A human genome diversity cell line panel

Enriching iPSC research diversity: Harnessing human biobank collections for improved ethnic representation

Biobanks of human biosamples and cell lines are indispensable for biomedical research on human health and disease and for drug development projects. Many human cell line biobanks worldwide hold collections of lymphoblastoid cell lines (LCLs), representing thousands of affected and control donors from diverse ethnic/ancestry groups. In recent years, induced human pluripotent stem cells (iPSCs) and differentiated human cells derived from these iPSCs have become indispensable for applied biomedical research. Establishing iPSCs remains a laborious and costly step towards generating differentiated human cells. To address this research need, several non-profit and commercial biobanks have established iPSC collections for distribution to researchers, thereby serving as a resource for generating differentiated human cells. The most common starting materials for generation of iPSCs are a skin biopsy for harvesting fibroblasts, or a blood sample for collection of peripheral blood mononuclear cells. However untapped resources include the large established collections of biobanked human LCLs which can be reprogrammed to iPSCs using a variety of published protocols including the use of non-integrating episomal vectors. Many biobanks curate LCLs from diverse ethnic/ancestry populations, an aspect largely absent in most established iPSC biobanks which tend to primarily reflect populations from developed countries. Here, we call upon researchers across the breadth of iPSC research to tap the unique resource of existing and diverse human LCL collections for establishing biobanked iPSC panels that better represent the varied human ethnic (and hence genomic) diversity, thereby benefiting precision medicine and drug development research on a global scale.

Extended genome-wide association study employing the African genome resources panel identifies novel susceptibility loci for Alzheimer's disease in individuals of African ancestry

INTRODUCTION

Despite a two-fold risk, individuals of African ancestry have been underrepresented in Alzheimer's disease (AD) genomics efforts.

METHODS

Genome-wide association studies (GWAS) of 2,903 AD cases and 6,265 controls of African ancestry. Within-dataset results were meta-analyzed, followed by functional genomics analyses.

RESULTS

A novel AD-risk locus was identified in MPDZ on chromosome (chr) 9p23 (rs141610415, MAF = 0.002, P = 3.68×10-9). Two additional novel common and nine rare loci were identified with suggestive associations (P < 9×10-7). Comparison of association and linkage disequilibrium (LD) patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 (ASCL1), suggesting that this association is modulated by regional origin of local African ancestry.

DISCUSSION

These analyses identified novel AD-associated loci in individuals of African ancestry and suggest that degree of African ancestry modulates some associations. Increased sample sets covering as much African genetic diversity as possible will be critical to identify additional loci and deconvolute local genetic ancestry effects.

HIGHLIGHTS

Genetic ancestry significantly impacts risk of Alzheimer's Disease (AD). Although individuals of African ancestry are twice as likely to develop AD, they are vastly underrepresented in AD genomics studies. The Alzheimer's Disease Genetics Consortium has previously identified 16 common and rare genetic loci associated with AD in African American individuals. The current analyses significantly expand this effort by increasing the sample size and extending ancestral diversity by including populations from continental Africa. Single variant meta-analysis identified a novel genome-wide significant AD-risk locus in individuals of African ancestry at the MPDZ gene, and 11 additional novel loci with suggestive genome-wide significance at P < 9×10-7. Comparison of African American datasets with samples of higher degree of African ancestry demonstrated differing patterns of association and linkage disequilibrium at one of these loci, suggesting that degree and/or geographic origin of African ancestry modulates the effect at this locus. These findings illustrate the importance of increasing number and ancestral diversity of African ancestry samples in AD genomics studies to fully disentangle the genetic architecture underlying AD, and yield more effective ancestry-informed genetic screening tools and therapeutic interventions.

Tracing the fine-scale demographic history and recent admixture in Hmong-Mien speakers

The linguistic, historical, and subsistent uniqueness of Hmong-Mien (HM) speakers offers a wonderful opportunity to investigate how these factors impact the genetic structure. The genetic differentiation among HM speakers and their population history are not well characterized. Here, we generate genome-wide data from 65 Yao ethnicity samples and analyze them with published data, particularly by leveraging haplotype-based methods. We determined that the fine-scale genetic substructure of HM speakers corresponds better with linguistic classification than with geography. Particularly, parallels between serial founder events and language differentiations can be observed in West Hmongic speakers. Multiple lines of evidence indicate that ~500-year-old GaoHuaHua individuals are most closely related to West Hmongic-speaking Bunu. The strong genetic bottleneck of some HM-speaking groups, especially Bunu, could potentially be associated with their long-term practice of swidden agriculture to some degree. The inferred admixture dates for most of the HM speakers overlap with the reign of the Ming dynasty (1368-1644 CE). Besides a common genetic origin for HM speakers, their genetic ancestry is shared primarily with neighboring Han Chinese and Tai-Kadai speakers in south China. In conclusion, our analyses reveal that recent isolation and admixture events have contributed to the genetic population history of present-day HM speakers.

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis

BACKGROUND

While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic score for BMI was associated with weight loss effectiveness and aimed to identify common genetic variants associated with weight loss.

METHODS

Participants in the ONTIME study (n = 1210) followed a uniform, multimodal behavioral weight-loss intervention. We first tested associations between a genome-wide polygenic score for higher BMI and weight loss effectiveness (total weight loss, rate of weight loss, and attrition). We then conducted a genome-wide association study (GWAS) for weight loss in the ONTIME study and performed the largest weight loss meta-analysis with earlier studies (n = 3056). Lastly, we ran exploratory GWAS in the ONTIME study for other weight loss outcomes and related factors.

