Genetic variants that associate with cirrhosis have pleiotropic effects on human traits

Background and AimsCirrhosis is characterized by extensive fibrosis of the liver and is a major cause of liver‐related mortality. Cirrhosis is partially heritable but genetic contributions to cirrhosis have not been systemically explored. Here, we carry out association analyses with cirrhosis in two large biobanks and determine the effects of cirrhosis associated variants on multiple human disease/traits.MethodsWe carried out a genome‐wide association analysis of cirrhosis as a diagnosis in UK BioBank (UKBB; 1088 cases vs. 407 873 controls) and then tested top‐associating loci for replication with cirrhosis in a hospital‐based cohort from the Michigan Genomics Initiative (MGI; 875 cases of cirrhosis vs. 30 346 controls). For replicating variants or variants previously associated with cirrhosis that also affected cirrhosis in UKBB or MGI, we determined single nucleotide polymorphism effects on all other diagnoses in UKBB (PheWAS), common metabolic traits/diseases and serum/plasma metabolites.ResultsUnbiased genome‐wide association study identified variants in/near PNPLA3 and HFE, and candidate variant analysis identified variants in/near TM6SF2, MBOAT7, SERPINA1, HSD17B13, STAT4 and IFNL4 that reproducibly affected cirrhosis. Most affected liver enzyme concentrations and/or aspartate transaminase‐to‐platelet ratio index. PheWAS, metabolic trait and serum/plasma metabolite association analyses revealed effects of these variants on lipid, inflammatory and other processes including new effects on many human diseases and traits.ConclusionsWe identified eight loci that reproducibly associate with population‐based cirrhosis and define their diverse effects on human diseases and traits.See Editorial on Page 281Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/1/liv14321_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/2/liv14321.pd

Body Composition and Genetic Lipodystrophy Risk Score Associate With Nonalcoholic Fatty Liver Disease and Liver Fibrosis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150618/1/hep41391.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150618/2/hep41391_am.pd

17‐Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/1/hep30350.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/2/hep30350_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/3/hep30350-sup-0001-Supinfo.pd

Insulin Resistance Exacerbates Genetic Predisposition to Nonalcoholic Fatty Liver Disease in Individuals Without Diabetes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/1/hep41353.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/2/hep41353_am.pd

A new framework for host-pathogen interaction research

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed

Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects.

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course

Data from: A comparison of supermatrix and supertree methods for multilocus phylogenetics using organismal datasets

It has been proposed that supertree approaches should be applied to large multilocus sequence datasets to achieve computational tractability. Large datasets such as those derived from phylogenomics studies can be broken into many locus-specific tree searches and the resulting trees can be stitched together via a supertree method. Using simulated data, workers have reported that they can rapidly construct a supertree that is comparable to the results of heuristic tree search on the entire dataset. To test this assertion with organismal data, we compared tree length under the parsimony criterion and computational time for twenty multilocus datasets using supertree (SuperFine and SuperTriplets) and supermatrix (heuristic search in TNT) approaches. Tree length and computational times were compared among methods using the Wilcoxon matched-pairs signed rank test. Supermatrix searches produce significantly shorter trees than either supertree approach (SuperFine or SuperTriplets; p 0.4, not significant). In conclusion, we show by using real rather than simulated data, that there is no basis, either in time tractability or tree length, for use of supertrees over heuristic tree search using a supermatrix for phylogenomics