A human genome diversity cell line panel

Comparative Analysis of the Genetic Composition of Minorities in the Carpathian Basin Through Genome-Wide Autosomal Data

BACKGROUND/OBJECTIVES

The Carpathian Basin is a genetically and culturally diverse region shaped by complex historical migrations and various ethnic groups. While studies based on Y-chromosomal and mitochondrial DNA have provided valuable insights into the genetic diversity of these populations, genome-wide autosomal SNP data remain underutilized in understanding the genetic structure of these groups. This study presents the first genome-wide autosomal SNP-based analysis of key Hungarian-speaking ethnic groups in the region, focusing on admixture patterns and the extent of preserved historical genetic components.

METHODS

We analyzed genome-wide autosomal SNP data from 597 individuals representing several ethnic groups in the Carpathian Basin. Standard population genetic methods were applied to assess genetic structure, admixture and differentiation, with comparisons to broader European reference populations.

RESULTS

Most ethnic groups displayed genetic affinities with Eastern European populations, consistent with historical and geographical proximity. The Swabian group, of German descent, exhibited a distinct Western European genetic component, likely due to historical isolation. Transylvanian populations appeared relatively homogeneous, indicating a shared ancestral background. In contrast, Csangos showed distinct sub-clusters, suggesting population isolation and distinct histories. Overall, genetic homogeneity characterizes the region, though certain isolated groups retain distinct ancestral signatures.

CONCLUSIONS

Autosomal SNP analysis revealed mild overall genetic structuring among Carpathian Basin ethnic groups. However, historical isolation has preserved unique genetic components in specific groups, highlighting the value of genome-wide data in uncovering fine-scale population structure. These findings contribute to a deeper understanding of regional genetic diversity, which has implications for both population history and health-related genetic research.

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

OBJECTIVES

Sleep is a complex behavior regulated by genetic and environmental factors impacting disease outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on common diseases, specifically mental health disorders, has yet to be fully elucidated. Using the Mass General Brigham Biobank, we examined the association of multidimensional sleep with disease outcomes and investigated whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health diseases.

METHODS

We generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms and constructed a Sleep Lifestyle Index based on self-reported questions and electronic health records; tested their association; performed phenome-wide association analyses between these indexes and clinical phenotypes; and analyzed their interaction on prevalent mental health diseases. A total of 15,884 participants were included in the analysis (mean age 54.4; 58.6% female).

RESULTS

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). A total of 108 disease outcomes were associated with both indexes, spanning 12 disease groups. No interactions were found between the indexes on mental health diseases.

CONCLUSIONS

Favorable sleep behaviors and genetic predisposition to healthy sleep may independently protect against disease, underscoring the impact of multidimensional sleep on population health and the need for prevention strategies focused on healthy sleep habits.

A geographic history of human genetic ancestry

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. 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 used 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 spatiotemporal context of genetic ancestry when describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

Evaluating genome-wide and targeted forensic sequencing approaches to kinship determination

Kinship determination is a valuable tool in forensic genetics, with applications including familial searching, disaster victim identification, and investigative genetic genealogy. Conventional typing of small numbers of autosomal short tandem repeats (STRs) confidently identifies only first-degree relatives. Massively parallel sequencing (MPS) can access more STRs and resolve alleles identical by length but differing in sequence (isoalleles), which may increase the power of kinship estimation, particularly when combined with additional sequenced single nucleotide polymorphism (SNP) loci, as in the ForenSeq DNA Signature Prep kit. MPS sequencing of ∼10,000 SNPs is available in the ForenSeq Kintelligence kit, promising detection of more distant kin, while SNP chips carrying hundreds of thousands of markers increase resolution still further. Here we evaluate these different resolutions in a set of pedigrees, and via simulations. As expected, the key factor influencing the precision of kinship estimation is the number of markers analysed and MPS-based analysis of STRs increases resolution, with the full set of ForenSeq DNA Signature Prep kit markers allowing detection of third-degree relatives. Since SNP chips include non-autosomal (X- and Y-chromosomal, and mitochondrial [mtDNA]) markers, we ask how these perform within the pedigrees, cross-referencing to Y-STR sequence data. We highlight the importance of understanding haplogroup resolutions in the increasingly complex Y and mtDNA phylogenies, to avoid false exclusions. Incorporation of X-SNPs allows tracing of X-chromosome segments within families. These different approaches can add value to kinship estimation, but some require simpler bioinformatic interfaces to make them more widely accessible in practice, and also access to appropriate allele frequency data to avoid problems associated with ancestry mis-specification.