RESULTS

We found that each standard deviation increment in the polygenic score was associated with a decrease in the rate of weight loss (Beta (95% CI) = -0.04 kg per week (-0.06, -0.01); P = 3.7 × 10-03) and with higher attrition after adjusting by treatment duration. No associations reached genome-wide significance in meta-analysis with previous GWAS studies for weight loss. However, associations in the ONTIME study showed effects consistent with published studies for rs545936 (MIR486/NKX6.3/ANK1), a previously noted weight loss locus. In the meta-analysis, each copy of the minor A allele was associated with 0.12 (0.03) kg/m2 higher BMI at week five of treatment (P = 3.9 × 10-06). In the ONTIME study, we also identified two genome-wide significant (P < 5×10-08) loci for the rate of weight loss near genes implicated in lipolysis, body weight, and metabolic regulation: rs146905606 near NFIP1/SPRY4/FGF1; and rs151313458 near LSAMP.

CONCLUSION

Our findings are expected to help in developing personalized weight loss approaches based on genetics.

CLINICAL TRIAL REGISTRATION

Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME; clinicaltrials.gov: NCT02829619) study.

A geographic history of human genetic ancestry

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multifaceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank

Sleep is a complex behavior regulated by genetic and environmental factors, and is known to influence health outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on diseases has yet to be fully elucidated. Using the Mass General Brigham Biobank, we aimed to examine the association of multidimensional sleep with health outcomes and investigate whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health outcomes. First, we generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms for sleep health and constructed a Sleep Lifestyle Index using data from self-reported sleep questions and electronic health records; second, we performed phenome-wide association analyses between these indexes and clinical phenotypes; and third, we analyzed the interaction between the indexes on prevalent mental health outcomes. Fifteen thousand eight hundred and eighty-four participants were included in the analysis (mean age 54.4; 58.6% female). The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (β=0.050, 95%CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10-5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10-5). No interactions were found between the indexes on prevalent mental health outcomes. These findings suggest that favorable sleep behaviors and genetic predisposition to healthy sleep may independently be protective of disease outcomes. This work provides novel insights into the role of multidimensional sleep on population health and highlights the need to develop prevention strategies focused on healthy sleep habits.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

18th International HLA and Immunogenetics Workshop: Report on the SNP-HLA Reference Consortium (SHLARC) component

The SNP-HLA Reference Consortium (SHLARC), a component of the 18th International HLA and Immunogenetics Workshop, is aimed at collecting diverse and extensive human leukocyte antigen (HLA) data to create custom reference panels and enhance HLA imputation techniques. Genome-wide association studies (GWAS) have significantly contributed to identifying genetic associations with various diseases. The HLA genomic region has emerged as the top locus in GWAS, particularly in immune-related disorders. However, the limited information provided by single nucleotide polymorphisms (SNPs), the hallmark of GWAS, poses challenges, especially in the HLA region, where strong linkage disequilibrium (LD) spans several megabases. HLA imputation techniques have been developed using statistical inference in response to these challenges. These techniques enable the prediction of HLA alleles from genotyped GWAS SNPs. Here we present the SHLARC activities, a collaborative effort to create extensive, and multi-ethnic reference panels to enhance HLA imputation accuracy.

Evaluation of population structure inferred by principal component analysis or the admixture model

Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome-wide human data, we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.

On generalized latent factor modeling and inference for high-dimensional binomial data

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two-step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene-environment association study.

High purity and integrity RNA from human cell lines stored in liquid nitrogen for over 20 years

Biobanks are a key resource for obtaining human cell lines for biomedical research, including for drug development projects. Such projects often include comparative RNA-sequencing of large panels of human cell lines from individuals affected by certain disorders and healthy controls, or from individuals with different drug response phenotypes. RNA extractions are typically done from growing cell cultures, a process that may take several weeks. However, maintaining large numbers of cell lines in parallel increases the project workload. Here, we show that extracting RNAs directly from frozen vials of human cell lines stored for over 20 years in a liquid nitrogen freezer yields RNAs with the high purity and integrity parameters that conform to those required for optimal RNA-sequencing and are closely similar to those obtained for RNAs extracted from growing human cell lines.

Extended genome-wide association study employing the African Genome Resources Panel identifies novel susceptibility loci for Alzheimer's Disease in individuals of African ancestry

INTRODUCTION

Despite a two-fold increased risk, individuals of African ancestry have been significantly underrepresented in Alzheimer's Disease (AD) genomics efforts.

METHODS

GWAS of 2,903 AD cases and 6,265 cognitive controls of African ancestry. Within-dataset results were meta-analyzed, followed by gene-based and pathway analyses, and analysis of RNAseq and whole-genome sequencing data.

RESULTS

A novel AD risk locus was identified in MPDZ on chromosome 9p23 (rs141610415, MAF=.002, P =3.68×10 -9 ). Two additional novel common and nine novel rare loci approached genome-wide significance at P <9×10 -7 . Comparison of association and LD patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 ( ASCL1 ), suggesting that the association is modulated by regional origin of local African ancestry.

