Ancestry Estimation Research Articles

Association of genetic ancestry with pre-eclampsia in multi-ethnic cohorts of pregnant women

ObjectivesMaternal self-reported ethnicity is recognised as a risk factor for pre-eclampsia in clinical screening tools and models. This study investigated whether ethnicity is acting as a proxy for genetic variants in this context. Study designA total of 436 women from multi-ethnic backgrounds recruited to two UK observational pregnancy hypertension cohort studies were genotyped. Genetically-computed individual ancestry estimates were calculated for each individual through comparison to the multi-ethnic 1000 Genomes reference panel genotypes. Regression models for pre-eclampsia using clinical risk factors including self-reported ethnicity with and without ancestry estimates were built and compared using Likelihood Ratio Tests (LRT). Main outcome measuresPre-eclampsia (early- and late-onset). ResultsIn these multi-ethnic cohorts (mean age 34.9 years; 41.3 % White, 34.2 % Black, 13.1 % Asian ethnic backgrounds; 82.6 % chronic hypertension), discrepancies between self-reported ethnicity and genetically-computed individual ancestry estimates were present in all ethnic groups, particularly minority groups. Genetically-computed pan-African ancestry percentage was associated with early-onset (< 34 weeks) pre-eclampsia in adjusted models (aOR 100 % vs 0 % African ancestry: 3.81, 95 % CI 1.04–14.14, p-value 0.044) independently of self-reported ethnicity and established clinical risk factors. Addition of genetically-computed African ancestry to a clinical risk factor model including self-reported ethnicity, improved model fit (Likelihood ratio test p-value 0.023). ConclusionsSelf-reported maternal ethnicity is an imperfect proxy for genetically-computed individual ancestry estimates, particularly in ethnic minority groups. Genetically-computed African ancestry percentage was associated with early-onset pre-eclampsia independently of self-reported maternal ethnicity. Well-powered studies in multi-ethnic cohorts are required to delineate the genetic contribution to pre-eclampsia.

Abstract C102: Polyethnic-1000: Advancing cancer genomics by studying ethnically diverse, underserved patient populations in New York

Abstract Prompted by the considerable biases toward European ancestry cases in large cancer genomics repositories, the New York Genome Center initiated an effort to diversify the public datasets that are forming the foundations of current cancer research, in order to optimize the unbiased applications of modern genomically-driven discoveries to a larger population. Our effort was focused on 8 cancer types (breast, prostate, bladder, lung, pancreatic, colon and endometrial cancer, as well as multiple myeloma) and generated Illumina tumor-normal whole-genome sequencing and tumor transcriptome sequencing. The majority of the samples were retrospectively obtained in clinics and hospitals of the New York region and the analyses are conducted with collaborators in these institutions. Our dataset is currently composed of 851 tumor-normal WGS and 471 RNASeq, and is progressively being distributed to the scientific community in partnership with ICGC-ARGO. Overall, 75% of the cohort is predominantly of non-European ancestry, as estimated by genetic ancestry analysis using the 1000Genomes continental references.We analyzed the samples with NYGC’s somatic variant calling pipeline and will present results from the 8 cancer types related to germline and somatic variants (single-nucleotide variants, indels, copy-number alterations, complex structural variants) as well as mutational signatures, neoantigen prediction, fusion genes and expression profiles, in relation to genetic ancestry estimation and clinical features of the patients. We anticipate that the dataset will significantly diversify the genetic ancestry of tumor profiles in public databases, and serve to form hypotheses regarding the contribution of ancestry on cancer disparities. The experience of the Polyethnic-1000 project informs our contribution to the Cancer Grand Challenge SAMBAI, which focuses on breast, prostate and pancreatic cancers in the African Diaspora. SAMBAI will further investigate the causes of cancer disparity by combining genomic data with social determinants of health and exposomics information, deeper immune profiling and new genomics technologies such as long reads sequencing and the use of a pangenome reference. Citation Format: Nicolas Robine, Lara Winterkorn, Tim Chu, Zoe Goldstein, Will Hooper, Ali Oku, Heather M. Geiger, Melissa B. Davis, Onyinye Balogun. Polyethnic-1000: Advancing cancer genomics by studying ethnically diverse, underserved patient populations in New York [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C102.