Genetic architecture in Greenland is shaped by demography, structure and selection

Greenlandic Inuit and other indigenous populations are underrepresented in genetic research1,2, leading to inequity in healthcare opportunities. To address this, we performed analyses of sequenced or imputed genomes of 5,996 Greenlanders with extensive phenotypes. We quantified their historical population bottleneck and how it has shaped their genetic architecture to have fewer, but more common, variable sites. Consequently, we find twice as many high-impact genome-wide associations to metabolic traits in Greenland compared with Europe. We infer that the high-impact variants arose after the population split from Native Americans and thus are Arctic-specific, and show that some of them are common due to not only genetic drift but also selection. We also find that European-derived polygenic scores for metabolic traits are only half as accurate in Greenlanders as in Europeans, and that adding Arctic-specific variants improves the overall accuracy to the same level as in Europeans. Similarly, lack of representation in public genetic databases makes genetic clinical screening harder in Greenlandic Inuit, but inclusion of Greenlandic data remedies this by reducing the number of non-causal candidate variants by sixfold. Finally, we identify pronounced genetic fine structure that explains differences in prevalence of monogenic diseases in Greenland and, together with recent changes in mobility, leads to a predicted future reduction in risk for certain recessive diseases. These results illustrate how including data from Greenlanders can greatly reduce inequity in genomic-based healthcare.

Whole genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole genome sequencing of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls. To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of BD association with single-variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD GWAS loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits

Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene-environment and gene-gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations.

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, so together may provide a more complete picture of sleep health, while 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 (including potential causal links) to behavioral, psychological, and cardiometabolic traits.

Genomic and physiological mechanisms of high-altitude adaptation in Ethiopian highlanders: a comparative perspective

High-altitude adaptation is a remarkable example of natural selection, yet the genomic and physiological adaptation mechanisms of Ethiopian highlanders remain poorly understood compared to their Andean and Tibetan counterparts. Ethiopian populations, such as the Amhara and Oromo, exhibit unique adaptive strategies characterized by moderate hemoglobin levels and enhanced arterial oxygen saturation, indicating distinct mechanisms of coping with chronic hypoxia. This review synthesizes current genomic insights into Ethiopian high-altitude adaptation, identifying key candidate genes involved in hypoxia tolerance and examining the influence of genetic diversity and historical admixture on adaptive responses. Furthermore, the review highlights significant research gaps, particularly the underrepresentation of Ethiopian populations in global genomic studies, the lack of comprehensive genotype-phenotype analyses, and inconsistencies in research methodologies. Addressing these gaps is crucial for advancing our understanding of the genetic basis of human adaptation to extreme environments and for developing a more complete picture of human physiological resilience. This review offers a comparative perspective with Tibetan and Andean highlanders, emphasizing the need for expanding genomic representation and refining methodologies to uncover the genetic mechanisms underlying high-altitude adaptation in Ethiopian populations.

Developmental validation of a novel all-in one assay of X chromosomal multi-insertion/deletion loci for forensic genetics

Multi-insertion/deletion polymorphisms (Multi-InDels), as the novel genetic markers, show great potential in forensic research. Whereas, forensic researchers mainly focus on the multi-InDels on the autosomes, which can provide relatively limited information in some complex paternity cases. In this study, a novel X chromosomal multi-InDel multiplex amplification system was designed, containing 22 multi-InDels and one STR locus on the X chromosome. In order to verify the applicability and effectiveness of the novel system, the sensitivity, species specificity, repeatability, stability, and precision of the panel were verified. The results showed that the novel developed multiplex amplification system could still obtain complete allele profile at 62.5pg of template DNA and showed good tolerance to six common inhibitors. Meanwhile, the analyses of case samples showed that the system performed well on biological samples commonly found in forensic cases. The combined power of discrimination for males and females in the Guizhou Han population was greater than 0.999999999. Besides, the combined mean exclusion chance of these 23 loci exceeded 0.9999 in the trios and the duos. In conclusion, the multiplex amplification system is sensitive, precision, reproducible, and informative, which could be used as a high-efficient tool for complex kinship identification and personal identification.