DISCUSSION

Increased sample sizes and sample sets from Africa covering as much African genetic diversity as possible will be critical to identify additional disease-associated loci and improve deconvolution of local genetic ancestry effects.

The genomic history of the indigenous people of the Canary Islands

The indigenous population of the Canary Islands, which colonized the archipelago around the 3rd century CE, provides both a window into the past of North Africa and a unique model to explore the effects of insularity. We generate genome-wide data from 40 individuals from the seven islands, dated between the 3rd-16rd centuries CE. Along with components already present in Moroccan Neolithic populations, the Canarian natives show signatures related to Bronze Age expansions in Eurasia and trans-Saharan migrations. The lack of gene flow between islands and constant or decreasing effective population sizes suggest that populations were isolated. While some island populations maintained relatively high genetic diversity, with the only detected bottleneck coinciding with the colonization time, other islands with fewer natural resources show the effects of insularity and isolation. Finally, consistent genetic differentiation between eastern and western islands points to a more complex colonization process than previously thought.

The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research

The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

Admixture mapping of peripheral artery disease in a Dominican population reveals a putative risk locus on 2q35

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have a significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors; however, recent work suggests prevalence may differ between sub-groups. Here, we examined a large cohort of diverse adults in the BioMe biobank in New York City. We observed the prevalence of PAD at 1.7% in EAs vs. 8.5% and 9.4% in AAs and HLs, respectively, and among HL sub-groups, the prevalence was found at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs [OR = 3.15 (95% CI 2.33-4.25), p < 6.44 × 10-14]. To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry and PAD in Dominican BioMe participants (N = 1,813) separately from European, African, and Native American (NAT) continental ancestry tracts. The top association with PAD was an NAT ancestry tract at chromosome 2q35 [OR = 1.96 (SE = 0.16), p < 2.75 × 10-05) with 22.6% vs. 12.9% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. Efforts to reproduce the signal in other Hispanic cohorts were unsuccessful. In summary, we showed how leveraging health system data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a putative risk locus in a Dominican population.

Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations

Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown. Here, we conduct genome-wide analysis of AD in multiplex CH families from the Alzheimer Disease Sequencing Project (ADSP). We developed, validated, and applied an implementation of a logistic mixed model for admixture mapping with binary traits that leverages genetic ancestry to identify ancestry-of-origin loci contributing to AD. We identified three loci on chromosome 13q33.3 associated with reduced risk of AD, where associations were driven by Native American (NAM) ancestry. This AD admixture mapping signal spans the FAM155A, ABHD13, TNFSF13B, LIG4, and MYO16 genes and was supported by evidence for association in an independent sample from the Alzheimer's Genetics in Argentina-Alzheimer Argentina consortium (AGA-ALZAR) study with considerable NAM ancestry. We also provide evidence of NAM haplotypes and key variants within 13q33.3 that segregate with AD in the ADSP whole-genome sequencing data. Interestingly, the widely used genome-wide association study approach failed to identify associations in this region. Our findings underscore the potential of leveraging genetic ancestry diversity in recently admixed populations to improve genetic mapping, in this case for AD-relevant loci.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

Characterization of large-scale genomic differences in the first complete human genome

BACKGROUND

The first telomere-to-telomere (T2T) human genome assembly (T2T-CHM13) release is a milestone in human genomics. The T2T-CHM13 genome assembly extends our understanding of telomeres, centromeres, segmental duplication, and other complex regions. The current human genome reference (GRCh38) has been widely used in various human genomic studies. However, the large-scale genomic differences between these two important genome assemblies are not characterized in detail yet.

RESULTS

Here, in addition to the previously reported "non-syntenic" regions, we find 67 additional large-scale discrepant regions and precisely categorize them into four structural types with a newly developed website tool called SynPlotter. The discrepant regions (~ 21.6 Mbp) excluding telomeric and centromeric regions are highly structurally polymorphic in humans, where the deletions or duplications are likely associated with various human diseases, such as immune and neurodevelopmental disorders. The analyses of a newly identified discrepant region-the KLRC gene cluster-show that the depletion of KLRC2 by a single-deletion event is associated with natural killer cell differentiation in ~ 20% of humans. Meanwhile, the rapid amino acid replacements observed within KLRC3 are probably a result of natural selection in primate evolution.

CONCLUSION

Our study provides a foundation for understanding the large-scale structural genomic differences between the two crucial human reference genomes, and is thereby important for future human genomics studies.

Human papillomavirus integration transforms chromatin to drive oncogenesis

BACKGROUND

Human papillomavirus (HPV) drives almost all cervical cancers and up to 70% of head and neck cancers. Frequent integration into the host genome occurs predominantly in tumorigenic types of HPV. We hypothesize that changes in chromatin state at the location of integration can result in changes in gene expression that contribute to the tumorigenicity of HPV.

RESULTS

We find that viral integration events often occur along with changes in chromatin state and expression of genes near the integration site. We investigate whether introduction of new transcription factor binding sites due to HPV integration could invoke these changes. Some regions within the HPV genome, particularly the position of a conserved CTCF binding site, show enriched chromatin accessibility signal. ChIP-seq reveals that the conserved CTCF binding site within the HPV genome binds CTCF in 4 HPV^+ cancer cell lines. Significant changes in CTCF binding pattern and increases in chromatin accessibility occur exclusively within 100 kbp of HPV integration sites. The chromatin changes co-occur with out-sized changes in transcription and alternative splicing of local genes. Analysis of The Cancer Genome Atlas (TCGA) HPV⁺ tumors indicates that HPV integration upregulates genes which have significantly higher essentiality scores compared to randomly selected upregulated genes from the same tumors.

