🧬 Heredity — Genetic Trait Probability AI

A Python-based AI that uses Bayesian networks to model genetic inheritance and infer the likelihood that a person possesses or exhibits a particular trait. The program computes joint probabilities over gene inheritance and observable traits across generations, demonstrating reasoning under uncertainty through probabilistic inference.

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🧠 Description

This project models heredity and genetic traits using Bayesian inference. Each individual in a dataset has a probabilistic distribution over the number of mutated genes they carry (0, 1, or 2) and whether they express a given trait (True or False).

Using parental information and trait observations, the AI determines:

• The probability of each person having 0, 1, or 2 copies of a gene.
• The probability of each person exhibiting the trait.

The program takes into account:

• Inheritance: Each child inherits one gene from each parent, with mutation probabilities.
• Mutation: A small chance of genetic mutation altering the inherited gene.
• Observation: Known traits influence the probability distribution.

This model showcases Bayesian reasoning, conditional probability, and joint probability normalization to make data-driven genetic predictions.

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⚙️ Features

• Models genetic inheritance using a Bayesian Network
• Computes joint probability distributions over genes and traits
• Performs probabilistic inference with normalization
• Uses realistic biological parameters (e.g., mutation, trait likelihood)
• Outputs probability tables for every person in the dataset

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🧩 Example Usage

Run the program on a sample dataset:

$ python heredity.py data/family0.csv

Example output:

Harry:
  Gene:
    2: 0.0092
    1: 0.4557
    0: 0.5351
  Trait:
    True: 0.2665
    False: 0.7335
James:
  Gene:
    2: 0.1976
    1: 0.5106
    0: 0.2918
  Trait:
    True: 1.0000
    False: 0.0000
Lily:
  Gene:
    2: 0.0036
    1: 0.0136
    0: 0.9827
  Trait:
    True: 0.0000
    False: 1.0000

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🧩 Key Functions

joint_probability(people, one_gene, two_genes, have_trait)

Calculates the joint probability of the given configuration of genes and traits across all people.

update(probabilities, one_gene, two_genes, have_trait, p)

Updates overall probabilities with a new joint probability contribution.

normalize(probabilities)

Normalizes all probabilities so that each distribution sums to 1.

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📊 Concepts Demonstrated

• Bayesian Networks
• Conditional Probability
• Joint Probability Distributions
• Normalization and Inference
• Genetic Modeling and Mutation

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📂 Dataset Example

data/family0.csv

name	mother	father	trait
Harry	Lily	James
James			1
Lily			0

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🧪 Dependencies

• Python 3.x
• Standard libraries only (itertools, csv, sys, etc.)

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🧩 How It Works

1. Load Data: Parses CSV files containing family relationships and traits.
2. Enumerate Probabilities: Calculates all possible combinations of gene and trait distributions.
3. Compute Joint Probability: Uses inheritance and mutation probabilities to calculate likelihoods.
4. Update and Normalize: Updates each individual’s probability distribution and ensures all probabilities sum to 1.
5. Output Results: Prints the probability of each person’s genetic and trait outcomes.

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Part of CS50’s Introduction to Artificial Intelligence with Python