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. 2018 Sep 17;12(Suppl 9):42.
doi: 10.1186/s12919-018-0151-8. eCollection 2018.

Coping with family structure in genome-wide association studies: a comparative evaluation

Yayun Hsu #  1 , Jonathan Auerbach  1 , Tian Zheng #  1 , Shaw-Hwa Lo  1
Affiliations

Coping with family structure in genome-wide association studies: a comparative evaluation

Yayun Hsu et al. BMC Proc. .

Abstract

In this paper, a fully statistical investigation of the control of family structure as random effects is analyzed and discussed, using both the genome-wide association studies (GWAS) data and simulated data. Three modeling strategies are proposed and the analysis results suggest the hybrid use of results from all possible models should be combined in practice.

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Conflict of interest statement

Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Kinship coefficient matrices used in 3 modeling options for fitting the linear mixed-effects model to data with family structures. a Option 1: representatives. b Option 2: kinship. c Option 3: independent
Fig. 2
Fig. 2
Ideal relation among significant SNP sets from 3 different modeling options
Fig. 3
Fig. 3
Manhattan plots for option 1
Fig. 4
Fig. 4
Manhattan plots for option 2 and option 3 (black dots)
Fig. 5
Fig. 5
Significant SNP sets identified from 3 different models (top 100) from 5 random runs. For each run, 1 individual was randomly drawn from each family, which led to different results. The results from the kinship and the independence options remain unchanged. (Coloring: red for representative, green for kinship, and blue for independence)
Fig. 6
Fig. 6
Kinship coefficient matrix used in the simulation study for family structures
Fig. 7
Fig. 7
Simulation results: Significant SNP sets identified from 3 different models (top 20)
See this image and copyright information in PMC

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