Dense SNP-based analyses complement forensic anthropology biogeographical ancestry assessments

Identification of unidentified human remains (UHRs) is crucial yet challenging, especially with traditional forensic techniques. Forensic anthropological examinations can yield ancestry estimations; however, the utility of these estimates is limited by the data points that can be collected from partial remains, complexities of admixture, and variation of phenotypic expression due to environmental effects. While it is generally known that anthropological estimates can be imprecise, the performance of these methods has not been studied at scale. Genome-wide SNP testing is an orthogonal approach for estimating ancestry and offers a unique opportunity to measure the magnitude of anthropological ancestry misattribution. Genomic ancestry inference leverages principal component analysis (PCA) and model-based clustering approaches. This study compares anthropologically determined ancestry with those estimated using genome-wide SNP markers. A dataset of 611 UHR samples with publicly available ancestry assessments from National Missing and Unidentified Persons System (NamUs) was analyzed. The genetic ancestry approach, validated against reference population samples, offers robust ancestry calculations for major population groups. Inconsistency between anthropological and genomic ancestry assignments were observed, particularly for admixed populations. Although forensic anthropological examinations remain valuable, their limitations emphasize the need for refinement and enhancement through the augmentation of SNP-based analyses. Further validation studies are crucial to define the uncertainty associated with both anthropological and genome-based ancestry estimates to resolve cases and aid law enforcement investigations. Additionally, current policy and practices for reporting ancestry for UHRs should be revisited to reduce potential misinformation.

Associations Between Preoperative Shortness of Breath and Potassium Channels Gene Variations in Women With Breast Cancer.

Shortness of breath is a common symptom in patients with cancer. However, the mechanisms that underlie this troublesome symptom are poorly understood. Therefore, this study aimed to determine the prevalence of and associated risk factors for shortness of breath in women prior to breast cancer surgery and identify associations between shortness of breath and polymorphisms for potassium channel genes. Patients were recruited prior to breast cancer surgery and completed a self-report questionnaire on the occurrence of shortness of breath. Genotyping of single nucleotides polymorphism (SNPs) in potassium channel genes was performed using a custom array. Multiple logistic regression analyses were done to identify associations between the occurrence of shortness of breath and SNPs in ten candidate genes. Of the 398 patients, 11.1% reported shortness of breath. These patients had a lower annual household income, a higher comorbidity burden, and a lower functional status. After controlling for functional status, comorbidity burden, genomic estimates of ancestry and self-reported race and ethnicity, the genetic associations that remained significant in the multiple regression analyses were for potassium voltage-gated channel subfamily D (KCND2) rs12673992, potassium voltage-gated channel modifier subfamily S (KCNS1) rs4499491, and potassium two pore channel subfamily K (KCNK2) rs4411107. While these findings warrant replication, they suggest that alterations in potassium channel function may contribute to the occurrence of shortness of breath in women prior to breast cancer surgery.

Genetic Signatures of Positive Selection in Human Populations Adapted to High Altitude in Papua New Guinea.

Papua New Guinea (PNG) hosts distinct environments mainly represented by the ecoregions of the Highlands and Lowlands that display increased altitude and a predominance of pathogens, respectively. Since its initial peopling approximately 50,000 years ago, inhabitants of these ecoregions might have differentially adapted to the environmental pressures exerted by each of them. However, the genetic basis of adaptation in populations from these areas remains understudied. Here, we investigated signals of positive selection in 62 highlanders and 43 lowlanders across 14 locations in the main island of PNG using whole-genome genotype data from the Oceanian Genome Variation Project (OGVP) and searched for signals of positive selection through population differentiation and haplotype-based selection scans. Additionally, we performed archaic ancestry estimation to detect selection signals in highlanders within introgressed regions of the genome. Among highland populations we identified candidate genes representing known biomarkers for mountain sickness (SAA4, SAA1, PRDX1, LDHA) as well as candidate genes of the Notch signaling pathway (PSEN1, NUMB, RBPJ, MAML3), a novel proposed pathway for high altitude adaptation in multiple organisms. We also identified candidate genes involved in oxidative stress, inflammation, and angiogenesis, processes inducible by hypoxia, as well as in components of the eye lens and the immune response. In contrast, candidate genes in the lowlands are mainly related to the immune response (HLA-DQB1, HLA-DQA2, TAAR6, TAAR9, TAAR8, RNASE4, RNASE6, ANG). Moreover, we find two candidate regions to be also enriched with archaic introgressed segments, suggesting that archaic admixture has played a role in the local adaptation of PNG populations.

Recurrent gene flow between Neanderthals and modern humans over the past 200,000 years.