CONCLUSIONS

Our results suggest that introduction of a new CTCF binding site due to HPV integration reorganizes chromatin state and upregulates genes essential for tumor viability in some HPV⁺ tumors. These findings emphasize a newly recognized role of HPV integration in oncogenesis.

Admixture Mapping of Peripheral Artery Disease in a Dominican Population Reveals a Novel Risk Locus on 2q35

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors, however recent work suggests prevalence may differ between sub-groups. Here we examined a large cohort of diverse adults in the Bio Me biobank in New York City (NYC). We observed the prevalence of PAD at 1.7% in EAs vs 8.5% and 9.4% in AAs and HLs, respectively; and among HL sub-groups, at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs (OR=3.15 (95% CI 2.33-4.25), P <6.44×10 -14 ). To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry (LA) and PAD in Dominican Bio Me participants (N=1,940) separately for European (EUR), African (AFR) and Native American (NAT) continental ancestry tracts. We identified a NAT ancestry tract at chromosome 2q35 that was significantly associated with PAD (OR=2.05 (95% CI 1.51-2.78), P <4.06×10 -6 ) with 22.5% vs 12.5% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607 , a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. In summary, we showed how leveraging health systems data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a novel risk locus in a Dominican population.

Identification of Sjögren's syndrome patient subgroups by clustering of labial salivary gland DNA methylation profiles

Heterogeneity in Sjögren's syndrome (SS), increasingly called Sjögren's disease, suggests the presence of disease subtypes, which poses a major challenge for the diagnosis, management, and treatment of this autoimmune disorder. Previous work distinguished patient subgroups based on clinical symptoms, but it is not clear to what extent symptoms reflect underlying pathobiology. The purpose of this study was to discover clinical meaningful subtypes of SS based on genome-wide DNA methylation data. We performed a cluster analysis of genome-wide DNA methylation data from labial salivary gland (LSG) tissue collected from 64 SS cases and 67 non-cases. Specifically, hierarchical clustering was performed on low dimensional embeddings of DNA methylation data extracted from a variational autoencoder to uncover unknown heterogeneity. Clustering revealed clinically severe and mild subgroups of SS. Differential methylation analysis revealed that hypomethylation at the MHC and hypermethylation at other genome regions characterize the epigenetic differences between these SS subgroups. Epigenetic profiling of LSGs in SS yields new insights into mechanisms underlying disease heterogeneity. The methylation patterns at differentially methylated CpGs are different in SS subgroups and support the role of epigenetic contributions to the heterogeneity in SS. Biomarker data derived from epigenetic profiling could be explored in future iterations of the classification criteria for defining SS subgroups.

Identification and characterization of novel DIP-STRs from whole-genome sequencing data

In an attempt to enhance forensic DNA mixture deconvolution several alternative DNA typing approaches have been developed. Among these, DIP-STR compound markers can resolve extremely unbalanced two-source DNA mixtures of same-or-opposite sex donors, up to a 1:1000 minor:major DNA ratio. A forensic set of 10 markers was validated for casework and a larger set of 23 DIP-STRs has proven suitable to biogeographic ancestry inference and for prenatal paternity testing. Yet, to promote the widespread use of this original approach, more markers and multiplex panels need to be developed. To this end, here we describe an extended set of forensic DIP-STRs identified using currently available whole-genome sequencing datasets. Complete lists of Indels and STRs were obtained from reported frequencies of genetic variants of 76,156 genomes. About 3000 identified DIP-STRs candidates were shorter than 200 bp and 500 showed high haplotype variability estimated using the genotypes of individuals homozygous for the DIP or the STR. Here, we present 23 additional DIP-STRs validated for sensitivity, specificity and Swiss population variability. Finally, a set of 30 markers comprising seven previously validated ones is proposed for the prospective development of a forensic DIP-STR multiplex panel.

Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 10⁵ to 10⁶ samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Characterization of Danube Swabian population samples on a high-resolution genome-wide basis

BACKGROUND

German-derived ethnicities are one of the largest ethnic groups in Hungary, dating back to the formation of the Kingdom of Hungary, which took place at the beginning of the 11th century. Germans arrived in Hungary in many waves. The most significant immigration wave took place following the collapse of the Ottoman Empire in East-Central Europe which closed the 150 year long Ottoman occupation. To date, there are no comprehensive genome-wide studies investigating the genetic makeup of the Danube Swabians. Here we analyzed 47 Danube Swabian samples collected from elderly Swabian individuals living in the Dunaszekcső-Bár area, in Danube side villages of Southwest Hungary. These Swabians, according to self-declaration, did not admix with other ethnic groups for 3-6 succeeding generations. Using Illumina Infinium 720 K Beadchip genotype data, we applied allele frequency-based and haplotype-based genome-wide marker data analyses to investigate the ancestry and genetic composition of the collected Danube Swabian samples.

RESULTS

Haplotype-based analyses like identity by descent segment analysis show that the investigated Danube Swabians possess significant German and other West European ancestry, but their Hungarian ancestry is also prominent. Our results suggest that their main source of ancestry can be traced back to Western Europe, presumably to the region of Germany.

