Human Genome Diversity Project Research Articles

A genotype imputation reference panel specific for native Southeast Asian populations.

We report the development of a "Southeast Asian Specific (SEA-specific) Reference Panel" through a "Cross-panel Imputation" approach, consisting of 2550 samples derived from the GA100K, SG10K, and the Peninsular Malaysia Orang Asli (OA) datasets, covering 113,851,450 variants. The SEA-specific panel produced more high confidence variants than 1000 Genomes Project (1KGP) when imputing the OA (8.9 million SEA-specific vs 8.1 million 1KGP) and the Singapore Genome Variation Project (SGVP) (12.5 million SEA-specific vs 11.8 million 1KGP) genotyping datasets. Further, the SEA-specific panel imputed SNPs with better estimated quality scores (INFO, DR2 and R2) on the OA genotyping dataset when comparing with TOPMED and the Human Genome Diversity Project, but performed similarly on SGVP dataset. This panel also exhibited higher recall and non-reference disconcordance rates, indicating the influence of ancestry closeness of the reference panel. However, we note that the imputation accuracy may be compromised by the size of the reference panel.

Local Ancestry Inference Based on Population-Specific Single-Nucleotide Polymorphisms-A Study of Admixed Populations in the 1000 Genomes Project.

Human populations have interacted throughout history, and a considerable portion of modern human populations show evidence of admixture. Local ancestry inference (LAI) is focused on detecting the genetic ancestry of chromosomal segments in admixed individuals and has wide applications. In this work, we proposed a new LAI method based on population-specific single-nucleotide polymorphisms (SNPs) and applied it in the analysis of admixed populations in the 1000 Genomes Project (1KGP). Based on population-specific SNPs in a sliding window, we computed local ancestry information vectors, which are moment estimators of local ancestral proportions, for two haplotypes of an admixed individual and inferred the local ancestral origins. Then we used African (AFR), East Asian (EAS), European (EUR) and South Asian (SAS) populations from the 1KGP and indigenous American (AMR) populations from the Human Genome Diversity Project (HGDP) as reference populations and conducted the proposed LAI analysis on African American populations and American populations in the 1KGP. The results were compared with those obtained by RFMix, G-Nomix and FLARE. We demonstrated that the existence of alleles in a chromosomal region that are specific to a particular reference population and the absence of alleles specific to the other reference populations provide reasonable evidence for determining the ancestral origin of the region. Contemporary AFR, AMR and EUR populations approximate ancestral populations of the admixed populations well, and the results from RFMix, G-Nomix and FLARE largely agree with those from the Ancestral Spectrum Analyzer (ASA), in which the proposed method was implemented. When admixtures are ancient and contemporary reference populations do not satisfactorily approximate ancestral populations, the performances of RFMix, G-Nomix and FLARE deteriorate with increased error rates and fragmented chromosomal segments. In contrast, our method provides fair results.

Genetic determinants of serum bilirubin using inferred native American gene variants in Chilean adolescents.

Gene variants in the UGT1A1 gene are strongly associated with circulating bilirubin levels in several populations, as well as other variants of modest effect across the genome. However, the effects of such variants are unknown regarding the Native American ancestry of the admixed Latino population. Our objective was to assess the Native American genetic determinants of serum bilirubin in Chilean admixed adolescents using the local ancestry deconvolution approach. We measured total serum bilirubin levels in 707 adolescents of the Chilean Growth and Obesity Cohort Study (GOCS) and performed high-density genotyping using the Illumina-MEGA array (>1.7 million genotypes). We constructed a local ancestry reference panel with participants from the 1000 Genomes Project, the Human Genome Diversity Project, and our GOCS cohort. Then, we inferred and isolated haplotype tracts of Native American, European, or African origin to perform genome-wide association studies. In the whole cohort, the rs887829 variant and others near UGT1A1 were the unique signals achieving genome-wide statistical significance (b = 0.30; p = 3.34 × 10-57). After applying deconvolution methods, we found that significance is also maintained in Native American (b = 0.35; p = 3.29 × 10-17) and European (b = 0.28; p = 1.14 × 10-23) ancestry components. The rs887829 variant explained a higher percentage of the variance of bilirubin in the Native American (37.6%) compared to European ancestry (28.4%). In Native American ancestry, carriers of the TT genotype of this variant averaged 4-fold higher bilirubinemia compared to the CC genotype (p = 2.82 × 10-12). We showed for the first time that UGT1A1 variants are the primary determinant of bilirubin levels in Native American ancestry, confirming its pan-ethnic relevance. Our study illustrates the general value of the local ancestry deconvolution approach to assessing isolated ancestry effects in admixed populations.

