Biology subjects

Gonzalez, M. E.

Publications and source records attributed to Gonzalez, M. E..

3 recordsLinked to original sources

bioRxiv2025

The shared genomic history of Middle to Late Holocene Southern Cone populations

The Southern Cone represents the southernmost region of South America to be colonized by humans. Although ancient genomes have been sequenced from southern Patagonia, genomic data from the central Southern Cone remain temporally and spatially sparse. The archaeological record of this region documents major cultural transformations during the Middle and Late Holocene, yet their relationship to demographic processes has long been debated. Here, we present genome-wide data from 52 individuals spanning the past 5,000 years, originating from four regions of the central Southern Cone in present-day Argentina and Uruguay: the central and southern Pampas, Northwest Patagonia, the Parana River Delta and Lower Uruguay River, and the eastern lowlands of Uruguay. Genomic evidence from the Pampas reveals the presence of at least three distinct ancestries during the Middle Holocene. While genetic contacts with southern Patagonian groups were sporadic, we identify the expansion of an ancestry of unknown geographic origin by 4,800 years ago, which increased substantially during the Late Holocene. This same ancestry arrived in Northwest Patagonia by at least 600 years ago, and it co-existed with individuals carrying a southern Andean genetic profile until colonial times. Genetic structure differentiates populations along the Parana River Delta and the Lower Uruguay River by approximately 1,600 years ago. In contrast, individuals from the eastern lowlands of Uruguay show genetic links with Sambaqui-associated populations from the southern coast of Brazil, suggesting the role of human dispersals in connecting tropical lowland cultural traditions. Overall, our work documents the diffusion of genetically distinct groups across all regions studied and provides compelling evidence that large-scale human movements contributed to the remarkable cultural diversity of central Southern Cone populations during the Middle and Late Holocene.

genetics↗

bioRxiv2019

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

BackgroundObesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analyses to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.\n\nMethodsWe expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify these metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.\n\nResultsWe identified 10 known metabolites that are more likely to be the causes (e.g. alpha-hydroxybutyrate) or effects (e.g. valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g. glycine). Importantly, we also identified about 5 times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.\n\nConclusionsThese findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

bioinformatics↗

bioRxiv2017

PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and metabolic quantitative trait loci. While untargeted profiling methods can measure up to thousands of metabolite signals in a single experiment, many signals cannot be readily identified as known metabolites or compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To deal with these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets using an imputation-based approach and to generate pathway annotations for these signals. We performed meta and pathway analyses for both known and unknown signals in multiple datasets and then validated the results using genetic associations. Finally, we applied the methods to detect metabolite signals and pathways associated with body mass index, demonstrating that our framework is useful for analyzing unknown signals in a robust and biologically meaningful manner and for improving the power of untargeted metabolomics studies.

bioinformatics↗