CONCLUSION

This is the first analysis of Danube Swabian population samples based on genome-wide autosomal data. Our results establish the basis for conducting further comprehensive research on Danube Swabians and on other German ethnicities of the Carpathian basin, which can help reconstruct their origin, and identify their major archaic genomic patterns.

Challenges in the recovery of the genetic data from human remains found on the Western Balkan migration route

Traditional DNA-based identification of human remains relies on the system of matching STR profile of the deceased with the family references or antemortem samples. In forensic cases without any available samples for the comparison, the body remains unidentified. The aim of this study was to assess the applicability of massively parallel sequencing (MPS) approach in the forensic cases of five drowned individuals recovered on the Western Balkan migration route. Besides capillary electrophoresis (CE)-based genetic profiling (aSTR, Y STR, and mitochondrial control region sequencing) of postmortem samples, we applied ForenSeq DNA Signature Prep Kit/Primer Mix B on MiSeqFGx platform and concomitant ForenSeq Universal Analysis (UAS) software. The assay showed high reproducibility and complete concordance with CE-based data except in locus DYF387S1. Allele and locus drop was evident in 2.9% of total SNPs that slightly reduced the completeness of the data. We endeavored to predict the phenotype of the tested samples and accurate biogeographical ancestry of European individual. UAS was less informative for the remaining samples assigned to Admixed American cluster. Nevertheless, the application of FROG-kb and Snipper tools along with admixture analysis in STRUCTURE and lineage markers revealed likely Middle Eastern and North African ancestry. We conclude that the combination of the phenotype and biogeographical ancestry predictions, including paternal and maternal genetic ancestry, represent a promising tool for humanitarian identification of dead migrants. Nevertheless, the data interpretation remains a challenging task.

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.

A central limit theorem concerning uncertainty in estimates of individual admixture

The concept of individual admixture (IA) assumes that the genome of individuals is composed of alleles inherited from K ancestral populations. Each copy of each allele has the same chance q_k to originate from population k, and together with the allele frequencies p in all populations at all M markers, comprises the admixture model. Here, we assume a supervised scheme, i.e. allele frequencies p are given through a reference database of size N, and q is estimated via maximum likelihood for a single sample. We study laws of large numbers and central limit theorems describing effects of finiteness of both, M and N, on the estimate of q. We recall results for the effect of finite M, and provide a central limit theorem for the effect of finite N, introduce a new way to express the uncertainty in estimates in standard barplots, give simulation results, and discuss applications in forensic genetics.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Rapid, Reference-Free human genotype imputation with denoising autoencoders

Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here, we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least fourfold faster inference run time relative to standard imputation tools.

Population history and genome wide association studies of birth weight in a native high altitude Ladakhi population

Pathological low birth weight due to fetal growth restriction (FGR) is an important predictor of adverse obstetric and neonatal outcomes. It is more common amongst native lowlanders when gestating in the hypoxic environment of high altitude, whilst populations who have resided at high altitude for many generations are relatively protected. Genetic study of pregnant populations at high altitude permits exploration of the role of hypoxia in FGR pathogenesis, and perhaps of FGR pathogenesis more broadly. We studied the umbilical cord blood DNA of 316 neonates born to pregnant women managed at the Sonam Norboo Memorial Hospital, Ladakh (altitude 3540m) between February 2017 and January 2019. Principal component, admixture and genome wide association studies (GWAS) were applied to dense single nucleotide polymorphism (SNP) genetic data, to explore ancestry and genetic predictors of low birth weight. Our findings support Tibetan ancestry in the Ladakhi population, with subsequent admixture with neighboring Indo-Aryan populations. Fetal growth protection was evident in Ladakhi neonates. Although no variants achieved genome wide significance, we observed nominal association of seven variants across genes (ZBTB38, ZFP36L2, HMGA2, CDKAL1, PLCG1) previously associated with birthweight.

Social and scientific motivations to move beyond groups in allele frequencies: The TOPMed experience

For the genomics community, allele frequencies within defined groups (or "strata") are useful across multiple research and clinical contexts. Benefits include allowing researchers to identify populations for replication or "look up" studies, enabling researchers to compare population-specific frequencies to validate findings, and facilitating assessment of variant pathogenicity in clinical contexts. However, there are potential concerns with stratified allele frequencies. These include potential re-identification (determining whether or not an individual participated in a given research study based on allele frequencies and individual-level genetic data), harm from associating stigmatizing variants with specific groups, potential reification of race as a biological rather than a socio-political category, and whether presenting stratified frequencies-and the downstream applications that this presentation enables-is consistent with participants' informed consents. The NHLBI Trans-Omics for Precision Medicine (TOPMed) program considered the scientific and social implications of different approaches for adding stratified frequencies to the TOPMed BRAVO (Browse All Variants Online) variant server. We recommend a novel approach of presenting ancestry-specific allele frequencies using a statistical method based upon local genetic ancestry inference. Notably, this approach does not require grouping individuals by either predominant global ancestry or race/ethnicity and, therefore, mitigates re-identification and other concerns as the mixture distribution of ancestral allele frequencies varies across the genome. Here we describe our considerations and approach, which can assist other genomics research programs facing similar issues of how to define and present stratified frequencies in publicly available variant databases.