Abstract PO3-27-05: Racioethnic and Ancestral Differences in Risk of Cardiometabolic Conditions and Cardiovascular Disease in Women Treated for Breast Cancer: the Pathways Heart Study

Abstract Background: Despite the established literature of racioethnic disparities in cardiovascular disease (CVD) in the general population, few studies have examined such differences in women after BC treatment, who are at higher risk due to cardiotoxic cancer treatment. Moreover, few prior studies have considered genetic similarity among the participants in the analyses, which can be objectively inferred to delineate the ancestral background of an individual. Methods: The Pathways Heart Study was established to investigate the incidence of cardiometabolic risk factors and CVD events in women with a history of BC at Kaiser Permanente Northern California (KPNC). The main study endpoints for this analysis were cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events, identified by ICD and CPT codes from EHR. Prevalent conditions were identified within 3 years prior to the date of BC diagnosis, and incident conditions were from the date of BC diagnosis to December 31, 2021. Ethnicity was self-reported and classified as non-Hispanic White (NHW), non-Hispanic Black (NHB), Asian, Hispanic, other, or unknown. Global genetic ancestry was estimated to capture the proportion of continental ancestries genetically similar to the reference populations in Africa, Americas, Asia, and Europe from the 1000 Genome Project and Human Genome Diversity Project (HGDP), which, with the current lack of universally accepted labeling, are herein referred to as African, Native American, Asian, and European ancestry, respectively. Multivariable logistic and Cox proportional hazards regression models with all-cause mortality considered as competing risk were used to analyze the associations of race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 women with BC in this analysis, 2,713 (66.6%) self-reported as NHW, 305 (7.5%) as NHB, 512 (12.6) as Asian, 447 (11.0%) as Hispanic, and 94 (2.3%) as other or unknown. NHB, Asian, and Hispanic women were more like to have prevalent diabetes than NHW women, the pattern of which persisted after BC diagnosis. Adjusted OR (95% CI) of risk of incident diabetes was 1.74 (1.21-2.49) for NHB, 3.63 (2.59-5.08) for Asian, and 2.19 (1.58-3.04) for Hispanic women, as compared to NHW. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity (sHR [95% CI] per 25% increment of African ancestry 1.19 [1.07-1.34], Asian ancestry 1.57 [1.44-1.72], and Native American ancestry 1.60 [1.29-2.00]), in comparison to European ancestry. For CVD risk, NHB women were more like to develop any prevalent and incident CVD than NHW women, particularly for ischemic heart disease (incident risk sHR=1.80 [1.10-2.94]). Genetic similarity analyses also showed higher risk of ischemic heart disease associated with African ancestry (incident risk per 25% increment, sHR=1.23 [1.06-1.43]). In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease. Similarly, lower risk of these CVD conditions was associated with Native American ancestry. Conclusions: Compared with women who self-reported as NHW, those self-reported as NHB, Asian, and Hispanic were at higher risk of diabetes prior to BC diagnosis and the elevated risk persisted after BC diagnosis. Moreover, Black women had higher risk of prevalent and incident CVD, whereas Hispanic women had lower risk of CVD, the latter of which might be related to the admixture of Native American ancestry. To our knowledge, this is the largest multi-ethnic study of disparities in CVD health in women post BC treatment, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among BC survivors that may warrant more research and clinical attentions. Citation Format: Song Yao, Haiyang Sheng, Peter Fiorica, Richard Cheng, Lucas Mendicino, Angela Omilian, Qianqian Zhu, Janise Roh, Cecile Laurant, Valerie Lee, Isaac Ergas, Carlos Iribarren, Jamal Rana, Mai Nguyen-Huynh, Eileen Rillamas-Sun, Dawn Hershman, Christine Ambrosone, Lawrence Kushi, Heather Greenlee, Marilyn Kwan. Racioethnic and Ancestral Differences in Risk of Cardiometabolic Conditions and Cardiovascular Disease in Women Treated for Breast Cancer: the Pathways Heart Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-27-05.

