About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Genetic Disorder Risk Prediction
- Language – English
- Category – Genomics Analysis
- Prompt Title – AI Prompt for Analyzing Genomic Data to Predict Genetic Disorders
Prompt Details
**Prompt Type:** Dynamic
**Purpose:** Genomics Analysis for Genetic Disorder Risk Prediction
**Target AI Platforms:** All
**Description:** This prompt aims to analyze provided genomic data to predict the risk of specific genetic disorders. It is designed to be adaptable to various data formats (VCF, BAM, FASTQ) and disorder types, providing detailed risk assessments and supporting evidence. The prompt encourages the AI to leverage its knowledge of genetic inheritance patterns, population frequencies, variant pathogenicity, and disease mechanisms.
**Prompt Structure:**
“`
## Genomic Data Analysis for Genetic Disorder Risk Prediction
**1. Data Input:**
* **Data Format:** {Specify data format: VCF, BAM, FASTQ, etc.}
* **Data Source:** {Specify data source: Direct upload, URL, file path, etc.}
* **Data Description:** {Provide a concise description of the genomic data: e.g., whole-genome sequencing, exome sequencing, targeted sequencing, etc.}
* **Sample Information (Optional):** {Include relevant patient information such as age, sex, ethnicity, and family history if available. Emphasize that this information is optional and should only be used if provided.}
**2. Analysis Focus:**
* **Disorder(s) of Interest:** {Specify the target genetic disorder(s) or categories, e.g., “Cystic Fibrosis”, “Hereditary Cancer Syndromes”, “Cardiomyopathies”. If the user wants a broad analysis, specify “General Genetic Disorder Risk”.}
* **Inheritance Mode (Optional):** {Specify suspected inheritance pattern if known (e.g., autosomal dominant, autosomal recessive, X-linked). If unknown, the AI should consider all possibilities.}
* **Penetrance (Optional):** {If applicable, provide information about the penetrance of the disorder(s) of interest.}
**3. Analysis Tasks:**
* **Variant Identification:** Identify and list all relevant genetic variants present in the provided data.
* **Variant Annotation:** Annotate identified variants with information including:
* Gene name and location
* Predicted impact on protein function (e.g., missense, nonsense, frameshift)
* Population frequency (e.g., from gnomAD, 1000 Genomes Project)
* Known pathogenicity classifications (e.g., from ClinVar, HGMD)
* Predicted pathogenicity scores (e.g., CADD, SIFT, PolyPhen)
* **Risk Assessment:** Based on the identified and annotated variants, assess the individual’s risk for the specified disorder(s). Provide a clear and concise risk categorization (e.g., low, moderate, high) with associated confidence levels.
* **Supporting Evidence:** Provide detailed explanations for the risk assessment, including:
* Specific variants contributing to the risk
* Evidence from variant databases and literature supporting the pathogenicity of the variants
* Inheritance patterns and population frequencies supporting the risk assessment.
* **Limitations:** Clearly state any limitations of the analysis, including:
* Data quality issues
* Incomplete coverage of the genome
* Limitations of current knowledge about the genetic basis of the disorder(s)
* Lack of phenotypic information
**4. Output Format:**
* Provide the results in a structured, human-readable format. Preferably a JSON format including separate sections for variant information, risk assessment, and supporting evidence. Alternatively, a well-formatted table can be used.
**Example Usage:**
“`
## Genomic Data Analysis for Genetic Disorder Risk Prediction
**1. Data Input:**
* Data Format: VCF
* Data Source: /path/to/patient_genome.vcf
* Data Description: Whole-genome sequencing data
**2. Analysis Focus:**
* Disorder(s) of Interest: Cystic Fibrosis, Hereditary Breast and Ovarian Cancer Syndrome
**3. Analysis Tasks:** (As described above)
**4. Output Format:** JSON
“`
**Note:** This prompt is designed to be adaptable. You can modify the specific parameters (data format, disorder of interest, etc.) based on the specific analysis needs. For complex analyses, consider breaking down the prompt into smaller, more manageable sub-prompts. Always prioritize patient privacy and data security when handling genomic data.
“`
This detailed prompt allows users to analyze genomic data for a wide range of genetic disorders. The dynamic structure enables customization based on specific requirements, making it suitable for diverse genomic analysis tasks and adaptable to future advancements in genomics research. Providing clear instructions and specifying desired output formats ensures interpretable and actionable results. The inclusion of limitations encourages the AI to acknowledge the inherent uncertainties associated with genomic risk predictions and promotes responsible use of this technology.