Identification of blood pressure genes in the Dahl salt-sensitive hypertension model

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Reconstructing native American migrations from whole-genome and whole-exome data.

There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern American ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations

Reconstructing Native American Migrations from Whole-Genome and Whole-Exome Data

There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern America ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

Heterogeneity in Genetic Admixture across Different Regions of Argentina

The population of Argentina is the result of the intermixing between several groups, including Indigenous American, European and African populations. Despite the commonly held idea that the population of Argentina is of mostly European origin, multiple studies have shown that this process of admixture had an impact in the entire Argentine population. In the present study we characterized the distribution of Indigenous American, European and African ancestry among individuals from different regions of Argentina and evaluated the level of discrepancy between self-reported grandparental origin and genetic ancestry estimates. A set of 99 autosomal ancestry informative markers (AIMs) was genotyped in a sample of 441 Argentine individuals to estimate genetic ancestry. We used non-parametric tests to evaluate statistical significance. The average ancestry for the Argentine sample overall was 65% European (95%CI: 63–68%), 31% Indigenous American (28–33%) and 4% African (3–4%). We observed statistically significant differences in European ancestry across Argentine regions [Buenos Aires province (BA) 76%, 95%CI: 73–79%; Northeast (NEA) 54%, 95%CI: 49–58%; Northwest (NWA) 33%, 95%CI: 21–41%; South 54%, 95%CI: 49–59%; p<0.0001] as well as between the capital and immediate suburbs of Buenos Aires city compared to more distant suburbs [80% (95%CI: 75–86%) versus 68% (95%CI: 58–77%), p = 0.01]. European ancestry among individuals that declared all grandparents born in Europe was 91% (95%CI: 88–94%) compared to 54% (95%CI: 51–57%) among those with no European grandparents (p<0.001). Our results demonstrate the range of variation in genetic ancestry among Argentine individuals from different regions in the country, highlighting the importance of taking this variation into account in genetic association and admixture mapping studies in this population

Cancer health disparities in racial/ethnic minorities in the United States

There are well-established disparities in cancer incidence and outcomes by race/ethnicity that result from the interplay between structural, socioeconomic, socio-environmental, behavioural and biological factors. However, large research studies designed to investigate factors contributing to cancer aetiology and progression have mainly focused on populations of European origin. The limitations in clinicopathological and genetic data, as well as the reduced availability of biospecimens from diverse populations, contribute to the knowledge gap and have the potential to widen cancer health disparities. In this review, we summarise reported disparities and associated factors in the United States of America (USA) for the most common cancers (breast, prostate, lung and colon), and for a subset of other cancers that highlight the complexity of disparities (gastric, liver, pancreas and leukaemia). We focus on populations commonly identified and referred to as racial/ethnic minorities in the USA—African Americans/Blacks, American Indians and Alaska Natives, Asians, Native Hawaiians/other Pacific Islanders and Hispanics/Latinos. We conclude that even though substantial progress has been made in understanding the factors underlying cancer health disparities, marked inequities persist. Additional efforts are needed to include participants from diverse populations in the research of cancer aetiology, biology and treatment. Furthermore, to eliminate cancer health disparities, it will be necessary to facilitate access to, and utilisation of, health services to all individuals, and to address structural inequities, including racism, that disproportionally affect racial/ethnic minorities in the USA.Fil: Zavala, Valentina A.. University of California; Estados UnidosFil: Bracci, Paige M.. University of California; Estados UnidosFil: Carethers, John M.. University of Michigan; Estados UnidosFil: Carvajal Carmona, Luis. University of California at Davis; Estados UnidosFil: Coggins, Nicole B.. University of California at Davis; Estados UnidosFil: Cruz Correa, Marcia R.. Universidad de Puerto Rico; Puerto RicoFil: Davis, Melissa. No especifíca;Fil: de Smith, Adam J.. University of California; Estados UnidosFil: Dutil, Julie. Ponce Research Institute; Puerto RicoFil: Figueiredo, Jane C.. Cedars Sinai Medical Center; Estados UnidosFil: Fox, Rena. University of California; Estados UnidosFil: Graves, Kristi D.. University Of Georgetown; Estados UnidosFil: Gomez, Scarlett Lin. University of California; Estados UnidosFil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Neuhausen, Susan L.. No especifíca;Fil: Newman, Lisa. No especifíca;Fil: Nguyen, Tung. University of California; Estados UnidosFil: Palmer, Julie R.. National Institutes of Health; Estados UnidosFil: Palmer, Nynikka R.. University of California; Estados UnidosFil: Pérez Stable, Eliseo J.. National Institutes of Health; Estados UnidosFil: Piawah, Sorbarikor. University of California; Estados UnidosFil: Rodriquez, Erik J.. National Institutes of Health; Estados UnidosFil: Sanabria Salas, María Carolina. Instituto Nacional de Cancerología; ColombiaFil: Schmit, Stephanie L.. University of Southern California; Estados UnidosFil: Serrano Gomez, Silvia J.. Instituto Nacional de Cancerología; ColombiaFil: Stern, Mariana Carla. University of Southern California; Estados UnidosFil: Weitzel, Jeffrey. No especifíca;Fil: Yang, Jun J.. St. Jude Children’s Research Hospital; Estados UnidosFil: Zabaleta, Jovanny. No especifíca;Fil: Ziv, Elad. University of California; Estados UnidosFil: Fejerman, Laura. University of California; Estados Unido

Association between Ancestry-Specific 6q25 Variants and Breast Cancer Subtypes in Peruvian Women

Background: Breast cancer incidence in the United States is lower in Hispanic/Latina (H/L) compared with African American/ Black or Non-Hispanic White women. An Indigenous American breast cancer-protective germline variant (rs140068132) has been reported near the estrogen receptor 1 gene. This study tests the association of rs140068132 and other polymorphisms in the 6q25 region with subtype-specific breast cancer risk in H/Ls of high Indigenous American ancestry. Methods: Genotypes were obtained for 5,094 Peruvian women with (1,755) and without (3,337) breast cancer. Associations between genotype and overall and subtype-specific risk for the protective variant were tested using logistic regression models and conditional analyses, including other risk-associated polymorphisms in the region. Results: We replicated the reported association between rs140068132 and breast cancer risk overall [odds ratio (OR), 0.53; 95% confidence interval (CI), 0.47-0.59], as well as the lower odds of developing hormone receptor negative (HR-) versus HR+ disease (OR, 0.77; 95% CI, 0.61-0.97). Models, including HER2, showed further heterogeneity with reduced odds for HR+HER2+ (OR, 0.68; 95% CI, 0.51-0.92), HR-HER2+ (OR, 0.63; 95% CI, 0.44-0.90) and HR-HER2- (OR, 0.77; 95% CI, 0.56-1.05) compared with HR+HER2-. Inclusion of other risk-associated variants did not change these observations. Conclusions: The rs140068132 polymorphism is associated with decreased risk of breast cancer in Peruvians and is more protective against HR- and HER2+ diseases independently of other breast cancer-associated variants in the 6q25 region. Impact: These results could inform functional analyses to understand the mechanism by which rs140068132-G reduces risk of breast cancer development in a subtype-specific manner. They also illustrate the importance of including diverse individuals in genetic studies.National Institutes of HealthRevisión por pare

Reconstructing Native American Migrations from Whole-Genome and Whole-Exome Data

There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern America ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors’ response in non-small cell lung cancer: a multicenter cohort study

BackgroundRecent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.MethodsRadiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6).ResultsThe best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59.ConclusionWe demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC