Differences In Genetic Ancestry Research Articles

Fine-scale sampling uncovers the complexity of migrations in 5th–6th century Pannonia

As the collapse of the Western Roman Empire accelerated during the 4th and 5th centuries, arriving "barbarian" groups began to establish new communities in the border provinces of the declining (and eventually former) empire. This was a time of significant cultural and political change throughout not only these border regions but Europe as a whole.1,2 To better understand post-Roman community formation in one of these key frontier zones after the collapse of the Hunnic movement, we generated new paleogenomic data for a set of 38 burials from a time series of three 5th century cemeteries3,4,5 at Lake Balaton, Hungary. We utilized a comprehensive sampling approach to characterize these cemeteries along with data from 38 additional burials from a previously published mid-6th century site6 and analyzed them alongside data from over 550 penecontemporaneous individuals.7,8,9,10,11,12,13,14,15,16,17,18,19 The range of genetic diversity in all four of these local burial communities is extensive and wider ranging than penecontemporaneous Europeans sequenced to date. Despite many commonalities in burial customs and demography, we find that there were substantial differences in genetic ancestry between the sites. We detect evidence of northern European gene flow into the Lake Balaton region. Additionally, we observe a statistically significant association between dress artifacts and genetic ancestry among 5th century genetically female burials. Our analysis shows that the formation of early Medieval communities was a multifarious process even at a local level, consisting of genetically heterogeneous groups.

Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health.

Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays. Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only 0.6% in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than 10% of the AIMs. Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina's Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations.

Genetic ancestry differences in tumor mutation in early and average-onset colorectal cancer.

3620 Background: The incidence and mortality of early onset colorectal cancer (EOCRC), defined as CRC diagnosed prior to age 50, are rising, in contrast to declining rates of average onset CRC (AOCRC). Epidemiologic trends for CRC appear to differ by race/ethnicity, which could be related in part to underlying differences in tumor mutation and gene expression. Previous research has used self-reported race/ethnicity categories, which has been shown to be missing or inaccurate, particularly in highly admixed groups such as Black and Hispanic patients. Hence, we examined tumor profiles of EOCRC and AOCRC using genetic ancestry. Methods: Genetic ancestry was inferred from de-identified data of 1,443 and 3,315 patients with EOCRC and AOCRC, respectively, who underwent tumor profiling with the Tempus xT NGS 648-gene panel and targeted RNAseq. Ancestry informative markers were used to estimate likelihoods for the five continental groups defined in the 1000 Genomes Project: Africa (AFR), Americas (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS). We assessed the association of genetic ancestry proportions (per 10% increase in each ancestry proportion) with presence of protein altering somatic mutations in key driver genes in CRC. We also assessed interactions between onset age (EOCRC vs. AOCRC) and genetic ancestry in somatic mutations. Results: Across all ages, African ancestry was associated with higher odds of mutations in APC and KRAS and lower odds of BRAF mutations (Table). East Asian ancestry was associated with higher odds of mutations in NTRK3 and TP53. For EOCRC, the association between KRAS and African ancestry remained significant (OR 1.07, p=0.04). Furthermore, there was an interaction (p=0.031) between onset age and race/ethnicity such that the association between African ancestry and APC mutation was significant in AOCRC (OR=1.09) but not EOCRC (OR=1.00). Conclusions: The use of genetic ancestry instead of categorical race/ethnicity provides a more quantitative and precise profile of shared genetic background that can underlie biological race differences in CRC cancer etiology and outcomes. [Table: see text]

Mediating Role of Socio-Environmental Factors in the Relationship between Genetic Ancestry Differences and Pediatric Asthma Readmission

Genetic ancestry studies have predominately looked for direct biologic effects on disease. This study sought to determine whether asthma-related readmissions differed by African ancestry and the extent to which such an association would be mediated by socioenvironmental factors.

Genetic ancestry differences in pediatric asthma readmission are mediated by socioenvironmental factors

Social and financial hardships, combined with disease managment and environmental factors explain approximately 80% of the observed disparity in asthma-related readmissions between Black and White children. We sought to determine whether asthma-related readmissions differed by degree of African ancestry and the extent to which such an association would also be explained by socioenvironmental risk factors. This study used data from a prospective cohort study of 695 Black and White children aged 1 to 16 years with an asthma-related admission. The primary outcome was a similar readmission within 12 months. Each subject's African ancestry was determined by single nucleotide polymorphisms on a continuous scale ranging from 0 to 1 (0= no African ancestry; 1= 100% African ancestry). We also assessed 37 social, environmental, and clinical variables that we clustered into 6 domains (for example, hardship, disease management). Survival and mediation analyses were conducted. A total of 134 children (19.3%) were readmitted within 12 months. Higher African ancestry was associated with asthma readmission (odds ratio 1.11, 95% confidence interval 1.05-1.18 for every 10% increase in African ancestry) with adjustment for age and gender. The association between African ancestry and readmission was mediated by hardship (sβ= 3.42, P< .001) and disease management (sβ= 0.046, P= .001), accounting for >50% of African ancestry's effect on readmission. African ancestry was no longer significantly associated with readmission (sβ= 0.035, P= .388) after accounting for these mediators. African ancestry was strongly associated with readmission, and the association was mediated by family hardship and disease management. These results are consistent with the notion that asthma-related racial disparities are driven by factors like structural racism and social adversity.

Abstract PL01-03: Ancestry and cancer disparities: Genomic sequencing in diverse populations

Abstract Over the past decade, genomic advancements have afforded tremendous opportunities in cancer research; including characterization of gene expression networks that distinguish tumor types, and the resolution of genomic structure that drives tumor growth and treatment outcomes. Despite these advances, disparities in cancer outcomes persist as does our paucity in data to understand the breadth of diversity in tumor traits across populations. Most of the data that defines our current understanding of cancer genetics was generated within the context of mainly European ancestry. This bias is partially due to geographic convenience of recruitment within the proximity of the institutions that generate these data elements. This type of systemic bias has perpetuated disparities of already marginalized racial minority populations, compared to their European race-group counterparts. However, international consortia have led to new insights and access, with strategic recruitment and study designs that harness population genetic diversity. Germline genomic investigations have uncovered ancestral susceptibilities that are either due to prevalence of ancestry-specific risk alleles or ancestrally/evolutionarily conserved modifiers of molecular pathways that prime development of certain tumor subtypes. Tumor/somatic genomics have repeatedly uncovered gene signatures that reflect thematic differences in tumor immune microenvironment as well as copy number variations, which are more recently being investigated with high-resolution technologies in imaging and genomic architecture, linked to genetic ancestry. As we turn our lens to the genomic context of population diversity, we are realizing novel opportunities to develop transformative precision medicine applications. Specifically, for this plenary presentation, we will highlight the transition of GWAS studies to functionalize genetic ancestry differences, enhanced with inclusion of ancestral populations, as opposed to the traditional filtration of these features. We will overview the newest concepts of complex germline and somatic architecture in the context of disparities and how these are determining the mysterious function of genomic associations identified as population-private risk across multiple ethnicities; including Asian, Latin American and African ancestry clades. We will demonstrate the utility of Whole Genome Sequencing in the clinical genetics space to mitigate disparities by characterizing the pathological features (Genomic Phenotypes) of the insidious Variables of Unknown Significance (VUS) and how these findings can fast-track minority access to alternative treatments. These data will ultimately improve our interpretations of overall cancer risk and risk of recurrence test results and recalibrate risk models. Lastly, we give a call to action that resounds the importance of including not only the genetic history (ancestry) but also the social history (self-reported race) of cancer patients and how this multidisciplinary approach will unlock our understanding of the drivers of aggressive tumor biology and be a catalyst for implementation of customized prevention and survivorship interventions. Only with the “Power of Inclusion” can we hope to traverse the breadth of disparities and make leaps of progress in cancer research and treatment. Citation Format: Melissa B. Davis. Ancestry and cancer disparities: Genomic sequencing in diverse populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr PL01-03.

Influence of genetic ancestry and socioeconomic status on type 2 diabetes in the diverse Colombian populations of Choc\xf3 and Antioquia

Differences in genetic ancestry and socioeconomic status (SES) among Latin American populations have been linked to health disparities for a number of complex diseases, such as diabetes. We used a population genomic approach to investigate the role that genetic ancestry and socioeconomic status (SES) play in the epidemiology of type 2 diabetes (T2D) for two Colombian populations: Chocó (Afro-Latino) and Antioquia (Mestizo). Chocó has significantly higher predicted genetic risk for T2D compared to Antioquia, and the elevated predicted risk for T2D in Chocó is correlated with higher African ancestry. Despite its elevated predicted genetic risk, the population of Chocó has a three-times lower observed T2D prevalence than Antioquia, indicating that environmental factors better explain differences in T2D outcomes for Colombia. Chocó has substantially lower SES than Antioquia, suggesting that low SES in Chocó serves as a protective factor against T2D. The combination of lower prevalence of T2D and lower SES in Chocó may seem surprising given the protective nature of elevated SES in many populations in developed countries. However, low SES has also been documented to be a protective factor in rural populations in less developed countries, and this appears to be the case when comparing Chocó to Antioquia.

Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure

BackgroundAccurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies.MethodsIn this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships.ResultsWe demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations.ConclusionsOur results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

Evaluation of inflammation-related genes polymorphisms in Mexican with Alzheimer's disease: a pilot study.

Amyloid peptide is able to promote the activation of microglia and astrocytes in Alzheimer’s disease (AD), and this stimulates the production of pro-inflammatory cytokines. Inflammation contributes to the process of neurodegeneration and therefore is a key factor in the development of AD. Some of the most important proteins involved in AD inflammation are: clusterin (CLU), complement receptor 1 (CR1), C reactive protein (CRP), tumor necrosis factor α (TNF-α), the interleukins 1α (IL-1α), 6 (IL-6), 10 (IL-10) and cyclooxygenase 2 (COX-2). In particular, COX-2 is encoded by the prostaglandin-endoperoxide synthase 2 gene (PTGS2). Since variations in the genes that encode these proteins may modify gene expression or function, it is important to investigate whether these variations may change the developing AD. The aim of this study was to determine whether the presence of polymorphisms in the genes encoding the aforementioned proteins is associated in Mexican patients with AD. Fourteen polymorphisms were genotyped in 96 subjects with AD and 100 controls; the differences in allele, genotype and haplotype frequencies were analyzed. Additionally, an ancestry analysis was conducted to exclude differences in genetic ancestry among groups as a confounding factor in the study. Significant differences in frequencies between AD and controls were found for the single-nucleotide polymorphism (SNP) rs20417 within the PTGS2 gene. Ancestry analysis revealed no significant differences in the ancestry of the compared groups, and the association was significant even after adjustment for ancestry and correction for multiple testing, which strengthens the validity of the results. We conclude that this polymorphism plays an important role in the development of the AD pathology and further studies are required, including their proteins.

Response to Comment on: Maruthur et al. Does Genetic Ancestry Explain Higher Values of Glycated Hemoglobin in African Americans? Diabetes 2011;60:2434–2438

We appreciate the interest of Dagogo-Jack (1) in our study of genetic ancestry and HbA1c in African Americans (2). We interpret his major concerns as follows: 1 ) HbA1c values may have a glucose-independent genetic component and 2 ) in our study, adjustment for glucose did not fully account for HbA1c values ≥6.5%. Our goal was to evaluate if differences in genetic ancestry might explain differences in HbA1c between individuals of …

Understanding the Hispanic/Latino Patient

The Hispanic/Latino population is the largest minority group in the United States, representing approximately 16% of the population in 2010. The US Census Bureau defines Hispanic/Latino origin as ethnicity, which tends to be associated with culture and is distinct from race. Based on the US Census Bureau classifications, Hispanics/Latinos have at least 3 main racial backgrounds (white, black, and Native Indian), with the combination and proportion differing among Hispanic/Latino subgroups. The reflection of these racial differences in genetic ancestry partly explains why biological characteristics differ among Hispanic/Latino subgroups. Partly as a result of variations in biological characteristics, the risk of type 2 diabetes mellitus varies among Hispanic/Latino subgroups. According to data from the 1982 to 1984 Hispanic Health and Nutrition Examination Survey (HHANES), the prevalence of diagnosed and undiagnosed type 2 diabetes among adults aged 45 to 74 years was higher in Mexican Americans (23.9%) and Puerto Ricans (26.1%) compared with Cubans (15.8%). In addition to genetics, there are multiple social and cultural factors that affect the prevalence and course of type 2 diabetes in Hispanic/Latino individuals. Although certain aspects of Hispanic/Latino culture may become barriers in the management of type 2 diabetes in this population, these cultural characteristics may also represent an opportunity for prevention and/or improvement of care. It is important for healthcare providers to have an understanding and appreciation of Hispanic/Latino culture in order to provide their Hispanic/Latino patients with healthcare that is culturally and socially appropriate. Only by considering genetic, social, and cultural factors can type 2 diabetes be successfully prevented, treated, and managed in Hispanic/Latino patients.

Matching on Race and Ethnicity in Case-Control Studies as a Means of Control for Population Stratification

Some investigators argue that controlling for self-reported race or ethnicity, either in statistical analysis or in study design, is sufficient to mitigate unwanted influence from population stratification. In this report, we evaluated the effectiveness of a study design involving matching on self-reported ethnicity and race in minimizing bias due to population stratification within an ethnically admixed population in California. We estimated individual genetic ancestry using structured association methods and a panel of ancestry informative markers, and observed no statistically significant difference in distribution of genetic ancestry between cases and controls (P=0.46). Stratification by Hispanic ethnicity showed similar results. We evaluated potential confounding by genetic ancestry after adjustment for race and ethnicity for 1260 candidate gene SNPs, and found no major impact (>10%) on risk estimates. In conclusion, we found no evidence of confounding of genetic risk estimates by population substructure using this matched design. Our study provides strong evidence supporting the race- and ethnicity-matched case-control study design as an effective approach to minimizing systematic bias due to differences in genetic ancestry between cases and controls.

Application of principal component analysis to pharmacogenomic studies in Canada

Ethnicity can confound results in pharmacogenomic studies. Allele frequencies of loci that influence drug metabolism can vary substantially between different ethnicities and underlying ancestral genetic differences can lead to spurious findings in pharmacogenomic association studies. We evaluated the application of principal component analysis (PCA) in a pharmacogenomic study in Canada to detect and correct for genetic ancestry differences using genotype data from 2094 loci in 220 key drug biotransformation genes. Using 89 Coriell worldwide reference samples, we observed a strong correlation between principal component values and geographic origin. We further applied PCA to accurately infer the genetic ancestry in our ethnically diverse Canadian cohort of 524 patients from the GATC study of severe adverse drug reactions. We show that PCA can be successfully applied in pharmacogenomic studies using a limited set of markers to detect underlying differences in genetic ancestry thereby maximizing power and minimizing false-positive findings.

Genetic Ancestry and Risk Factors for Breast Cancer among Latinas in the San Francisco Bay Area

Genetic association studies using case-control designs are susceptible to false-positive and false-negative results if there are differences in genetic ancestry between cases and controls. We measured genetic ancestry among Latinas in a population-based case-control study of breast cancer and tested the association between ancestry and known breast cancer risk factors. We reasoned that if genetic ancestry is associated with known breast cancer risk factors, then the results of genetic association studies would be confounded. We used 44 ancestry informative markers to estimate individuals' genetic ancestry in 563 Latina participants. To test whether ancestry is a predictor of hormone therapy use, parity, and body mass index (BMI), we used multivariate logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (95% CI) associated with a 25% increase in Indigenous American ancestry, adjusting for age, education, and the participant's and grandparents' place of birth. Hormone therapy use was significantly less common among women with higher Indigenous American ancestry (OR, 0.78; 95% CI, 0.63-0.96). Higher Indigenous American ancestry was also significantly associated with overweight (BMI, 25-29.9 versus <25) and obesity (BMI, > or =30 versus <25), but only among foreign-born Latina women (OR, 3.44; 95% CI, 1.97-5.99 and OR, 1.95; 95% CI, 1.24-3.06, respectively). Some breast cancer risk factors are associated with genetic ancestry among Latinas in the San Francisco Bay Area. Therefore, case-control genetic association studies for breast cancer should directly measure genetic ancestry to avoid potential confounding.