Biology subjects

Chiang, C. W. K.

Publications and source records attributed to Chiang, C. W. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-cell multiomic mapping of genetic predisposition to childhood B-cell acute lymphoblastic leukemia

Inherited genetic variation substantially increases the risk for developing childhood B-cell acute lymphoblastic leukemia (B-ALL), the most common cancer in children, yet the underlying mechanisms remain poorly understood. To address this limitation, we employ a single-cell multiomic framework to functionally dissect common regulatory variants associated with B-ALL risk. Coupling this multiomic analysis with assessment of allelic skews in chromatin accessibility, we reveal the impact of risk alleles and disruptions in transcription factor networks specific to B-cell progenitors, thereby providing mechanistic insights into altered regulatory programs underlying B-ALL predisposition. By constructing long-range variant-to-target gene maps, we identify 34 high-confidence B-ALL susceptibility genes. Among these, we uncover and functionally validate a risk allele that selectively upregulates expression of ELK3, a previously unrecognized regulator of B-cell development and leukemogenesis. Together, these findings establish a comprehensive variant-to-function map of cell state-specific regulatory disruptions underlying inherited predisposition to B-ALL and define new risk mechanisms, which could pave the way for future targeted prevention approaches.

cancer biology↗

bioRxiv2025

A genealogy-based approach for revealing ancestry-specific structures in admixed populations

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods for elucidating the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for elucidating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the Ancestral Recombination Graph (ARG) and local ancestry calls and computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories. Taken together, as-eGRM has the promise to better reveal the fine-scale structure within an ancestry component of admixed individuals, which can help improve the robustness and interpretation of findings from association studies of disease or complex traits for these understudied populations.

genetics↗

bioRxiv2023

A likelihood-based framework for demographic inference from genealogical trees

The demographic history of a population drives the pattern of genetic variation and is encoded in the gene-genealogical trees of the sampled alleles. However, existing methods to infer demographic history from genetic data tend to use relatively low-dimensional summaries of the genealogy, such as allele frequency spectra. As a step toward capturing more of the information encoded in the genome-wide sequence of genealogical trees, here we propose a novel framework called the genealogical likelihood (gLike), which derives the full likelihood of a genealogical tree under any hypothesized demographic history. Employing a graph-based structure, gLike summarizes across independent trees the relationships among all lineages in a tree with all possible trajectories of population memberships through time and efficiently computes the exact marginal probability under a parameterized demographic model. Through extensive simulations and empirical applications on populations that have experienced multiple admixtures, we showed that gLike can accurately estimate dozens of demographic parameters when the true genealogy is known, including ancestral population sizes, admixture timing, and admixture proportions. Moreover, when using genealogical trees inferred from genetic data, we showed that gLike outperformed conventional demographic inference methods that leverage only the allele-frequency spectrum and yielded parameter estimates that align with established historical knowledge of the past demographic histories for populations like Latino Americans and Native Hawaiians. Furthermore, our framework can trace ancestral histories by analyzing a sample from the admixed population without proxies for its source populations, removing the need to sample ancestral populations that may no longer exist. Taken together, our proposed gLike framework harnesses underutilized genealogical information to offer exceptional sensitivity and accuracy in inferring complex demographies for humans and other species, particularly as estimation of genome-wide genealogies improves.

genetics↗

bioRxiv2023

Recombination map tailored to Native Hawaiians reduces false positive findings in genomic scans of positive selection

Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map), and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearmans rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score| > 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.

genetics↗

bioRxiv2023

Imputation Efficacy Across Global Human Populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.

genetics↗

bioRxiv2023

Tree-based QTL mapping with expected local geneticrelatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

genetics↗

bioRxiv2022

Inverted genomic regions between reference genome builds in humans impact imputation accuracy and decrease the power of association testing

