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

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.

Association of adiponectin promoter variants with traits and clusters of metabolic syndrome in Arabs: family-based study

Plasma levels of adiponectin are decreased in type 2 diabetes, obesity and hypertension. Our aim was to use a family-based analysis to identify the genetic variants of the adiponectin (ADIPOQ) gene that are associated with obesity, insulin resistance, dyslipidemia and hypertension, among Arabs. We screened 328 Arabs in one large extended family for single nucleotide polymorphisms (SNPs) in the promoter region of the ADIPOQ gene. Two common SNPs were detected: rs17300539 and rs266729. Evidences of association between traits related to the metabolic syndrome and the SNPs were studied by implementing quantitative genetic association analysis. Results showed that SNP rs266729 was significantly associated with body weight (p-value=0.001), waist circumference (p-value=0.037), BMI (p-value=0.015) and percentage of total body fat (p-value=0.003). Up to 4.1% of heritability of obesity traits was explained by the rs266729 locus. Further cross-sectional analysis showed that carriers of the G allele had significantly higher values of waist circumference, BMI and percentage of total body fat (p-values 0.014, 0.004 and 0.032, respectively). No association was detected between SNP rs266729 and other clusters of metabolic syndrome or their traits except for HOMA-IR and fasting plasma insulin levels, p-values 0.035 and 0.004, respectively. In contrast, both measured genotype and cross-sectional analysis failed to detect an association between the SNP rs17300539 with traits and clusters of metabolic syndrome. In conclusion, we showed family-based evidence of association of SNP rs266729 at ADIPOQ gene with traits defining obesity in Arab population. This is important for future prediction and prevention of obesity in population where obesity is in an increasing trend.