A harmonized public resource of deeply sequenced diverse human genomes.

Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Reconstruct recent multi-population migration history by using identical-by-descent sharing

Identical-by-descent (IBD) is a fundamental genomic characteristic in population genetics and has been widely used for population history reconstruction. However, limited by the nature of IBD, which could only capture the relationship between two individuals/haplotypes, existing IBD-based history inference is constrained to two populations. In this study, we propose a framework by leveraging IBD sharing in multi-population and develop a method, MatrixIBD, to reconstruct recent multi-population migration history. Specifically, we employ the structured coalescent theory to precisely model the genealogical process and then estimate the IBD sharing across multiple populations. Within our model, we establish a theoretical connection between migration history and IBD sharing. Our method is rigorously evaluated through simulations, revealing its remarkable accuracy and robustness. Furthermore, we apply MatrixIBD to Central and South Asia in the Human Genome Diversity Project and successfully reconstruct the recent migration history of three closely related populations in South Asia. By taking into account the IBD sharing across multiple populations simultaneously, MatrixIBD enables us to attain clearer and more comprehensive insights into the history of regions characterized by complex migration dynamics, providing a holistic perspective on intricate patterns embedded within the recent population migration history.

Typological thinking in human genomics research contributes to the production and prominence of scientific racism.

Public genomic datasets like the 1000 Genomes project (1KGP), Human Genome Diversity Project (HGDP), and the Adolescent Brain Cognitive Development (ABCD) study are valuable public resources that facilitate scientific advancements in biology and enhance the scientific and economic impact of federally funded research projects. Regrettably, these datasets have often been developed and studied in ways that propagate outdated racialized and typological thinking, leading to fallacious reasoning among some readers that social and health disparities among the so-called races are due in part to innate biological differences between them. We highlight how this framing has set the stage for the racist exploitation of these datasets in two ways: First, we discuss the use of public biomedical datasets in studies that claim support for innate genetic differences in intelligence and other social outcomes between the groups identified as races. We further highlight recent instances of this which involve unauthorized access, use, and dissemination of public datasets. Second, we discuss the memification, use of simple figures meant for quick dissemination among lay audiences, of population genetic data to argue for a biological basis for purported human racial groups. We close with recommendations for scientists, to preempt the exploitation and misuse of their data, and for funding agencies, to better enforce violations of data use agreements.

Polygenic risk score comparator (PRScomp): Test population vs. worldwide populations

BackgroundPolygenic risk scores (PRS) are a powerful tool for predicting an individual's genetic risk for complex diseases. MethodsWe have developed a web service (PRScomp) as a user-friendly tool to evaluate PRS of the user own population and compare it with worldwide populations. ResultsA disease/trait database has been constructed from GWAS Catalog summary statistics. Genotype data of test population is uploaded and merged with the reference dataset (1000 Genome Project and Human Genome Diversity Project) to obtain a file including the common SNPs. The user can select a disease/trait from the database and a curated set of risk markers is used to calculate summatory PRS. Distribution of z-scored PRS values is presented in publication-ready plots and text files that can be downloaded. DiscussionPRScomp can be useful for public health decision-making by identifying population-specific genetic risk factors and informing the development of targeted interventions for at-risk populations.

