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The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.

Publication ,  Journal Article
Nazarian, A; Arbeev, KG; Kulminski, AM
Published in: Journal of applied genetics
February 2020

The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P < 5E-08) as the four P values resulted from the tested methods for any SNP were all below or all above 5E-08. Nevertheless, larger discrepancies were detected between logistic regression and LMMs-based models at suggestive level of significance (i.e., of 5E-08 ≤ P < 5E-06). The SNP effects estimated by the logistic regression models were not statistically different from those estimated by GLMMs that implemented Wald's test. However, several SNP effects were significantly different from their counterparts in LMMs analyses. We suggest that fitting GLMMs with Wald's test on a pre-selected subset of SNPs obtained from logistic regression models can ensure the balance between the speed of analyses and the accuracy of parameters.

Duke Scholars

Published In

Journal of applied genetics

DOI

EISSN

2190-3883

ISSN

1234-1983

Publication Date

February 2020

Volume

61

Issue

1

Start / End Page

75 / 86

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Plant Biology & Botany
  • Pedigree
  • Multifactorial Inheritance
  • Models, Genetic
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Algorithms
 

Citation

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ICMJE
MLA
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Nazarian, A., Arbeev, K. G., & Kulminski, A. M. (2020). The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees. Journal of Applied Genetics, 61(1), 75–86. https://doi.org/10.1007/s13353-019-00526-7
Nazarian, Alireza, Konstantin G. Arbeev, and Alexander M. Kulminski. “The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.Journal of Applied Genetics 61, no. 1 (February 2020): 75–86. https://doi.org/10.1007/s13353-019-00526-7.
Nazarian A, Arbeev KG, Kulminski AM. The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees. Journal of applied genetics. 2020 Feb;61(1):75–86.
Nazarian, Alireza, et al. “The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.Journal of Applied Genetics, vol. 61, no. 1, Feb. 2020, pp. 75–86. Epmc, doi:10.1007/s13353-019-00526-7.
Nazarian A, Arbeev KG, Kulminski AM. The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees. Journal of applied genetics. 2020 Feb;61(1):75–86.
Journal cover image

Published In

Journal of applied genetics

DOI

EISSN

2190-3883

ISSN

1234-1983

Publication Date

February 2020

Volume

61

Issue

1

Start / End Page

75 / 86

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Plant Biology & Botany
  • Pedigree
  • Multifactorial Inheritance
  • Models, Genetic
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Algorithms