene)\n{\"gene_symbol\": \"TCF7L2\", \"limit\": 20}\n```\n\nStep 4 -- Cross-reference with population data for stratification analysis.\n\n---\n\n## Common Population Codes\n\n| Code | Population | Superpopulation |\n|------|-----------|-----------------|\n| YRI | Yoruba in Ibadan, Nigeria | AFR |\n| LWK | Luhya in Webuye, Kenya | AFR |\n| GWD | Gambian Mandinka | AFR |\n| CEU | Utah residents (CEPH) | EUR |\n| GBR | British in England\u002FScotland | EUR |\n| FIN | Finnish in Finland | EUR |\n| TSI | Toscani in Italia | EUR |\n| CHB | Han Chinese in Beijing | EAS |\n| JPT | Japanese in Tokyo | EAS |\n| CHS | Southern Han Chinese | EAS |\n| MXL | Mexican Ancestry in LA | AMR |\n| PUR | Puerto Rican in Puerto Rico | AMR |\n| GIH | Gujarati Indian in Houston | SAS |\n| PJL | Punjabi from Lahore | SAS |\n\n---\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | AF difference > 0.2 across superpopulations, GWAS p \u003C 5e-8, replicated in multiple cohorts | rs7903146 (TCF7L2) with AF = 0.30 EUR vs 0.05 EAS, GWAS p = 1e-40 |\n| **Moderate** | AF difference 0.05-0.2, GWAS p \u003C 5e-8 in one ancestry, nominal in others | Variant with AF = 0.15 AFR vs 0.08 EUR, GWAS p \u003C 5e-8 in EUR only |\n| **Weak** | AF difference \u003C 0.05, GWAS p \u003C 5e-8 but single study, no cross-ancestry replication | Common variant with similar AF across populations, significant in one cohort |\n| **Population-specific** | Variant common (AF > 0.01) in one superpopulation, rare (AF \u003C 0.01) in others | Sickle cell variant (rs334) AF ~0.10 in AFR, \u003C 0.001 elsewhere |\n\n### Interpretation Guidance\n\n- **Allele frequency interpretation by ancestry**: Allele frequencies vary across superpopulations (AFR, AMR, EAS, EUR, SAS) due to genetic drift, selection, and demographic history. AFR populations have the highest genetic diversity and longest haplotypes broken by recombination. Disease-risk alleles may be common in one ancestry and rare in another, leading to differential genetic risk across populations.\n- **Fst significance thresholds**: Fst measures population differentiation (0 = no differentiation, 1 = complete fixation of different alleles). Global Fst for human populations averages ~0.12. Locus-specific Fst > 0.3 suggests strong differentiation (possible selection). Fst > 0.5 is extreme and rare in humans outside known selection targets (e.g., SLC24A5 for skin pigmentation). Compare locus Fst against genome-wide distribution to identify outliers.\n- **LD interpretation**: Linkage disequilibrium (LD) patterns differ by ancestry. AFR populations have shorter LD blocks due to older demographic history, requiring denser genotyping for fine-mapping. EUR and EAS populations have longer LD blocks. When a GWAS hit is in LD with multiple variants, the causal variant is more likely to be resolved in AFR-ancestry data. Report r-squared values: r2 > 0.8 = strong LD, 0.2-0.8 = moderate, \u003C 0.2 = weak.\n- **Population stratification**: Uncontrolled population structure in GWAS inflates false positives. The 1000 Genomes superpopulation labels provide a framework for stratified analysis. Mixed-ancestry samples (e.g., AMR) require local ancestry deconvolution for accurate interpretation.\n- **Sample size context**: 1000 Genomes has ~2500 samples across 26 populations. Population-specific allele frequencies have limited precision for smaller populations (N \u003C 100). For rare variants (AF \u003C 0.01), larger resources like gnomAD provide more reliable estimates.\n\n### Synthesis Questions\n\n1. Does the allele frequency of the variant of interest differ meaningfully (> 5%) across superpopulations, and could this explain differential disease prevalence or GWAS effect sizes?\n2. Is the GWAS association replicated across ancestries, or is it population-specific, potentially due to LD structure differences or population-specific selection?\n3. For fine-mapping, does the LD pattern in AFR populations narrow the association signal compared to EUR, helping identify the likely causal variant?\n4. Are the population labels and sample sizes in the 1000 Genomes dataset adequate for the analysis, or is the target population underrepresented?\n5. Could population stratification (uncontrolled ancestry differences between cases and controls) explain the observed association, rather than a true genetic effect?\n\n---\n\n## Tool Parameter Quick Reference\n\n| Tool | Key Parameters | Notes |\n|------|---------------|-------|\n| IGSR_search_populations | superpopulation, query, limit | superpopulation: AFR\u002FAMR\u002FEAS\u002FEUR\u002FSAS |\n| IGSR_search_samples | population, data_collection, sample_name, limit | population = population code (e.g. YRI) |\n| IGSR_list_data_collections | limit | 18 collections total |\n| gwas_search_associations | trait, limit | free-text trait search |\n| gwas_get_variants_for_trait | trait, limit | trait = EFO ID |\n| gwas_get_snps_for_gene | gene_symbol, limit | returns mapped SNPs |\n",{"name":5,"triggers":43,"description":70},[44,63],{"keywords":45},[46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62],"1000 Genomes","IGSR","population","superpopulation","AFR","AMR","EAS","EUR","SAS","YRI","GBR","CHB","population stratification","ancestry","admixture","allele frequency","population genetics",{"patterns":64},[65,58,66,67,68,69],"population code","ancestral population","1000 Genomes sample","continental ancestry","superpopulation filter","Population genetics research using the 1000 Genomes Project (IGSR) -- search populations by superpopulation ancestry (AFR, AMR, EAS, EUR, SAS), retrieve samples by population code, list available data collections, and integrate with GWAS tools for population stratification analysis. Use when users ask about 1000 Genomes populations, sample ancestry, allele frequency variation across continental groups, population-specific GWAS interpretation, or IGSR data collections like the 30x high-coverage resequencing or HGSVC.\n","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-population-genetics-1000genomes","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":78,"prompt":88,"zipAvailable":89},[79,82,85],{"tool":80,"command":81},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-population-genetics-1000genomes",{"tool":83,"command":84},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-population-genetics-1000genomes",{"tool":86,"command":87},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-population-genetics-1000genomes","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-population-genetics-1000genomes\u002FSKILL.md",true,[],[92,113,144,171,184,200],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":38},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[99,100,101,102,103,104,105,106,107,28,108,109,110,111,112],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":124,"publishedAt":38},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[119],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[125,126,127,128,129,130,131,132,133,134,135,136,137,138,28,139,140,141,142,143],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":156,"publishedAt":38},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[150,153],{"categoryNo":151,"categoryName":152,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":154,"categoryName":155,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000008","测试与质量",[157,158,159,160,161,162,163,164,165,166,126,167,168,169,170],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":38},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[173,178,179,25,180,181,182,183],"setup","report","installation","project setup","issues","state",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":191,"publishedAt":38},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[190],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},[186,192,104,193,194,195,28,196,197,198,199],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":201,"name":178,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":205,"publishedAt":38},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[178,25,141,206,181,207],"files","interactive"]