ArticleGenetic epidemiology2018
Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm.
Article in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2.Molecular psychiatry · 2022Pooled it
- FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies.Human molecular genetics · 2023Article
- A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests.Bioinformatics (Oxford, England) · 2021Article
- A comparison of popular TDT-generalizations for family-based association analysis.Genetic epidemiology · 2019Article
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7 authors.
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Abstract
For family-based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT-approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts. Here, we propose a simplification of the original algorithm that maintains the original approach but reduces the computational burden of the approach substantially and gives valuable insights regarding the conditional distribution. With the modified algorithm, the application to whole-genome sequencing (WGS) studies becomes feasible; for example, in sliding window approaches or spatial-clustering approaches. The reduction of the computational burden that our modification provides is especially dramatic when both parental genotypes are missing. For example, for eight variants and 441 nuclear families with mostly offspring-only families, in a WGS study at the APOE locus, the running time decreased from approximately 21 hr for the original algorithm to 0.11 sec after our modification.
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