Biology subjects

Biddanda, A.

Publications and source records attributed to Biddanda, A..

14 recordsLinked to original sources

bioRxiv2026

Gene flux shapes diversity and evolution of the ancient 17q21.31 inversion polymorphism

A hallmark of chromosomal inversions is that they suppress recombination between haplotypes, allowing inversion haplotypes to persist as single co-inherited units. To determine the extent to which inversions nevertheless permit genetic exchange, we investigated a common 979-kb inversion polymorphism at the human 17q21.31 locus. This locus exhibits deep divergence between the reference (H1) and inverted (H2) haplotypes, extensive segmental duplications (SDs) flanking the inversion, and association with neurodegenerative diseases, developmental disorders, and fertility-related phenotypes. Using single-cell sperm genome sequencing data, we directly measured recombination rates between H1 and H2 haplotypes and found near-complete suppression of single-crossover events between the haplotypes. The rare single crossovers that did occur were mediated by non-allelic homologous recombination between shared H1 and H2 SDs, generating novel duplication architectures. In contrast, two-switch events consistent with gene conversion or double crossovers, spanning 17-150 kb, occurred throughout the inversion at rates exceeding genome-wide estimates for events of comparable size. Consistent with recurring genetic exchange, we identified 99 distinct H1-H2 recombinant haplotypes segregating in All of Us genomes, including 26 with combinations of H1 and H2 SDs. These recombinant haplotypes facilitated dissection of the inversion's effects on fertility-related phenotypes; using a large parent-embryo dataset, we found that H2 additively increases female crossover rates across chromosomes and that KANSL1 duplications do not explain this effect. Finally, ancestral recombination graphs dated H1-H2 gene flux (the exchange of genetic material between alternative arrangements) to approximately 100-500 thousand years ago, revealing that H1 and H2 haplotypes have co-segregated for at least half a million years. Together, these results demonstrate that inversions can be permeable barriers to recombination, with ongoing gene flux influencing the diversity and evolution of inversion polymorphisms.

genomics↗

bioRxiv2026

Recovering signatures of archaic introgression using ancestral recombination graphs

Neanderthal and Denisovan genomes have reshaped our understanding of archaic introgression. Yet, the limited number of archaic genomes sequenced and the reliance on unadmixed outgroups have left much of this history unresolved. We introduce TRACE, a method to identify archaic ancestry using features of ancestral recombination graphs inferred from contemporary genomes alone. Simulations show that TRACE reliably detects archaic introgression without requiring archaic genomes or unadmixed outgroups. Applied to 1000 Genomes data, TRACE recovers known Neanderthal and Denisovan introgression and reveals signals of ghost admixture from previously uncharacterized hominins in both Africans and non-Africans. Strikingly, ghost ancestry persists in Neanderthal and Denisovan ancestry deserts, challenging their interpretation as Homo sapiens-specific regions. In Oceanians, TRACE finds deep lineages enriched in Denisovan--and not Neanderthal--regions, supporting a model of super-archaic gene flow. TRACE provides a scalable framework for mapping the legacy of archaic introgression in the absence of archaic genome sequences.

evolutionary biology↗

bioRxiv2025

Assembling unmapped reads reveals hidden variation in South Asian genomes

Conventional genome mapping-based approaches systematically overlook genetic variation, particularly in regions that substantially differ from the reference. To explore this hidden variation, we examined unmapped and poorly mapped reads from the genomes of 640 human individuals from South Asian (SAS) populations in the 1000 Genomes Project and the Simons Genome Diversity Project. We assembled tens of megabases of non-redundant sequence in tens of thousands of large contigs, a significant portion of which is present in both SAS and non-SAS populations. We demonstrated that much of this sequence is not discovered by traditional variant discovery approaches even when using complete genomes and pangenomes. Across 20,000 placed contigs, we found 8,215 intersections with 106 protein coding genes and >15,000 placements within 1 kbp of a known GWAS hit. We used long read data from a subset of samples to validate the majority of their assembled sequences, aligned RNA-seq data to identify hundreds of unplaced contigs with transcriptional potential, and queried existing nucleotide databases to infer the origins of the remaining unplaced sequences. Our results highlight the limitations of even the most complete reference genomes and provide a model for understanding the distribution of hidden variation in any human population.

genomics↗

bioRxiv2024

Study design and the sampling of deleterious rare variants in biobank-scale datasets