Association of APOE with Alzheimer’s disease in Mexico, Caribbean‐Hispanics, Peruvian and Mexican Americans

AbstractBackgroundThe Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for Late Onset Alzheimer’s disease (LOAD). However, its genetic effect is heterogeneous across different ethnicities. We investigated the association of APOE with LOAD in five different Hispanic studies: Mexicans in the Mexican Health and Aging Study (MHAS), Caribbean Hispanics in the Washington Heights Inwood Aging project (WHICAP), Peruvians in the Genetics of Alzheimer’s disease In Peruvian Populations (GAPP) and Mexican Americans from The Health & Aging Brain among Latino Elders (HABLE) and the Texas Alzheimer’s Research and Care Consortium (TARCC).MethodA total of 7,832 participants were included in the analysis. APOE genotypes were generated by direct genotyping or imputation. SNPs associations with LOAD were assessed using multivariate logistic regression models in PLINK. Analyses were adjusted for age (≥60 years old), sex, and education. Global ancestry proportions, Native Americans, European, African were estimated with ADMIXTURE software using the Human Genome Diversity Project as the reference panel. Genetic ancestry specific secondary analyses were conducted using the same models.ResultThe genetic ancestral composition was significantly different across cohorts. Peruvians and Mexicans showed a predominant native ancestry, Caribbean‐Hispanics appeared to have a major contribution of European ancestry, and Mexican Americans exhibited similar proportions of both European and Native ancestries. We observed a significant association with e4 allele dosage in all the cohorts, except for MHAS. The genetic effect of at least one copy of the e4 allele on LOAD risk was very similar in GAPP and HABLE (OR = 3.42, SE = 0.39 and OR = 3.33, SE = 0.33, respectively), while attenuated in TARCC (OR = 2.81, SE = 0.28) and WHICAP (OR = 1.82, SE = 0.16). Among MHAS participants with a predominant European ancestry, APOE association achieved statistical significance with an increased disease risk (OR = 3.42, SE = 0.55) for homozygous carriers. Within the native‐prevalent ancestry subsample, the increased LOAD risk among APOE_ e4 heterozygous carriers were similar in both GAPP and HABLE cohorts (OR = 3.61, SE = 0.44 and OR = 3.64, SE = 0.43, respectively).ConclusionOur results showed a differential risk for LOAD from the APOE_ e4 allele across populations with admixed genetic ancestry.

Searching for sequencing signal anomalies associated with genome structural variations

Genomic structural variations (SVs) are one of the main sources of genetic diversity. Structural variants as mutagens may have a significant impact on human health and lead to hereditary diseases and cancers. Existing methods of finding structural variants are based on analysis of high-throughput sequencing data and despite significant progress in the development of the detection methods, there is still a need for improving the identification of structural variations with accuracy appropriate for use in a diagnostic procedure. Analysis of the signal of sequencing coverage (i.e., the number of sequencing fragments that aligned to every point of a genome) holds new potential for the design of approaches for structural variations discovery, and can be used as time-series analysis. Here, we present an approach for identification of patterns in the coverage signal. The method has been developed based on algorithms used for analysis of time series data, namely KNN (K-nearest neighbour) search algorithm and the SAX (Symbolic Aggregation Approximation) method. Using the rich dataset encompassing full genomes of 911 individuals with different ethnic backgrounds generated by the Human Genome Diversity Project initiative, we constructed generalized patterns of signal coverage in the vicinity of breakpoints corresponding to various structural variant types. Also, with the benefit of the SAX models of the motifs we developed a software package for fast detection of anomalies in the coverage signal.

Phenotypic Associations With the HMOX1 GT(n) Repeat in European Populations.

Heme oxygenase 1 is a key enzyme in the management of heme in humans. A GT(n) repeat length in the heme oxygenase 1 gene (HMOX1) has been widely associated with a variety of phenotypes, including susceptibility to and outcomes in diabetes, cancer, infections, and neonatal jaundice. However, studies have generally been small and results inconsistent. In this study, we imputed the GT(n) repeat length in participants from 2 UK cohort studies (the UK Biobank study (n=463,005; recruited in 2006-2010) and the Avon Longitudinal Study of Parents and Children (ALSPAC; n=937; recruited in 1990-1991)), with the reliability of imputation tested in other cohorts (1000 Genomes Project, Human Genome Diversity Project, and Personal Genome Project UK). Subsequently, we measured the relationship between repeat length and previously identified associations (diabetes, chronic obstructive pulmonary disease, pneumonia, and infection-related mortality in the UK Biobank; neonatal jaundice in ALSPAC) and performed a phenomewide association study in the UK Biobank. Despite high-quality imputation (correlation between true repeat length and imputed repeat length>0.9 in test cohorts), clinical associations were not identified in either the phenomewide association study or specific association studies. These findings were robust to definitions of repeat length and sensitivity analyses. Despite multiple smaller studies identifying associations across a variety of clinical settings, we could not replicate or identify any relevant phenotypic associations with the HMOX1 GT(n) repeat.

