Dataset Information

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

ABSTRACT: 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 genome-wide association study 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.

SUBMITTER: Clark MM

PROVIDER: S-EPMC4715557 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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

Clark Michelle M MM Blangero John J Dyer Thomas D TD Sobel Eric M EM Sinsheimer Janet S JS

Annals of human genetics 20151115 1

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 ...[more]

PMID: 26567478

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Project description:BackgroundObservational and experimental evidence demonstrates that protein intake in infancy programs linear growth. To our knowledge, few studies have examined prenatal maternal protein intake.ObjectiveOur objective was to examine associations of maternal protein intake during pregnancy with offspring linear growth.DesignWe analyzed data from 1961 mother-child pairs in Project Viva. We assessed first- and second-trimester diet with the use of food-frequency questionnaires and analyzed protein intake as grams per kilogram prepregnancy weight per day. We used research measures of offspring length at birth and in infancy (∼6 mo), early childhood (∼3 y), and midchildhood (∼7 y), as well as clinical growth measures obtained from after birth through midchildhood. We calculated sex-specific birth length z scores for gestational age with the use of international reference data. We used mixed models with repeated length measures to predict individual length gain velocities for birth to <6 mo and 6 mo to 7 y of age, then used these velocities as outcomes in adjusted linear regression models with maternal protein intake as the main predictor.ResultsMean (range) second-trimester protein intake was 1.4 g · kg-1 · d-1 (0.3-3.1 g · kg-1 · d-1). After adjusting for maternal sociodemographics, gestational weight gain, maternal and paternal height, and (for postdelivery outcomes) child sex, gestational age, and breastfeeding duration, each 1-SD (0.36 g · kg-1 · d-1) increment in second-trimester protein intake corresponded to a -0.10 (95% CI: -0.18, -0.03) change in birth length z score, a -0.03 cm/mo (95% CI: -0.05, -0.01 cm/mo) change in slope of length growth from birth to <6mo, and a -0.09 cm/y (95% CI: -0.14, -0.05 cm/y) change in slope of length growth from 6 mo to midchildhood. Results were similar for first-trimester intake.ConclusionsIn a population with relatively high protein intake during pregnancy, higher protein intake was associated with shorter offspring birth length and slower linear growth into midchildhood. Results suggest that higher protein intake during pregnancy does not increase fetal and child growth and may even reduce early length growth. Project Viva was registered at clinicaltrials.gov as NCT02820402.

Project description:Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.

Dataset Information

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

Publications

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

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