Genetic risk factors for Mesoamerican nephropathy

Mesoamerican nephropathy (MeN) is a progressive kidney disease found on the Pacific coast of Central America primarily in young male agricultural workers without typical kidney disease risk factors. While it is generally accepted that environmental exposures contribute to MeN, we hypothesized that there was also an important genetic component. We performed a genome-wide association study comparing individuals with MeN versus individuals with normal kidney function. We found that Native American ancestry was strongly associated with increased risk of MeN. We also identified candidate variants in the OPCML gene, which encodes a protein that binds opioid receptors, that were associated with ~sixfold reduced odds of MeN (allele frequency 0.029 in controls and 0.005 in cases, OR = 0.16; P = 4 × 10-8). Sugarcane workers with the protective OPCML variants had markedly increased urine osmolality suggesting greater ability to defend against hypovolemia. Experiments with Opcml knock-out mice revealed roles for OPCML in fluid balance and temperature regulation consistent with our findings in humans. Our data suggest that heritable differential sensitivity to heat stress and dehydration contributes to high rates of kidney disease in Central America.

Estimation of genetic admixture proportions via haplotypes

Estimation of ancestral admixture is essential for creating personal genealogies, studying human history, and conducting genome-wide association studies (GWAS). The following three primary methods exist for estimating admixture coefficients. The frequentist approach directly maximizes the binomial loglikelihood. The Bayesian approach adds a reasonable prior and samples the posterior distribution. Finally, the nonparametric approach decomposes the genotype matrix algebraically. Each approach scales successfully to datasets with a million individuals and a million single nucleotide polymorphisms (SNPs). Despite their variety, all current approaches assume independence between SNPs. To achieve independence requires performing LD (linkage disequilibrium) filtering before analysis. Unfortunately, this tactic loses valuable information and usually retains many SNPs still in LD. The present paper explores the option of explicitly incorporating haplotypes in ancestry estimation. Our program, HaploADMIXTURE, operates on adjacent SNP pairs and jointly estimates their haplotype frequencies along with admixture coefficients. This more complex strategy takes advantage of the rich information available in haplotypes and ultimately yields better admixture estimates and better clustering of real populations in curated datasets.

The MICA deletion across different populations

The MICA gene encodes a glycoprotein upregulated upon cellular stress, particularly in oxidative stress, intracellular infections, and tumorigenesis. This stress-signaling molecule interacts with the activating receptor NKG2D from Natural Killer (NK) and some T lymphocytes, stimulating their cytotoxic activity. MICA is encoded within the human Major Histocompatibility Complex next to the HLA-B locus and is highly polymorphic. MICA might be absent from chromosome 6 due to a large deletion of approximately 100 Kb between HLA-B and MICB. Therefore, some individuals may not produce any isoform of MICA. The distribution of this phenotype may vary among different populations. We evaluated the distribution of the MICA*del and other MICA null alleles in different biogeographic regions and the Linkage Disequilibrium (LD) pattern between this allele and HLA-B. We detected at least two different patterns of deletion, one with full deletion of MICA and surrounding sequences and one partial MICA deletion. The presence of different patterns of deletion suggests independent deletion events. We confirm that the previously described MICA*del allele is mainly associated with B*48 and MICB*009N in Asia and America, but other haplotypes also occur. While most samples with complete or partial MICA deletion are heterozygous and present one functional copy of both MICA and MICB genes, we detected two samples with no functional MICA and one with no functional MIC genes. Therefore, other mechanisms might be in place to compensate for the absence of MIC molecules.

