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Searching for parent-of-origin effects on cardiometabolic traits in imprinted genomic regions.

Einat Granot-HershkovitzPeitao WuDavid KarasikInga PeterGina M PelosoDaniel LevyRamachandran S VasanL Adrienne CupplesChing-Ti LiuJames B MeigsDavid S SiscovickJosée DupuisYechiel FriedlanderHagit Hochner
Published in: European journal of human genetics : EJHG (2020)
Cardiometabolic traits pose a major global public health burden. Large-scale genome-wide association studies (GWAS) have identified multiple loci accounting for up to 30% of the genetic variance in complex traits such as cardiometabolic traits. However, the contribution of parent-of-origin effects (POEs) to complex traits has been largely ignored in GWAS. Family-based studies enable the assessment of POEs in genetic association analyses. We investigated POEs on a range of complex traits in 3 family-based studies. The discovery phase was carried out in large pedigrees from the Kibbutzim Family Study (n = 901 individuals) and in 872 parent-offspring trios from the Jerusalem Perinatal Study. Focusing on imprinted genomic regions, we examined parent-specific associations with 12 complex traits (i.e., body-size, blood pressure, lipids), mostly cardiometabolic risk traits. Forty five of the 11,967 SNPs initially found to have POE were evaluated for replication (p value < 1 × 10-4) in Framingham Heart Study families (max n = 8000 individuals). Three common variants yielded evidence of POE in the meta-analysis. Two variants, located on chr6 in the HLA region, showed a paternal effect on height (rs1042136: βpaternal = -0.023, p value = 1.5 × 10-8 and rs1431403: βpaternal = -0.011, p value = 5.4 × 10-6). The corresponding maternally-derived effects were statistically nonsignificant. The variant rs9332053, located on chr13 in RCBTB2 gene, demonstrated a maternal effect on hip circumference (βmaternal = -4.24, p value = 9.6 × 10-6; βpaternal = 1.29, p value = 0.23). These findings provide evidence for the utility of incorporating POEs into association studies of cardiometabolic traits, especially anthropometric traits. The study highlights the benefits of using family-based data for deciphering the genetic architecture of complex traits.
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