Applicability of the IrisPlex system for eye color prediction in an admixed population from Argentina

Eye color prediction based on an individual's genetic information is of interest in the field of forensic genetics. In recent years, researchers have studied different genes and markers associated with this externally visible characteristic and have developed methods for its prediction. The IrisPlex represents a validated tool for homogeneous populations, though its applicability in populations of mixed ancestry is limited, mainly regarding the prediction of intermediate eye colors. With the aim of validating the applicability of this system in an admixed population from Argentina (n = 302), we analyzed the six single nucleotide variants used in that multiplex for eye color and four additional SNPs, and evaluated its prediction ability. We also performed a genotype-phenotype association analysis. This system proved to be useful when dealing with the extreme ends of the eye color spectrum (blue and brown) but presented difficulties in determining the intermediate phenotypes (green), which were found in a large proportion of our population. We concluded that these genetic tools should be used with caution in admixed populations and that more studies are required in order to improve the prediction of intermediate phenotypes.

Genetic evidence of tri-genealogy hypothesis on the origin of ethnic minorities in Yunnan

BACKGROUND

Yunnan is located in Southwest China and consists of great cultural, linguistic, and genetic diversity. However, the genomic diversity of ethnic minorities in Yunnan is largely under-investigated. To gain insights into population history and local adaptation of Yunnan minorities, we analyzed 242 whole-exome sequencing data with high coverage (~ 100-150 ×) of Yunnan minorities representing Achang, Jingpo, Dai, and Deang, who were linguistically assumed to be derived from three ancient lineages (the tri-genealogy hypothesis), i.e., Di-Qiang, Bai-Yue, and Bai-Pu.

RESULTS

Yunnan minorities show considerable genetic differences. Di-Qiang populations likely migrated from the Tibetan area about 6700 years ago. Genetic divergence between Bai-Yue and Di-Qiang was estimated to be 7000 years, and that between Bai-Yue and Bai-Pu was estimated to be 5500 years. Bai-Pu is relatively isolated, but gene flow from surrounding Di-Qiang and Bai-Yue populations was also found. Furthermore, we identified genetic variants that are differentiated within Yunnan minorities possibly due to the living circumstances and habits. Notably, we found that adaptive variants related to malaria and glucose metabolism suggest the adaptation to thalassemia and G6PD deficiency resulting from malaria resistance in the Dai population.

CONCLUSIONS

We provided genetic evidence of the tri-genealogy hypothesis as well as new insights into the genetic history and local adaptation of the Yunnan minorities.

The female protective effect against autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed three to four times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common inherited genetic risk for ASD are less studied, particularly within families. Leveraging the Danish iPSYCH resource, we found siblings of female ASD cases (n = 1,707) had higher rates of ASD than siblings of male ASD cases (n = 6,270; p < 1.0 × 10-10). In the Simons Simplex and SPARK collections, mothers of ASD cases (n = 7,436) carried more polygenic risk for ASD than fathers of ASD cases (n = 5,926; 0.08 polygenic risk score [PRS] SD; p = 7.0 × 10-7). Further, male unaffected siblings under-inherited polygenic risk (n = 1,519; p = 0.03). Using both epidemiologic and genetic approaches, our findings strongly support an FPE against ASD's common inherited influences.

Meta-imputation: An efficient method to combine genotype data after imputation with multiple reference panels

Genotype imputation is an integral tool in genome-wide association studies, in which it facilitates meta-analysis, increases power, and enables fine-mapping. With the increasing availability of whole-genome-sequence datasets, investigators have access to a multitude of reference-panel choices for genotype imputation. In principle, combining all sequenced whole genomes into a single large panel would provide the best imputation performance, but this is often cumbersome or impossible due to privacy restrictions. Here, we describe meta-imputation, a method that allows imputation results generated using different reference panels to be combined into a consensus imputed dataset. Our meta-imputation method requires small changes to the output of existing imputation tools to produce necessary inputs, which are then combined using dynamically estimated weights that are tailored to each individual and genome segment. In the scenarios we examined, the method consistently outperforms imputation using a single reference panel and achieves accuracy comparable to imputation using a combined reference panel.

Impact of African ancestry on the relationship between body mass index and survival in an early-stage breast cancer trial (ECOG-ACRIN E5103)

BACKGROUND

African ancestry (AA) and obesity are associated with worse survival in early-stage breast cancer. Obesity disproportionately affects women of AA; however, the intersection between ancestry and obesity on breast cancer outcomes remains unclear.

METHODS

A total of 2854 patients in the adjuvant trial E5103 were analyzed. Genetic ancestry was determined using principal components from a genome-wide array. The impact of continuous or binary body mass index (BMI) on disease-free survival (DFS) and overall survival (OS) was evaluated by multivariable Cox proportional hazards models in AA patients and European ancestry (EA) patients.

RESULTS

There were 2471 EA patients and 383 AA patients. Higher BMI was significantly associated with worse DFS and OS only in AA patients (DFS hazard ratio [HR], 1.25; 95% CI, 1.07-1.46; OS HR, 1.38; 95% CI, 1.10-1.73), not in EA patients (DFS HR, 0.97; 95% CI, 0.90-1.05; OS HR, 1.03; 95% CI, 0.93-1.14). Severe obesity (BMI ≥40) was significantly associated with worse survival in AA patients (DFS HR, 2.04; 95% CI, 1.21-3.43; OS HR, 2.21; 95% CI, 1.03-4.75) but had no impact on that of EA patients. In the estrogen receptor-positive (ER+) and triple-negative breast cancer subgroups, BMI was significantly associated with worse outcomes only in those AA patients with ER+ disease. Within the AA group, BMI remained associated with worse survival regardless of the AA proportion.