RNA sequencing comparing centenarian and middle-aged women lymphoblastoid cell lines identifies age-related dysregulated expression of genes encoding selenoproteins, heat shock proteins, CD99, and BID

Women typically live longer than men, and constitute the majority of centenarians. We applied RNA-sequencing (RNA-seq) of blood-derived lymphoblastoid cell lines (LCLs) from women aged 60-80 years and centenarians (100-105 years), validated the RNA-seq findings by real-time PCR, and additionally measured the differentially expressed genes in LCLs from young women aged 20-35 years. Top RNA-seq genes with differential expression between the age groups included three selenoproteins (GPX1, SELENOW, SELENOH) and three heat shock proteins (HSPA6, HSPA1A, HSPA1B), with the highest expression in LCLs from young women, indicating that young women are better protected from oxidative stress. The expression of two additional genes, BID encoding BH3-interacting domain death agonist and CD99 encoding CD99 antigen, showed unique age dependence, with similar expression levels in young and centenarian women while exhibiting higher and lower expression levels, respectively, in LCLs from women aged 60-80 years compared with the two other age groups. This age-related differential expression of BID and CD99 suggests elevated inflammation susceptibility in middle-aged women compared with either young or centenarian women. Our findings, once validated with human peripheral blood mononuclear cells and further cell types, may lead to novel healthy aging diagnostics and therapeutics.

Analysis of Gyimes Csango population samples on a high-resolution genome-wide basis

BACKGROUND

The Csangos are an East-Central European ethnographic group living mainly in east of Transylvania in Romania. Traditionally, ethnography distinguishes three Csango subpopulations, the Moldavian, Gyimes and Burzenland Csangos. In our previous study we found that the Moldavian Csangos have East Asian/Siberian Turkic ancestry components that might be unique in the East-Central European region and might help to better understand the history of Hungarian speaking ethnic groups of the area. Since then, we obtained further Csango samples from Moldavia and from a distinct region of Gyimes, which two Csango subgroups are traditionally different since they live in a degree of isolation not only from other people but also from each other. Here we present the first genomic analysis of Gyimes Csangos, which intended to compare the genetic makeup of these two Csango subgroups using both allele-frequency and haplotype-based methods. The main goal of the study was to investigate the genetic isolation of the Csangos on a genome-wide SNP basis and to assess the isolation of Gyimes Csangos, which in contrast to the Moldavians was not yet studied.

RESULTS

Our results show that these two Csango groups show slight differences from each other. We confirmed the genetic isolation of Moldavian Csangos and revealed that Gyimes Csangos have a similar, but detectably weaker isolation. In the case of Gyimes Csangos we detected also a stronger East European or presumably Asian derived ancestry.

CONCLUSION

The Gyimes Csangos show a degree of genetic isolation comparable to that of the Moldavians. The Asian ancestry that differentiates the Moldavian Csango people from the other East-Central European populations may be present in the Gyimes Csangos in an even higher degree, since Gyimes Csango individuals show a more significant share from that ancestry component.

Genotype imputation in human genomic studies

Imputation is a method that supplies missing information about genetic variants that could not be directly genotyped with DNA microarrays or low-coverage sequencing. Imputation plays a critical role in genome-wide association studies (GWAS). It leads to a significant increase in the number of studied variants, which improves the resolution of the method and enhances the comparability of data obtained in different cohorts and/or by using different technologies, which is important for conducting meta-analyses. When performing imputation, genotype information from the study sample, in which only part of the genetic variants are known, is complemented using the standard (reference) sample, which has more complete genotype data (most often the results of whole-genome sequencing). Imputation has become an integral part of human genomic research due to the benefits it provides and the increasing availability of imputation tools and reference sample data. This review focuses on imputation in human genomic research. The first section of the review provides a description of technologies for obtaining information about human genotypes and characteristics of these types of data. The second section describes the imputation methodology, lists the stages of its implementation and the corresponding programs, provides a description of the most popular reference panels and methods for assessing the quality of imputation. The review concludes with examples of the use of imputation in genomic studies of samples from Russia. This review shows the importance of imputation, provides information on how to carry it out, and systematizes the results of its application using Russian samples.

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, multi-faceted 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.

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.

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.