Hapi

Hapi infers haplotypes within nuclear families using a dynamic programming algorithm that selectively ignores uninformative states and state transitions to compute minimum-recombinant and maximum likelihood haplotypes.

Key Features:

• Dynamic programming algorithm: Uses a novel dynamic programming algorithm that selectively ignores uninformative states and state transitions to enhance computational efficiency.
• Haplotype computations: Computes both minimum-recombinant haplotypes and maximum likelihood haplotypes.
• Family-based data processing: Processes genetic information from related individuals within nuclear families to infer inheritance patterns.
• Accuracy over genomic distances: Provides high accuracy of haplotype inference over extended genomic distances.
• Performance benchmarking: Demonstrated speed improvements on a dataset of 103 families, being 3.8 times faster for minimum-recombinant haplotypes and 320 times faster for maximum likelihood haplotypes compared with state-of-the-art algorithms.

Scientific Applications:

• Hereditary disease studies: Enables haplotype-based analyses relevant to mapping hereditary diseases in nuclear families.
• Population genetics: Facilitates population genetics analyses that leverage accurate family-based haplotype inference.
• Evolutionary biology: Aids evolutionary biology studies that examine haplotype structure and transmission.
• Inheritance-pattern analysis: Supports analysis of genetic inheritance patterns within nuclear families.
• Genetic architecture of traits: Facilitates investigation of the genetic architecture of traits and disorders through extended-region haplotype inference.

Methodology:

Implements a novel dynamic programming algorithm that selectively ignores uninformative states and state transitions and computes minimum-recombinant and maximum likelihood haplotypes from genetic data of related individuals.