Abstract
Low or high birth weight is one of the main causes for neonatal morbidity and mortality. They are also associated with adulthood chronic illness. Birth weight is a complex trait which is affected by baby's genes, maternal environments as well as the complex interactions between them. To understand the genetic basis of birth weight, we reanalyzed a genome-wide association study data set which consists of four populations, namely Thai, Afro-Caribbean, European, and Hispanic population with regular linear models. In addition to fit the data with parametric linear models, we fitted the data with a nonparametric varying-coefficient model to identify variants that are nonlinearly modulated by mother's condition to affect birth weight. For this purpose, we used baby's cord glucose level as the mother's environmental variable. At the 10(-5) genome-wide threshold, we identified 33 SNP variants in the Thai population, 26 SNPs in the Afro-Caribbean population, 18 SNPs in the European population, and 7 SNPs in the Hispanic population. Some of the variants are significantly modulated by baby's cord glucose level either linearly or nonlinearly, implying potential interactions between baby's gene and mother's glucose level to affect baby's birth weight. There is no overlap between variants identified in the four populations, indicating strong genetic heterogeneity of birth weight between the four ethnic groups. The findings of this study provide insights into the genetic basis of birth weight and reveal its genetic heterogeneity.