Although it is well known that the ancestors of modern humans and Neanderthals admixed, the effects of gene flow on the Neanderthal genome are not well understood. We develop methods to estimate the amount of human-introgressed sequences in Neanderthals and apply it to whole-genome sequence data from 2000 modern humans and three Neanderthals. We estimate that Neanderthals have 2.5 to 3.7% human ancestry, and we leverage human-introgressed sequences in Neanderthals to revise estimates of Neanderthal ancestry in modern humans, show that Neanderthal population sizes were significantly smaller than previously estimated, and identify two distinct waves of modern human gene flow into Neanderthals. Our data provide insights into the genetic legacy of recurrent gene flow between modern humans and Neanderthals.

Global and local ancestry estimation in a captive baboon colony.

The last couple of decades have highlighted the importance of studying hybridization, particularly among primate species, as it allows us to better understand our own evolutionary trajectory. Here, we report on genetic ancestry estimates using dense, full genome data from 881 olive (Papio anubus), yellow (Papio cynocephalus), or olive-yellow crossed captive baboons from the Southwest National Primate Research Center. We calculated global and local ancestry information, imputed low coverage genomes (n = 830) to improve marker quality, and updated the genetic resources of baboons available to assist future studies. We found evidence of historical admixture in some putatively purebred animals and identified errors within the Southwest National Primate Research Center pedigree. We also compared the outputs between two different phasing and imputation pipelines along with two different global ancestry estimation software. There was good agreement between the global ancestry estimation software, with R2 > 0.88, while evidence of phase switch errors increased depending on what phasing and imputation pipeline was used. We also generated updated genetic maps and created a concise set of ancestry informative markers (n = 1,747) to accurately obtain global ancestry estimates.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

The Polygenic Score Catalog: new functionality and tools to enable FAIR research.

Polygenic scores (PGS) have transformed human genetic research and have multiple potential clinical applications, including risk stratification for disease prevention and prediction of treatment response. Here, we present a series of recent enhancements to the PGS Catalog (www.PGSCatalog.org), the largest findable, accessible, interoperable, and reusable (FAIR) repository of PGS. These include expansions in data content and ancestral diversity as well as the addition of new features. We further present the PGS Catalog Calculator (pgsc_calc, https://github.com/PGScatalog/pgsc_calc), an open-source, scalable and portable pipeline to reproducibly calculate PGS that securely democratizes equitable PGS applications by implementing genetic ancestry estimation and score normalization using reference data. With the PGS Catalog & calculator users can now quantify an individual's genetic predisposition for hundreds of common diseases and clinically relevant traits. Taken together, these updates and tools facilitate the next generation of PGS, thus lowering barriers to the clinical studies necessary to identify where PGS may be integrated into clinical practice.

Sex and ancestry variation in ulna morphology in an adult South African cadaveric sample

BackgroundSex and/or ancestry estimations based on skeletal elements are vital in forensics, as these variables are key to identification of unknown skeletal remains. Unfortunately, patterns of skeletal variation are often shared between sex and ancestry groups, making independent estimation of such variables less accurate, especially when substantial size differences exist both within and between groups. Geometric morphometric analysis allows isolation of the size component of variation, enabling independent and more sensitive detection of shape variation between groups. This creates the potential for more accurate estimations of sex and ancestry either independently or simultaneously, thus reducing the chances of compounding errors of estimation. This would be especially beneficial in heterogeneous populations, such as that of South Africa, where group separation may be affected by complicated genetic and environmental influences. MethodsThis study assessed sex and ancestry variation in morphology of 1894 ulnae of South African males and females of the country's three largest ancestry groups. Three-dimensional data was submitted to Generalized Procrustes Analysis for superimposition and scaling to a common centroid size. Mean centroid sizes and shapes were compared, and accuracy of sex, ancestry, or sex-ancestry estimation was assessed using Discriminant Function Analysis and leave-one-out cross-validation. Covariation with size, age and year-of-birth were assessed through regression analysis. ResultsMale ulnae were absolutely and proportionally larger than female ulnae, while Black individuals were similarly larger than Colored and White individuals. Based on this variation, sex could be estimated with 68.8 % accuracy, and ancestry with 73.6 % accuracy. Simultaneous sex-ancestry assessment showed similar morphological patterning and yielded a mean classification accuracy of 73.6 %. ConclusionsThese results have practical value for forensic application, where relatively poorly elements such as the ulna are often all that is available for analysis. Additionally, simultaneous estimation of sex and ancestry reduces compounding errors that may arise from first estimating one variable and basing the rest of the biological profile estimations thereon. Such improved estimations are of great potential value, especially in heterogeneous populations such as that of South Africa.

Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers.

Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.

How admixed captive breeding populations could be rescued using local ancestry information.

This paper asks the question: can genomic information be used to recover a species that is already on the pathway to extinction due to genetic swamping from a related and more numerous population? We show that a breeding strategy in a captive breeding program can use whole genome sequencing to identify and remove segments of DNA introgressed through hybridisation. The proposed policy uses a generalized measure of kinship or heterozygosity accounting for local ancestry, that is, whether a specific genetic location was inherited from the target of conservation. We then show that optimizing these measures would minimize undesired ancestry while also controlling kinship and/or heterozygosity, in a simulated breeding population. The process is applied to real data representing the hybridized Scottish wildcat breeding population, with the result that it should be possible to breed out domestic cat ancestry. The ability to reverse introgression is a powerful tool brought about through the combination of sequencing with computational advances in ancestry estimation. Since it works best when applied early in the process, important decisions need to be made about which genetically distinct populations should benefit from it and which should be left to reform into a single population.

Classification of Malaysian individuals using Fordisc 3 based on four craniofacial measurements

ABSTRACT Fordisc is a discriminant function computer program used to study ancestry, although it lacks representation from a Malaysian sample. Our aim was to evaluate ancestry estimates of Fordisc 3 with Malaysian crania of known sex and ethnicity. A dataset from Hisham and Ibrahim of 300 individuals was included in this study. They had undergone full-body computed tomographic scanning prior to post-mortem examination with scan resolutions of 1.0 mm. Maximum cranial length, maximum cranial breadth, nasal height, nasal breadth, and midorbital width were obtained from these images. Measurements were fed into the Fordisc 3 program and were analysed using the Forensic Anthropology Data Bank (FDB) and Howells’ reference populations according to sex, however midorbital width was excluded in all analyses. Ancestry assessment was evaluated on the basis of percentage of correctly classified individuals. Based on Howells database, 50.67% of the sample are correctly classified into Asian, in contrast to only 32.33% according to FDB. These classification trends are limited by the number of measurements, reference populations available in Fordisc and population history of the sample itself. We need to collect standard anthropological measurements in future research and potentially contribute to the database in Fordisc to improve its performance.

Abstract 6121: Racial differences in the intratumoral immune cell infiltrate in luminal breast cancer

Abstract Background: Black women have a higher rate of breast cancer mortality compared with their non-Hispanic White (NHW) counterparts, with an 80% higher likelihood of breast cancer death from axillary node-negative, ER-positive (a.k.a. luminal) tumors (Hoskins et al, JAMA Oncol 2021). Black women are 30% more likely to have ER-positive tumors with a high-risk gene expression profile, indicating a role for tumor biology in this disparity (Hoskins et al, JAMA Oncol 2023). The underlying biological mechanisms responsible are unknown. The aim of this study was to investigate the immune cell infiltrate in ER-positive breast tumors as a potential driver of disproportionately aggressive luminal tumor biology among Black women. Methods: Gene expression data from ER-positive tumors diagnosed in women identified as Black or NHW in The Cancer Genome Atlas (TCGA) were deconvoluted with the CIBERSORT tool to estimate the relative intratumoral proportion of 22 immune cell types within each tumor. Differential analysis of imputed immune cell fractions comparing race was conducted with the Mann-Whitney test. P-values were adjusted for multiple comparisons with the false discovery rate (FDR) method. Linear regression of previously reported ancestry estimates for breast cancer patients in TCGA (Huo, et al, JAMA Oncol 2017) was used to test for association between African ancestry and proportion of regulatory T cells (Tregs) in tumors from Black women. Results: CIBERSORT analysis of tumors found a significantly increased proportion of Tregs in tumors from Black (n=86) than NHW (n=534) patients, with log2 fold-change= 0.74 (FDR-corrected p-value= 0.01). Plasma cells, T follicular helper cells and M0 macrophages were increased in tumors from Black women with uncorrected p-values &amp;lt; 0.05, but racial differences in these cell types were not significant after FDR correction (p &amp;gt;0.1). Ancestry analysis among Black women indicated that African ancestry was not associated with the intratumoral proportion of Tregs (1 SD change in proportion African ancestry was associated with 0.06 standard deviation increase in Tregs, p &amp;gt; 0.3). Confirmation of findings with a racially diverse breast tumor TMA from an independent cohort is ongoing and results will be presented. Conclusions: Increased pro-tumorigenic Treg infiltrate in ER-positive breast tumors from Black women may contribute to a disproportionately aggressive tumor phenotype and racial survival disparity. The findings could have therapeutic implications and suggest that immune checkpoint inhibitors may have increased activity against luminal breast cancer in Black patients. Although this study did not show a statistically significant effect for genetic admixture within African Americans, larger studies are needed to distinguish the contribution of ancestry vs. social context, environmental, and behavioral factors as the driver of this racial difference in tumor biology. Citation Format: Neha Hippalgaonkar, George Chlipala, Dezheng Huo, Garth Rauscher, Naiche Adler, Jan Kitajewski, Kent Hoskins. Racial differences in the intratumoral immune cell infiltrate in luminal breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6121.