CONCLUSIONS

Higher BMI was statistically significantly associated with worse breast cancer outcomes in AA but not EA patients. This association was most significant for severe obesity and those with ER+ disease. These observations help define optimal populations for weight change interventions designed to affect disparities and survival in early-stage breast cancer.

LAY SUMMARY

African ancestry and obesity are both risk factors for worse survival after early-stage breast cancer. Women of African descent are also disproportionately affected by obesity; however, it is unclear what impact body weight has on racial disparities in breast cancer. Data from a large phase 3 clinical trial in high-risk, early-stage breast cancer were used to determine how body weight affects survival outcomes in European versus African Americans. Study results demonstrate that a higher body mass index is associated with increased risk of breast cancer recurrence and worse survival in women of African ancestry but not in women of European ancestry.

Comparative transcriptome reveals the thermal stress response differences between Heilongjiang population and Xinjiang population of Lota lota

Some cold-water fishes are particularly sensitive to the water temperature increasing caused by current global warming. However, the alterations in the physiology and behavior of infraspecific populations living in heterogeneous landscapes in response to water temperature increasing were significantly different. Consequently, understanding the impact of temperature increasing on different populations may be crucial for the conservation of cold-water fishes in the context of global warming. The burbot is the only freshwater specie in Gadiformes. To better understand the differences of different populations of burbot under similar thermal stress, Lota lota was selected as the research objects. Firstly, RNA-seq was applied to identify the transcriptomic responses of Heilongjiang population exposed to three temperature gradients (0 °C, 18 °C and 28 °C). Compared with 0 °C, 4216 and 12,657 genes were significantly differentially expressed at 18 °C and 28 °C, respectively. Meanwhile, 49 genes were significantly differentially expressed in three temperature pairs and these genes were presumed to involve in stress response process, immunologic process, reproductive process, development process, material metabolism process, signal transduction process, spermatogenesis process and cell apoptosis process. The response differences of two L. lota populations to similar thermal stress were compared and the results showed that they have different gene expression responses (the number of differentially expression genes and biological processes). The lower annual temperature of the Heilongjiang River might make it more sensitive to temperature increasing. Based on the comparative transcriptome analyses, 12 orthologous genes were considered as the potential regulators of L. lota preference for cold-water environment and these genes are potentially related to the immunologic process, reproductive process, development process, signal transduction process, and cell apoptosis process. Those results can provide basic information for the rational development of conservation strategies of different L. lota populations under the background of global warming.

Re-situations of scientific knowledge: a case study of a skirmish over clusters vs clines in human population genomics

We track and analyze the re-situation of scientific knowledge in the field of human population genomics ancestry studies. We understand re-situation as a process of accommodating the direct or indirect transfer of objects of knowledge from one site/situation to (one or many) other sites/situations. Our take on the concept borrows from Mary S. Morgan's work on facts traveling while expanding it to include other objects of knowledge such as models, data, software, findings, and visualizations. We structure a specific case study by tracking the re-situation of these objects between three research projects studying human population diversity reported in three articles in Science, Genome Research and PLoS Genetics between 2002 and 2005. We characterize these three engagements as a unit of analysis, a "skirmish," in order to compare: (a) the divergence of interests in how life-scientists answer similar research questions and (b) to track the challenging transformation of workflows in research laboratories as these scientific objects are re-situated individually or in bundles. Our analysis of the case study shows that an accurate understanding of re-situation requires tracking the whole bundle of objects in a project because they interact in particular key ways. The absence or dismissal of these interactions opens the door to unforeseen trade-offs, misunderstandings and misrepresentations about research design(s) and workflow(s) and what these say about the questions asked and the findings produced.

The genetic legacy of the Manila galleon trade in Mexico

The population of Mexico has a considerable genetic substructure due to both its pre-Columbian diversity and due to genetic admixture from post-Columbian trans-oceanic migrations. The latter primarily originated in Europe and Africa, but also, to a lesser extent, in Asia. We analyze previously understudied genetic connections between Asia and Mexico to infer the timing and source of this genetic ancestry in Mexico. We identify the predominant origin within Southeast Asia—specifically western Indonesian and non-Negrito Filipino sources—and we date its arrival in Mexico to approximately 13 generations ago (1620 CE). This points to a genetic legacy from the seventeenth century Manila galleon trade between the colonial Spanish Philippines and the Pacific port of Acapulco. Indeed, within Mexico we observe the highest level of this trans-Pacific ancestry in Acapulco, located in the state of Guerrero. This colonial Spanish trade route from East Asia to Europe was centred on Mexico and appears in historical records, but its legacy has been largely ignored. Identities and stories were suppressed due to slavery, assimilation of the immigrants as ‘Indios’ and incomplete historical records. Here we characterize this understudied Mexican ancestry.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Diversity and its causes: Lewontin on racism, biological determinism and the adaptationist programme