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program

BackgroundThe Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic changes over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). To investigate these aspects, we divided MVP participants into five birth cohorts (N-range = 123,888 [born from 1943 to 1947] to 136,699 [born from 1948 to 1953]).ResultsAncestry groups were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP + HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010 < Cohen’s d < 0.259, p < 7.80 × 10−4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept = 1.08 ± 0.042). The 1kGP + HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction = 0.045 ± 0.007, p < 0.05) confounding in the GWAS statistics.ConclusionsThis study provides a characterization of ancestry diversity of the MVP cohort over time and compares two strategies to infer genetically defined ancestry groups by assessing differences in controlling population stratification in genome-wide association studies.

Methods for Assessing Population Relationships and History Using Genomic Data.

Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.

Comprehensive Genetic Analysis of Druze Provides Insights into Carrier Screening.

Druze individuals, like many genetically homogeneous and isolated populations, harbor recurring pathogenic variants (PV) in autosomal recessive (AR) disorders. Variant calling of whole-genome sequencing (WGS) of 40 Druze from the Human Genome Diversity Project (HGDP) was performed (HGDP-cohort). Additionally, we performed whole exome sequencing (WES) of 118 Druze individuals: 38 trios and 2 couples, representing geographically distinct clans (WES-cohort). Rates of validated PV were compared with rates in worldwide and Middle Eastern populations, from the gnomAD and dbSNP datasets. Overall, 34 PVs were identified: 30 PVs in genes underlying AR disorders, 3 additional PVs were associated with autosomal dominant (AD) disorders, and 1 PV with X-linked-dominant inherited disorder in the WES cohort. The newly identified PVs associated with AR conditions should be considered for incorporation into prenatal-screening options offered to Druze individuals after an extension and validation of the results in a larger study.

An anthropologist’s point of view

The historical relationship between the cognate fields of biological anthropology and human population genetics is complicated. Of Cavalli-Sforza’s contributions to this dialogue, The Human Genome Diversity Project has had some of the most interesting cultural reverberations to the present day.

A collection of read depth profiles at structural variant breakpoints

SWaveform, a newly created open genome-wide resource for read depth signal in the vicinity of structural variant (SV) breakpoints, aims to boost development of computational tools and algorithms for discovery of genomic rearrangement events from sequencing data. SVs are a dominant force shaping genomes and substantially contributing to genetic diversity. Still, there are challenges in reliable and efficient genotyping of SVs from whole genome sequencing data, thus delaying translation into clinical applications and wasting valuable resources. SWaveform includes a database containing ~7 M of read depth profiles at SV breakpoints extracted from 911 sequencing samples generated by the Human Genome Diversity Project, generalised patterns of the signal at breakpoints, an interface for navigation and download, as well as a toolbox for local deployment with user’s data. The dataset can be of immense value to bioinformatics and engineering communities as it empowers smooth application of intelligent signal processing and machine learning techniques for discovery of genomic rearrangement events and thus opens the floodgates for development of innovative algorithms and software.

Population-based change-point detection for the identification of homozygosity islands.