Biological Identification of Skulls in Indonesian and Thai Populations: Ancestry Estimation, Sex Determination, Stature Estimation, and Age Estimation

Biological Identification of Skulls in Indonesian and Thai Populations: Ancestry Estimation, Sex Determination, Stature Estimation, and Age Estimation

Estimation of ancestry from cranial measurements based on MDCT data acquired in a Japanese and Western Australian population

The estimation of ancestry is important not only towards establishing identity but also as a required precursor to facilitating the accurate estimation of other attributes such as sex, age at death, and stature. The present study aims to analyze morphological variation in the crania of Japanese and Western Australian individuals and test predictive models based on machine learning for their potential forensic application. The Japanese and Western Australian samples comprise computed tomography (CT) scans of 230 (111 female; 119 male) and 225 adult individuals (112 female; 113 male), respectively. A total of 18 measurements were calculated, and machine learning methods (random forest modeling, RFM; support vector machine, SVM) were used to classify ancestry. The two-way unisex model achieved an overall accuracy of 93.2% for RFM and 97.1% for SVM, respectively. The four-way sex and ancestry model demonstrated an overall classification accuracy of 84.0% for RFM and 93.0% for SVM. The sex-specific models were most accurate in the female samples (♀ 95.1% for RFM and 100% for SVM; ♂91.4% for RFM and 97.4% for SVM). Our findings suggest that cranial measurements acquired in CT images can be used to accurately classify Japanese and Western Australian individuals into their respective population. This is the first study to assess the feasibility of ancestry estimation using three-dimensional CT images of the skull.

Abstract C004: Assessing local ancestry inference using different sequencing assays and depth of coverage

Abstract Introduction: Several studies have reported differences in mutational frequencies between different ancestral populations. For example, PTEN alterations and TMPRSS2-ERG fusions are less common in men with African ancestry. However, these studies have primarily relied on measurements of "global ancestry" and have not been able to ascertain which genomic regions contribute to the observed differences in mutational frequencies. Recent advancements in computational algorithms now enable the estimation of “local ancestry” whereby individual chromosomal segments can be assigned to a particular ancestral background. These tools have been primarily developed for deep whole-genome sequencing (WGS) and have not been optimized for different sequencing assays including ultra-low pass WGS (ULP-WGS), whole exome sequencing (WES), and SNP genotyping arrays. The goal of our study is to establish best practices for local ancestry inference across a diverse set of sequencing and genotyping assays. Methods: We procured WGS data from four ancestries through the 1000 Genomes Project (n=1,067) for local ancestry inference. ULP-WGS and WES (with on- and off-target coverage) were simulated from the full WGS data. Pseudo-array data was emulated by restricting to SNP sites on Affymetrix Genome-Wide Human SNP array 6.0. Each genotype was imputed by Eagle-Beagle (EB) or GLIMPSE2 (GL2), and local ancestry inference was performed using G-nomix using window sizes spanning from 0.2 to 16.0 centimorgans (cM). Accuracy was assessed by comparing local ancestry calls for each assay in a training set (n=1007) to a validation set (n=62). Results: Local ancestry estimation in ULP-WGS data achieved an accuracy exceeding 98% when utilizing GL2, even with the smallest window size of 0.2 cM and a minimum sequencing depth of 0.1x. To achieve similar accuracy of 98% in ULP-WGS with EB, a window size of 1.5 cM was needed for sequencing depths from 2 to 0.5x, with a larger window size of 4 cM required for a depth of 0.25x. Interestingly, such accuracy did not obtain at a depth of 0.1x. For WES with on-target sites and pseudo-array data, we utilized EB for imputation as it was more computationally efficient and accurate with larger window sizes compared to GL2. The accuracy of WES with on-target sites exceeded 90% utilizing 4.0 cM or larger window sizes, but no window size achieved 98% accuracy. Incorporating off-target regions in WES and employing GL2 enhanced the accuracy to 98% at a window size of 1.5 cM. Pseudo-array data with EB achieved an accuracy of 98% at 0.5 cM and thus was better than WES. Conclusions: Our results show that local ancestry can be accurately estimated in different sequencing and genotyping assays when the window size and imputation tool are appropriately selected. This work will facilitate the use of local ancestry inference in studies utilizing different genotyping assays. Ultimately, researchers understand the influence of local ancestral haplotypes on molecular features of cancer. Citation Format: Tomoki Motegi, Joshua D. Campbell. Assessing local ancestry inference using different sequencing assays and depth of coverage [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C004.

