Routines for the statistical analysis of indirectly measured haplotypes, developed by Schaid Daniel. The methods assume all subjects are unrelated and haplotypes are ambiguous due to unknown linkage phase. The main functions include haplo.em(), haplo.glm(), haplo.score(), and haplo.power().
Use Cases
- Estimate haplotype frequencies based on ambiguous genotype data using the haplo.em() function.
- Perform regression analysis of traits on haplotypes with covariates using the haplo.glm() function.
- Test for association between haplotypes and a trait using the haplo.score() function.
- Calculate statistical power for haplotype association studies using the haplo.power() function.
Strengths
- Methods are specifically designed for the common scenario of ambiguous haplotypes.
- Includes four main statistical functions (haplo.em, haplo.glm, haplo.score, haplo.power) for a complete analysis workflow.
- Detailed examples are provided in the accompanying vignette.
Limitations
- Row count and dataset size are unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Data may reflect bias inherent to the paperswithcode platform's research focus.
Provenance
- Source
- paperswithcode
- Collection Method
- Software package for statistical analysis.
- Time Range
- null
- Freshness
- Last update date is unknown; freshness unverified.
- Geography
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