This work is motivated by the problem of identifying homozygosity islands on the genome of individuals in a population. Our method directly tackles the issue of identification of the homozygosity islands at the population level, without the need of analysing single individuals and then combine the results, as is made nowadays in state-of-the-art approaches. We propose regularized offline change-point methods to detect changes in the parameters of a multidimensional distribution when we have several aligned, independent samples of fixed resolution. We present a penalized maximum likelihood approach that can be efficiently computed by a dynamic programming algorithm or approximated by a fast binary segmentation algorithm. Both estimators are shown to converge almost surely to the set of change-points without the need of specifying a priori the number of change-points. In simulation, we observed similar performances from the exact and greedy estimators. Moreover, we provide a new methodology for the selection of the regularization constant which has the advantage of being automatic, consistent, and less prone to subjective analysis. The data used in the application are from the Human Genome Diversity Project (HGDP) and is publicly available. Algorithms were implemented using the R software R Core Team (R: A Language and Environment for Statistical Computing. Vienna (Austria): R Foundation for Statistical Computing, 2020.) in the R package blockcpd, found at https://github.com/Lucas-Prates/blockcpd.

Distribution of a novel CYP2C haplotype in Native American populations.

The CYP2C19 gene, located in the CYP2C cluster, encodes the major drug metabolism enzyme CYP2C19. This gene is highly polymorphic and no-function (CYP2C19*2 and CYP2C19*3), reduced function (CYP2C19*9) and increased function (CYP2C19*17) star alleles (haplotypes) are commonly used to predict CYP2C19 metabolic phenotypes. CYP2C19*17 and the genotype-predicted rapid (RM) and ultrarapid (UM) CYP2C19 metabolic phenotypes are absent or rare in several Native American populations. However, discordance between genotype-predicted and pharmacokinetically determined CYP2C19 phenotypes in Native American cohorts have been reported. Recently, a haplotype defined by rs2860840T and rs11188059G alleles in the CYP2C cluster has been shown to encode increased rate of metabolism of the CYP2C19 substrate escitalopram, to a similar extent as CYP2C19*17. We investigated the distribution of the CYP2C:TG haplotype and explored its potential impact on CYP2C19 metabolic activity in Native American populations. The study cohorts included individuals from the One Thousand Genomes Project AMR superpopulation (1KG_AMR), the Human Genome Diversity Project (HGDP), and from indigenous populations living in Brazil (Kaingang and Guarani). The frequency range of the CYP2C:TG haplotype in the study cohorts, 0.469 to 0.598, is considerably higher than in all 1KG superpopulations (range: 0.014-to 0.340). We suggest that the high frequency of the CYP2C:TG haplotype might contribute to the reported discordance between CYP2C19-predicted and pharmacokinetically verified CYP2C19 metabolic phenotypes in Native American cohorts. However, functional studies involving genotypic correlations with pharmacokinetic parameters are warranted to ascertain the importance of the CYP2C:TG haplotype.

North and East African mitochondrial genetic variation needs further characterization towards precision medicine

IntroductionMitochondria are maternally inherited cell organelles with their own genome, and perform various functions in eukaryotic cells such as energy production and cellular homeostasis. Due to their inheritance and manifold biological roles in health and disease, mitochondrial genetics serves a dual purpose of tracing the history as well as disease susceptibility of human populations across the globe. This work requires a comprehensive catalogue of commonly observed genetic variations in the mitochondrial DNAs for all regions throughout the world. So far, however, certain regions, such as North and East Africa have been understudied. ObjectivesTo address this shortcoming, we have created the most comprehensive quality-controlled North and East African mitochondrial data set to date and use it for characterizing mitochondrial genetic variation in this region. MethodsWe compiled 11 published cohorts with novel data for mitochondrial genomes from 159 Sudanese individuals. We combined these 641 mitochondrial sequences with sequences from the 1000 Genomes (n = 2504) and the Human Genome Diversity Project (n = 828) and used the tool haplocheck for extensive quality control and detection of in-sample contamination, as well as Nanopore long read sequencing for haplogroup validation of 18 samples. ResultsUsing a subset of high-coverage mitochondrial sequences, we predict 15 potentially novel haplogroups in North and East African subjects and observe likely phylogenetic deviations from the established PhyloTree reference for haplogroups L0a1 and L2a1. ConclusionOur findings demonstrate common hitherto unexplored variants in mitochondrial genomes of North and East Africa that lead to novel phylogenetic relationships between haplogroups present in these regions. These observations call for further in-depth population genetic studies in that region to enable the prospective use of mitochondrial genetic variation for precision medicine.