Over the last two decades, the human reference genome has undergone multiple updates as we complete a linear representation of our genome. There are two versions of human references currently used in the biomedical literature, GRCh37/hg19 and GRCh38, and conversions between these versions are critical for quality control, imputation, and association analysis. In the present study we show that genomic coordinates for single nucleotide variants (SNVs) in regions inverted between different builds of the reference genome are erroneously converted by the TOPMed imputation server. Depending on the array type, we estimate the inappropriate conversions of variant coordinates would occur in about 2-5 Mb of the genome. Independent of the imputation server, however, in-house conversions of variant coordinates are routine yet impact of mapped variants to these inverted regions and its downstream analytical consequences are underrecognized. We observed that errors for palindromic variants in these inverted regions cannot be detected by standard quality control procedures and destabilize the local haplotype structure, leading to loss of imputation accuracy and power in association analyses. Though only a small proportion of the genome is affected, we show that these regions include important disease susceptibility variants that would be lost due to poor imputation. For example, we show that a known locus associated with prostate cancer on chr10 would have its association P-value drop from 2.86x10-7 to 0.0011 in a case-control analysis of 20,286 Africans and African Americans (10,643 cases and 9,643 controls). We devise a straight-forward heuristic based on the popular tool liftOver that can easily detect and correct these variants in the inverted regions between genome builds to locally improve imputation accuracy.

genomics↗

bioRxiv2022

Variant to function mapping at single-cell resolution through network propagation

With burgeoning human disease genetic associations and single-cell genomic atlases covering a range of tissues, there are unprecedented opportunities to systematically gain insights into the mechanisms of disease-causal variation. However, sparsity and noise, particularly in the context of single-cell epigenomic data, hamper the identification of disease- or trait-relevant cell types, states, and trajectories. To overcome these challenges, we have developed the SCAVENGE method, which maps causal variants to their relevant cellular context at single-cell resolution by employing the strategy of network propagation. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation including enrichment of blood traits at distinct stages of human hematopoiesis, defining monocyte subsets that increase the risk for severe coronavirus disease 2019 (COVID-19), and identifying intermediate lymphocyte developmental states that are critical for predisposition to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution, but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.

genomics↗

bioRxiv2021

A genealogical estimate of genetic relationships

The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM generally does not take advantage of linkage information and does not reflect the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed using genealogies inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to genotyping data from a population sample from Northern and Eastern Finland, we find that clustering analysis using the eGRM reveals population structure driven by subpopulations that would not be apparent using the canonical GRM, and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.

genetics↗

bioRxiv2021

Deciphering signatures of natural selection via deep learning

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. We propose a deep learning-based framework, DeepGenomeScan, that can detect signatures of local adaptation. We demonstrate that DeepGenomeScan outperformed PCA and RDA-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under non-linear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified using existing methods.

genomics↗

bioRxiv2021

Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) significantly improves the accuracy of predicting geographic origin of individuals

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect geographic patterns from genetic data is principal components analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised nonlinear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC significantly improved the population separability compared with PCA and DAPC. The application to POPRES and CONVERGE datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals, and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

genomics↗

bioRxiv2020

Association of Structural Variation with Cardiometabolic Traits in Finns

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low frequency SVs for association with 116 quantitative traits, and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including two novel loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB gene promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p=1.47x10-54), and is also associated with increased levels of total cholesterol (p=1.22x10-28) and 14 additional cholesterol-related traits, and (2) a multiallelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p=4.81x10-21) and alanine (p=6.14x10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs), and one linking recurrent HP gene deletion and cholesterol levels (p=6.24x10-10), which was also found to be strongly associated with increased glycoprotein level (p=3.53x10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

genetics↗

bioRxiv2020

Allele frequency differentiation at height-associated SNPs among continental human populations

Polygenic adaptation is thought to be an important mechanism of phenotypic evolution in humans, although recent evidence of confounding due to residual stratification in consortium GWAS made studies of polygenic adaptation more difficult to interpret. Using FST as a measure of allele frequency differentiation, a previous study has shown that the mean FST among African, East Asian, and European populations is significantly higher at height-associated SNPs than that found at matched non-associated SNPs, suggesting that polygenic adaptation is one of the reasons for differences in human height among these continental populations. However, we showed here even though the height-associated SNPs were identified using only European ancestry individuals, the estimated effect sizes are significantly associated with structures across continental populations, potentially explaining the elevated level of differentiation previously reported. To alleviate concerns of biased ascertainment of SNPs, we re-examined the distribution of FST at height-associated alleles ascertained from two biobank level GWAS (UK Biobank, UKB, and Biobank Japan, BBJ). We showed that when compared to non-associated SNPs, height-associated SNPs remain significantly differentiated among African, East Asian, and European populations from both 1000 Genomes (p = 0.0012 and p = 0.0265 when height SNPs were ascertained from UKB and BBJ, respectively), and Human Genome Diversity Panels (p = 0.0225 for UKB and p = 0.0032 for BBJ analyses). In contrast to FST-based analyses, we found no significant difference or consistent ranked order among continental populations in polygenic height scores constructed from SNPs ascertained from UKB and BBJ. In summary, our results suggest that, consistent with previous reports, height-associated SNPs are significantly differentiated in frequencies among continental populations after removing concerns of confounding by uncorrected stratification. Polygenic score-based analysis in this context appears to be susceptible to the choice of SNPs and, as we compared to FST-based statistics in simulations, would lose power in detecting polygenic adaptation if there are independent converging selections in more than one population.