Lewontin's 1972 paper (RC Lewontin, 1972 The apportionment of human diversity, in Evolutionary biology, vol. 6 (eds T Dobzhansky, MK Hecht, WC Steere), pp. 381–398) can be viewed as one foray in his battle against biological determinism. Our paper shows where Lewontin, The apportionment of human diversity, fits in the debate over human classification that it stimulated. We outline three assumptions inherent in the biological deterministic view of human phenotypic diversity and show how the 1972 paper, as well as Lewontin's papers in 1970 and 1974 on the problems with the heritability statistic and his 1979 criticism of naive pan-selectionism, invalidate these assumptions. These papers were crucial components of his campaign against biological determinism and the racism with which it was associated. In the current climate of widespread racism and the rise of sociogenomics, it is important to revisit Lewontin's writings and to disseminate the messages they contain.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Inferring population structure in biobank-scale genomic data

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

Concurrent IgA Nephropathy and Membranous Nephropathy, Is It an Overlap Syndrome?

IgA nephropathy (IgAN) and membranous nephropathy (MN) are common glomerulonephritis, the presence of which in the same patient– concurrent of IgAN and MN (cIgAN/MN) has been described occasionally. This study aims to show clinical-pathological features of cIgAN/MN and attempts to suggest underlying pathogenesis using disease-specific biomarkers and a genomics approach. This retrospective cohort study described the clinical and pathological data from 137 patients with cIgAN/MN diagnosed in Peking University First Hospital from 2005 to 2019. One hundred primary IgAN and 100 MN cases were randomly selected as disease controls between the same time interval. Moreover, disease-specific biomarkers and polygenic risk score models were conducted to reveal the underlying pathogenesis. The median age of the cIgAN/MN cases was 45-year-old, and 46% were women. Compared to IgAN, patients with cIgAN/MN had a higher level of 24-hour proteinuria excretion but lower microscopic hematuria. They had a lower median level of galactose-deficient IgA1 (Gd-IgA1, 4.00 versus 5.45 μg/ml, P=0.002) as well as the standardized genetic risk scores of developing IgAN (GRSs: 0.05 versus 0.68, P&lt;0.001). Compared to MN, patients with cIgAN/MN had a lower proportion of nephrotic syndrome and a lower level of albumin-to-creatinine ratio. However, the 24-hour proteinuria levels, serum lipid profiles, proportion of hypertension, and pathology classification were similar. Patients with cIgAN/MN had lower levels of plasma autoantibodies against the M-type transmembrane phospholipase A2 receptor (PLA2R) (11.23 versus 36.59 U/ml, P=0.005). Intriguingly, there were no statistical differences in standardized GRSs of developing MN between them (2.77 versus 3.02, P=0.326). Compared to IgAN, cIgAN/MN may lean towards MN more according to clinical-pathological features, disease-specific biomarker levels, and disease-specific genetic risk scores.

Unveiling forensically relevant biogeographic, phenotype and Y-chromosome SNP variation in Pakistani ethnic groups using a customized hybridisation enrichment forensic intelligence panel

Massively parallel sequencing following hybridisation enrichment provides new opportunities to obtain genetic data for various types of forensic testing and has proven successful on modern as well as degraded and ancient DNA. A customisable forensic intelligence panel that targeted 124 SNP markers (67 ancestry informative markers, 23 phenotype markers from the HIrisplex panel, and 35 Y-chromosome SNPs) was used to examine biogeographic ancestry, phenotype and sex and Y-lineage in samples from different ethnic populations of Pakistan including Pothwari, Gilgit, Baloach, Pathan, Kashmiri and Siraiki. Targeted sequencing and computational data analysis pipeline allowed filtering of variants across the targeted loci. Study samples showed an admixture between East Asian and European ancestry. Eye colour was predicted accurately based on the highest p-value giving overall prediction accuracy of 92.8%. Predictions were consistent with reported hair colour for all samples, using the combined highest p-value approach and step-wise model incorporating probability thresholds for light or dark shade. Y-SNPs were successfully recovered only from male samples which indicates the ability of this method to identify biological sex and allow inference of Y-haplogroup. Our results demonstrate practicality of using hybridisation enrichment and MPS to aid in human intelligence gathering and will open many insights into forensic research in South Asia.

Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

BACKGROUND

Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes.

METHODS

The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes.

RESULTS

Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m²), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m²). Each standard deviation increment in the PRS was associated with 0.83 kg/m² unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m² in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m² in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes.

CONCLUSIONS

In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine.

How to reduce dimension with PCA and random projections?

In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are "data-oblivious" methods such as random projections and sketching, and "data-aware" methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data-adaptivity for PCA. In this work, we study how to combine them to get the best of both. We study "sketch and solve" methods that take a random projection (or sketch) first, and compute PCA after. We compute the performance of several popular sketching methods (random iid projections, random sampling, subsampled Hadamard transform, CountSketch, etc) in a general "signal-plus-noise" (or spiked) data model. Compared to well-known works, our results (1) give asymptotically exact results, and (2) apply when the signal components are only slightly above the noise, but the projection dimension is non-negligible. We also study stronger signals allowing more general covariance structures. We find that (a) signal strength decreases under projection in a delicate way depending on the structure of the data and the sketching method, (b) orthogonal projections are slightly more accurate, (c) randomization does not hurt too much, due to concentration of measure, (d) CountSketch can be somewhat improved by a normalization method. Our results have implications for statistical learning and data analysis. We also illustrate that the results are highly accurate in simulations and in analyzing empirical data.