Literature DB >> 29159827

Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm.

Julian Hecker1,2, Xin Xu3, F William Townes1, Heide Loehlein Fier1,2, Chris Corcoran4, Nan Laird1, Christoph Lange1,5.   

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.
© 2017 WILEY PERIODICALS, INC.

Entities:  

Keywords:  FBAT; admixture; candidate region; whole-genome sequencing

Mesh:

Substances:

Year:  2017        PMID: 29159827      PMCID: PMC5774664          DOI: 10.1002/gepi.22094

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  5 in total

1.  A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information.

Authors:  D Rabinowitz; N Laird
Journal:  Hum Hered       Date:  2000 Jul-Aug       Impact factor: 0.444

2.  Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics.

Authors:  Steve Horvath; Xin Xu; Stephen L Lake; Edwin K Silverman; Scott T Weiss; Nan M Laird
Journal:  Genet Epidemiol       Date:  2004-01       Impact factor: 2.135

3.  On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows.

Authors:  Heide Loehlein Fier; Dmitry Prokopenko; Julian Hecker; Michael H Cho; Edwin K Silverman; Scott T Weiss; Rudolph E Tanzi; Christoph Lange
Journal:  Genet Epidemiol       Date:  2017-03-20       Impact factor: 2.135

4.  Methods for collapsing multiple rare variants in whole-genome sequence data.

Authors:  Yun Ju Sung; Keegan D Korthauer; Michael D Swartz; Corinne D Engelman
Journal:  Genet Epidemiol       Date:  2014-09       Impact factor: 2.135

5.  Defining window-boundaries for genomic analyses using smoothing spline techniques.

Authors:  Timothy M Beissinger; Guilherme J M Rosa; Shawn M Kaeppler; Daniel Gianola; Natalia de Leon
Journal:  Genet Sel Evol       Date:  2015-04-17       Impact factor: 4.297

  5 in total
  3 in total

1.  A comparison of popular TDT-generalizations for family-based association analysis.

Authors:  Julian Hecker; Nan Laird; Christoph Lange
Journal:  Genet Epidemiol       Date:  2019-01-04       Impact factor: 2.135

2.  Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2.

Authors:  Christoph Lange; Rudolph E Tanzi; Dmitry Prokopenko; Sanghun Lee; Julian Hecker; Kristina Mullin; Sarah Morgan; Yuriko Katsumata; Michael W Weiner; David W Fardo; Nan Laird; Lars Bertram; Winston Hide
Journal:  Mol Psychiatry       Date:  2022-03-04       Impact factor: 13.437

3.  A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests.

Authors:  Julian Hecker; F William Townes; Priyadarshini Kachroo; Cecelia Laurie; Jessica Lasky-Su; John Ziniti; Michael H Cho; Scott T Weiss; Nan M Laird; Christoph Lange
Journal:  Bioinformatics       Date:  2020-12-26       Impact factor: 6.937

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.