Relationship between genetic ancestry and metabolic syndrome in community-dwelling old adults

Genetic ancestry may contribute to ethnic differences in the risk of metabolic disorders. Aging leads to a worse ability for homeostasis maintenance, favoring the establishment of metabolic disorders. PurposeThis study aimed to evaluate the relationship between the degree of genetic ancestry (European, African, and Amerindian) with the Metabolic Syndrome (MetS) diagnosis and its diagnostic components separately, in community-dwelling old adults. One hundred and sixty-one community-dwelling old adults volunteered in this study. Sociodemographic data and health history were recorded. Venous blood samples were withdrawal for biochemical analysis and DNA extraction aiming to obtain genetic ancestry estimates (Amerindian [AME], European [EUR], and African [AFR]), which was done from 12 loci. MetS diagnosis followed the NCEP-ATPIII criteria. Additionally, the sample was stratified according to the presence or absence of each criterion used for MetS diagnosis (i.e., Type 2 diabetes mellitus (T2DM), hypertension, hypertriglyceridemia, dyslipidemia [low HDL], and central obesity (elevated waist circumference)). Comparisons of genetic ancestry estimates were performed using the Mann-Whitney test, with the significance level set at p < 0.05. The prevalence of MetS was 40.4%. The degree AME, EUR and AFR genetic ancestry was not different between volunteers with or without MetS (p > 0.05). However, AME ancestry was significantly higher among diabetic volunteers (non-diabetics: 13.7% (6.3–35.8) x Diabetics: 26.1% (10.6–48.5); p < 0.05). Community-dwelling old adults with a higher percentage of Amerindian ancestry seem to be prone to T2DM diagnosis.

Ancestry estimation in forensic anthropology: accuracy of the AncesTrees software in a Brazilian sample.

Ancestry estimation methods are seldom validated in Brazil.AncesTrees performed poorly on our sample, with a maximum accuracy of 70%.Brazil's highly mixed population hinders ancestry estimation.Mixed individuals (pardos) are predominantly classified as Europeans.The insertion of Brazilian metric data into the AncesTrees database would produce better results.

Abstract 15734: Local Admixture Mapping of Metabolites in African Americans

Introduction: There is increasing evidence that metabolite levels in African Americans (AA) are influenced by population differentiated genetic variation. We investigated how local genetic ancestry may be associated with levels of circulating plasma metabolites using metabolomic profiling in the Jackson Heart Study, an AA cohort, in an attempt to identify additional genetic determinants of metabolite levels. Hypothesis: Certain metabolites will be associated with local African genetic ancestry. Methods: We performed admixture mapping using local ancestry estimates (probabilities of whether an individual has 0, 1 or 2 alleles of African ancestry at each site in the genome from RFMix, based on similarity to 1000G reference panel) from TOPMed sequencing data for autosomal SNPs. Associations were then tested for 302 targeted metabolites measured using LC-MS. Each local ancestry regression model was adjusted for age, sex, and estimated global ancestry. We used a previously established significance threshold for local African ancestry analysis, p&lt;2.1X10 -5 . We further adjusted findings for previously reported GWAS variants in conditional analysis. Results: There were 38 local admixture mapping signals for 32 metabolites. Fourteen metabolite signals survived conditional analysis and represent signals distinct from those identified in single variant GWAS. Metabolites of note include the branched chain amino acids, whose association with cardiometabolic disease has been shown to differ based on self-identified race. Conclusions: We present local admixture mapping results providing further evidence that population differentiated genetic variants influence circulating levels of plasma metabolites. For a large proportion of our findings, admixture associations were not attenuated by conditioning on previously reported SNPs from GWAS studies. These analyses suggest that admixture mapping may identify association signals that are missed by GWAS, and may help to elucidate population differences affecting key biologic pathways.