evolutionary biology↗

bioRxiv2020

The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians

Epidemiological studies of obesity, Type-2 diabetes (T2D), cardiovascular diseases and several common cancers have revealed an increased risk in Native Hawaiians compared to European- or Asian-Americans living in the Hawaiian islands. However, there remains a gap in our understanding of the genetic factors that affect the health of Native Hawaiians. To fill this gap, we studied the genetic risk factors at both the chromosomal and sub-chromosomal scales using genome-wide SNP array data on [~]4,000 Native Hawaiians from the Multiethnic Cohort. We estimated the genomic proportion of Native Hawaiian ancestry ("global ancestry," which we presumed to be Polynesian in origin), as well as this ancestral component along each chromosome ("local ancestry") and tested their respective association with binary and quantitative cardiometabolic traits. After attempting to adjust for non-genetic covariates evaluated through questionnaires, we found that per 10% increase in global Polynesian genetic ancestry, there is a respective 8.6%, and 11.0% increase in the odds of being diabetic (P = 1.65 10-4) and having heart failure (P = 2.18 10-4), as well as a 0.059 s.d. increase in BMI (P = 1.04 10-10). When testing the association of local Polynesian ancestry with risk of disease or biomarkers, we identified a chr6 region associated with T2D. This association was driven by an uniquely prevalent variant in Polynesian ancestry individuals. However, we could not replicate this finding in an independent Polynesian cohort from Samoa due to the small sample size of the replication cohort. In conclusion, we showed that Polynesian ancestry, which likely capture both genetic and lifestyle risk factors, is associated with an increased risk of obesity, Type-2 diabetes, and heart failure, and that larger cohorts of Polynesian ancestry individuals will be needed to replicate the putative association on chr6 with T2D. Author SummaryNative Hawaiians are one of the fastest growing ethnic minority in the U.S., and exhibit increased risk for metabolic and cardiovascular diseases. However, they are generally understudied, especially from a genetic perspective. To fill this gap, we studied the association of Polynesian genetic ancestry, at genomic and subgenomic scale, with quantitative and binary traits in self-identified Native Hawaiians. We showed that Polynesian ancestry, which likely capture both genetic and non-genetic risk factors related to Native Hawaiian people and culture are associated with increased risk for obesity, type-2 diabetes, and heart failure. While we do not endorse utilizing genetic information to supplant current standards of defining community membership through self-identity or genealogical records, our results suggest future studies could identify population-specific genetic susceptibility factors that may be useful in suggesting underlying biological mechanisms and reducing the disparity in disease interventions in Polynesian populations.

genetics↗

bioRxiv2019

Population specific reference panels are crucial for the genetic analyses of Native Hawai’ians: an example of the CREBRF locus

