Statistical Approaches for Detecting Transgenerational Genetic Effects in Humans

Sinsheimer, Janet S.; Creek, Michelle M.

doi:10.1007/978-1-4614-8078-5_11

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…To date, studies have not looked at the role of MFG incompatibility on the quantitative traits related to these adult onset diseases. Although there are a number of methods for investigating MFG incompatibility as a risk factor for disease (see Sinsheimer & Creek, 2013 for a review of these methods), the proposed methods to investigate the effects of maternal and offspring genes on quantitative traits typically rely on retrospective likelihoods and are limited to case-parent trios (Kistner & Weinberg, 2004, Kistner & Weinberg, 2005, Wheeler & Cordell, 2007. Moreover, the retrospective likelihood design is not easily generalized to arbitrary family structures, multiple markers, or multivariate traits (Kraft et al, 2004) and parameter interpretation can be challenging.…”

Section: Introductionmentioning

confidence: 99%

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

Clark

Blangero

Dyer

et al. 2015

Annals of Human Genetics

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SUMMARY Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the Quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT’s alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With GWAS data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered.

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Section: Introductionmentioning

confidence: 99%

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

Clark

Blangero

Dyer

et al. 2015

Annals of Human Genetics

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“…Furthermore, due to intrinsic large sample property of the ML estimators, small sample size, and/or non‐normal distribution may affect the results of this model. Bayesian SEM approach as one of the familiar solutions was introduced to deal with categorical manifest variables, non‐normal distribution, and small sample size . However, to the best of our knowledge, there is no comprehensive evaluation of Bayesian approach performance for modeling SEM on general pedigree data to date.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian structural equation model in general pedigree data analysis

Akbarzadeh

Moghimbeigi

Morris

et al. 2019

Statistical Analysis

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Structural equation modeling (SEM) is a powerful, comprehensive, and flexible multivariate statistical method for modeling relationships between observed and latent variables. However, in genetic association analysis, frequentist approaches to fitting SEMs do not always lead to convergence and admissible solutions for complex models, categorical variables, complicated data structures such as pedigree data and small sample sizes. Accordingly, to conduct a SEM pedigree data analysis, Stan platform as a probabilistic programming language was applied in our study to propose a new version of the Bayesian approach that adopts Hamiltonian Monte Carlo (HMC) and data augmentation techniques. At first, a comprehensive simulation study was conducted to compare the precision of each parameter of the suggested method with that of the classic technique in terms of bias, alpha error rate, and coverage probability. After that, the method was applied to real data with a conceptual model including ordinal indicators in order to conduct genetic association analysis of two well‐known genetic markers with metabolic syndrome trait. The simulation findings revealed the proposed Bayesian method was a more efficient technique than MLE approach. Moreover, Bayesian approach yielded a better statistical performance in solving the problems than did classic approach.

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“…These designs are direct extensions of association testing for MFG interactions with qualitative traits [40, 41]. In addition to potential difficulties in parameter interpretation, such approaches have typically been limited to case-parent trios and cannot easily account for the main effects of other covariates [42, 43].…”

Section: Introductionmentioning

confidence: 99%

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes

Clark

Chazara²,

Sobel

et al. 2016

Hum Hered

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Background/Aims: Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test. Methods: Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families. Results: We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study. Conclusion: We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

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Statistical Approaches for Detecting Transgenerational Genetic Effects in Humans

Cited by 3 publications

References 53 publications

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

A Bayesian structural equation model in general pedigree data analysis

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring <b><i>KIR</i></b> and <b><i>HLA-C</i></b> Genes

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