Haley R Eidem1, Kriston L McGary1, John A Capra2, Patrick Abbot1, Antonis Rokas3. 1. Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA. 2. Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37235, USA. 3. Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37235, USA. Electronic address: antonis.rokas@vanderbilt.edu.
Abstract
BACKGROUND: Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies. METHODS: We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy. RESULTS: We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions. CONCLUSION: We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy.
BACKGROUND: Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies. METHODS: We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy. RESULTS: We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions. CONCLUSION: We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy.
Authors: Abigail L LaBella; Abin Abraham; Yakov Pichkar; Sarah L Fong; Ge Zhang; Louis J Muglia; Patrick Abbot; Antonis Rokas; John A Capra Journal: Nat Commun Date: 2020-07-24 Impact factor: 14.919
Authors: Marina Sirota; Cristel G Thomas; Rebecca Liu; Maya Zuhl; Payal Banerjee; Ronald J Wong; Cecele C Quaintance; Rita Leite; Jessica Chubiz; Rebecca Anderson; Joanne Chappell; Mara Kim; William Grobman; Ge Zhang; Antonis Rokas; Sarah K England; Samuel Parry; Gary M Shaw; Joe Leigh Simpson; Elizabeth Thomson; Atul J Butte Journal: Sci Data Date: 2018-11-06 Impact factor: 6.444
Authors: Alison G Paquette; Oksana Shynlova; Xiaogang Wu; Mark Kibschull; Kai Wang; Nathan D Price; Stephen J Lye Journal: J Cell Mol Med Date: 2019-07-24 Impact factor: 5.310
Authors: Haley R Eidem; Jacob L Steenwyk; Jennifer H Wisecaver; John A Capra; Patrick Abbot; Antonis Rokas Journal: BMC Med Genomics Date: 2018-11-19 Impact factor: 3.063