Common polymorphism in H19 associated with birthweight and cord blood IGF-II levels in humans

Background: Common genetic variation at genes that are imprinted and exclusively maternally expressed could explain the apparent maternal-specific inheritance of low birthweight reported in large family pedigrees. We identified ten single nucleotide polymorphisms ( SNPs) in H19, and we genotyped three of these SNPs in families from the contemporary ALSPAC UK birth cohort ( 1,696 children, 822 mothers and 661 fathers) in order to explore associations with size at birth and cord blood IGF- II levels. Results: Both offspring's and mother's H19 2992C> T SNP genotypes showed associations with offspring birthweight ( P = 0.03 to P = 0.003) and mother's genotype was also associated with cord blood IGF-II levels ( P = 0.0003 to P = 0.0001). The offspring genotype association with birthweight was independent of mother's genotype ( P = 0.01 to P = 0.007). However, mother's untransmitted H19 2992T allele was also associated with larger birthweight ( P = 0.04) and higher cord blood IGF-II levels ( P = 0.002), suggesting a direct effect of mother's genotype on placental IGF-II expression and fetal growth. The association between mother's untransmitted allele and cord blood IGF-II levels was more apparent in offspring of first pregnancies than subsequent pregnancies ( P-interaction = 0.03). Study of the independent Cambridge birth cohort with available DNA in mothers (N = 646) provided additional support for mother's H19 2992 genotype associations with birthweight ( P = 0.04) and with mother's glucose levels ( P = 0.01) in first pregnancies. Conclusion: The common H19 2992T allele, in the mother or offspring or both, may confer reduced fetal growth restraint, as indicated by associations with larger offspring birth size, higher cord blood IGF-II levels, and lower compensatory early postnatal catch-up weight gain, that are more evident among mother's smaller first-born infants

Multiple-test procedures and smile plots

multproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical p-value for rejection of null hypotheses. These procedures define a confidence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal “upper confidence limit” for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis

Multiple-test procedures and smile plots

multproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical pvalue for rejection of null hypotheses. These procedures define a conÞdence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal Òupper confidence limitÓ for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis. Copyright 2003 by Stata Corporation.smile plot, multiple-test procedure, closed testing procedure, data mining, family-wise error rate, false discovery rate, Bonferroni, Sidak, Holm, Holland, Copenhaver, Hochberg, Rom, Simes, Benjamini, Yekutieli, Krieger, Liu