Statistical imputation applied to genome-wide array data is the most cost-effective approach to complete the catalog of genetic variation in a study population. However, imputed genotypes in underrepresented populations incur greater inaccuracies due to ascertainment bias and a lack of representation among reference individuals,, further contributing to the obstacles to study these populations. Here we examined the consequences due to the lack of representation by genotyping a functionally important, Polynesian-specific variant, rs373863828, in the CREBRF gene, in a large number of self-reported Native Hawaiians (N=3,693) from the Multiethnic Cohort. We found the derived allele of rs373863828 was significantly associated with several adiposity traits with large effects (e.g. 0.214 s.d., or approximately 1.28 kg/m2, per allele, in BMI as the most significant; P = 7.5x10-5). Due to the current absence of Polynesian representation in publicly accessible reference sequences, rs373863828 or any of its proxies could not be tested through imputation using these existing resources. Moreover, the association signals at this Polynesian-specific variant could not be captured by alternative approaches, such as admixture mapping. In contrast, highly accurate imputation can be achieved even if a small number (<200) of Polynesian reference individuals were available. By constructing an internal set of Polynesian reference individuals, we were able to increase sample size for analysis up to 3,936 individuals, and improved the statistical evidence of association (e.g. p = 1.5x10-7, 3x10-6, and 1.4x10-4 for BMI, hip circumference, and T2D, respectively). Taken together, our results suggest the alarming possibility that lack of representation in reference panels would inhibit discovery of functionally important, population-specific loci such as CREBRF. Yet, they could be easily detected and prioritized with improved representation of diverse populations in sequencing studies.

genetics↗

bioRxiv2019

Evidence of polygenic adaptation at height-associated loci in mainland Europeans and Sardinians

Adult height was one of the earliest putative examples of polygenic adaptation in human. By constructing polygenic height scores using effect sizes and frequencies from hundreds of genomic loci robustly associated with height, it was reported that Northern Europeans were genetically taller than Southern Europeans beyond neutral expectation. However, this inference was recently challenged. Sohail et al. and Berg et al. showed that the polygenic signature disappeared if summary statistics from UK Biobank (UKB) were used in the analysis, suggesting that residual uncorrected stratification from large-scale consortium studies was responsible for the previously noted genetic difference. It thus remains an open question whether height loci exhibit signals of polygenic adaptation in any human population. In the present study, we re-examined this question, focusing on one of the shortest European populations, the Sardinians, as well as on the mainland European populations in general. We found that summary statistics from UKB significantly correlate with population structure in Europe. To further alleviate concerns of biased ascertainment of GWAS loci, we examined height-associated loci from the Biobank of Japan (BBJ). Applying frequency-based inference over these height-associated loci, we showed that the Sardinians remain significantly shorter than expected (~ 0.35 standard deviation shorter than CEU based on polygenic height scores, P = 1.95e-6). We also found the trajectory of polygenic height scores decreased over at least the last 10,000 years when compared to the British population (P = 0.0123), consistent with a signature of polygenic adaptation at height-associated loci. Although the same approach showed a much subtler signature in mainland European populations, we found a clear and robust adaptive signature in UK population using a haplotype-based statistic, tSDS, driven by the height-increasing alleles (P = 4.8e-4). In summary, by examining frequencies at height loci ascertained in a distant East Asian population, we further supported the evidence of polygenic adaptation at height-associated loci among the Sardinians. In mainland Europeans, we also found an adaptive signature, although becoming more pronounced only in haplotype-based analysis.

genetics↗

bioRxiv2018

Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

Genetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

evolutionary biology↗

bioRxiv2017

A comprehensive map of genetic variation in the world’s largest ethnic group - Han Chinese

As are most non-European populations around the globe, the Han Chinese are relatively understudied in population and medical genetics studies. From low-coverage whole-genome sequencing of 11,670 Han Chinese women we present a catalog of 25,057,223 variants, including 548,401 novel variants that are seen at least 10 times in our dataset. Individuals from our study come from 19 out of 22 provinces across China, allowing us to study population structure, genetic ancestry, and local adaptation in Han Chinese. We identify previously unrecognized population structure along the East-West axis of China and report unique signals of admixture across geographical space, such as European influences among the Northwestern provinces of China. Finally, we identified a number of highly differentiated loci, indicative of local adaptation in the Han Chinese. In particular, we detected extreme differentiation among the Han Chinese at MTHFR, ADH7, and FADS loci, suggesting that these loci may not be specifically selected in Tibetan and Inuit populations as previously suggested. On the other hand, we find that Neandertal ancestry does not vary significantly across the provinces, consistent with admixture prior to the dispersal of modern Han Chinese. Furthermore, contrary to a previous report, Neandertal ancestry does not explain a significant amount of heritability in depression. Our findings provide the largest genetic data set so far made available for Han Chinese and provide insights into the history and population structure of the worlds largest ethnic group.

genetics↗