ABSTRACT: Transcript abundance results from the balance between transcription and mRNA decay, and varies pervasively in humans. We have examined the effect of DNA variation on mRNA half-life differences by conducting a genome-wide survey of mRNA stability in seven human HapMap lymphoblastoid cell lines (LCLs). We determined the mRNA half-life for each gene from the ratio of 4-thio-uridine (4sU)-labeled nascent RNAs to total RNAs. 5,145 (46%) of 11,132 analyzed genes showed inter-individual mRNA half-life differences at a false discovery rate, FDR<0.05. As previously reported, we found transcription to be the main factor influencing transcript abundance. Although mRNA half-life explained only ~6% of transcript abundance on average, it explained ~16% for the subset of genes (~10%) showing inter-individual mRNA half-life differences (P<0.001). We confirmed previously reported correlations of mRNA half-life with transcript length, 3M-bM-^@M-^Y-UTR length, and number of exon-junctions per kb of transcript. The number of miRNA targets in 3M-bM-^@M-^Y-UTRs was negatively correlated with half-life (P=2.2M-CM-^W10-16), a new observation that is consistent with the role of miRNA in inducing mRNA degradation. Notably, coding GC and GC3 content showed positive correlations with mRNA half-life in genes with inter-individual mRNA half-life differences, implying a role of mRNA stability in shaping synonymous codon usage bias. Consistently, G or C alleles of coding SNPs were found associated with longer mRNA half-life (P=0.021). As expected, we also found that nonsense SNPs were associated with shorter mRNA half-life (P=0.009). Our results strongly suggest that inter-individual mRNA stability differences are widespread and affected by DNA sequence and composition variation. A total of 7 HapMap LCLs were used to measure mRNA half-life. Total RNAs and the 4sU-labeled-newly synthesized RNAs (nascent RNAs) were isolated from the same cell culture and were assayed simultaneously with human Exon array. For 3 LCLs, we included 3 biological replicates (i.e., independent cell cultures) and for 1 LCL we also included technical duplicates. mRNA half-life was calculated from the ratio of nascent RNAs/total RNAs. We used ANOVA to test inter-individual difference of mRNA half-life between 3 subjects who have biological replicates and technical duplicates. We examined the Spearman rank correlation of mRNA half-life with a number of gene features, including transcript length, intron length, 5'-UTR length and folding energy, 3'-UTR length and folding energy, microRNA target sites, GC and GC3 contents, etc. We also performed linear regression to test the effects of specific type of sequence variants (nonsense SNPs, SNPs within miRNA target sites, and coding synonymous and nonsynonymous SNPs) on mRNA half-life across 3 subjects that have whole genome sequencing data available (1000 genome project June 2011 release).