One key component of study design in population genetics is the "geographic breadth" of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number variants as compared geographically narrow samples (an effect we label "discovery"); though the variants will be detected at lower average frequency than in narrow samples (e.g. as singletons, an effect we label "dilution"). Importantly, these effects are amplified for larger sample sizes and moderated by the magnitude of fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale. SignificanceAs genetic studies grow, researchers are increasingly seeking to identify rare genetic variants with large impacts on traits. In this paper, we combine theoretical methods and data analysis to show how differences in sampling with respect to geographic location can influence the number and frequency of genetic variants that are found. Our results suggest that geographically broad samples will include more distinct genetic variants, though each variant will be found at a lower frequency, as compared to geographically narrow samples. Our results can help researchers to consider the implications of study design on expected results when constructing new genetic samples.

genetics↗

bioRxiv2024

Complete sequencing of ape genomes

We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives.

genomics↗

bioRxiv2023

The Complete Sequence and Comparative Analysis of Ape Sex Chromosomes

Apes possess two sex chromosomes--the male-specific Y and the X shared by males and females. The Y chromosome is crucial for male reproduction, with deletions linked to infertility1. The X chromosome carries genes vital for reproduction and cognition2. Variation in mating patterns and brain function among great apes suggests corresponding differences in their sex chromosomes. However, due to their highly repetitive nature and incomplete reference assemblies, ape sex chromosomes have been challenging to study. Here, using the methodology developed for the telomere-to-telomere (T2T) human genome, we produced gapless assemblies of the X and Y chromosomes for five great apes (chimpanzee, bonobo, gorilla, Bornean and Sumatran orangutans) and a lesser ape, the siamang gibbon. These assemblies allowed us to untangle the intricacies of ape sex chromosome evolution. We found that, compared to the Xs, the ape Ys vary greatly in size and have low alignability and high levels of structural rearrangements. This divergence on the Y arises from the accumulation of lineage-specific ampliconic regions, palindromes, transposable elements, and satellites. Our analysis of Y chromosome genes revealed expansions of multi-copy gene families and signatures of purifying selection. Thus, the Y exhibits dynamic evolution, while the X is more stable. Mapping short-read sequencing data to these assemblies revealed diversity and selection patterns on sex chromosomes of >100 great ape individuals. These reference assemblies are expected to inform human evolution and conservation genetics of nonhuman apes, all of which are endangered species.

genomics↗

bioRxiv2023

Sources of gene expression variation in a globally diverse cohort

Abstract/SummaryGenetic variation influencing gene expression and splicing is a key source of phenotypic diversity. Though invaluable, studies investigating these links in humans have been strongly biased toward participants of European ancestries, diminishing generalizability and hindering evolutionary research. To address these limitations, we developed MAGE, an open-access RNA-seq data set of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, mirroring variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-eQTLs and cis-sQTLs, respective), identifying >15,000 putatively causal eQTLs and >16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1310 eQTLs and 1657 sQTLs that are largely private to previously underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations and that apparent "population-specific" effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands understanding of gene expression diversity across human populations and provides an inclusive resource for studying the evolution and function of human genomes.

genomics↗

bioRxiv2022

Whole-genome sequencing across 449 samples spanning 47 ethnolinguistic groups provides insights into genetic diversity in Nigeria

African populations have been drastically underrepresented in genomics research and failure to capture the genetic diversity across the numerous ethnolinguistic groups (ELGs) found in the continent has hindered the equity of precision medicine initiatives globally. Here, we describe the whole-genome sequencing of 449 Nigerian individuals across 47 unique self-reported ELGs. Population structure analysis reveals genetic differentiation amongst our ELGs, consistent with previous findings. From the 36 million SNPs and INDELs discovered in our dataset, we provide a high-level catalog of both novel and medically-relevant variation present across the ELGs. These results emphasize the value of this resource for genomics research, with added granularity by representing multiple ELGs from Nigeria. Our results also underscore the potential of using these cohorts with larger sample sizes to improve our understanding of human ancestry and health in Africa.

genomics↗

bioRxiv2022

A survey of proteomic variation across two ethnic groups in Nigeria and its relationship to obesity risk

Proteomic variation between individuals has immense potential for identifying novel drug targets and disease mechanisms. However, with high-throughput proteomic technologies still in their infancy, they have largely been applied in large majority European ancestry cohorts (e.g. the UK Biobank). An open question is the degree to which proteomic signatures seen in European and other groups mirror those seen in diverse populations, such as cohorts from Africa. Coupled with genetic information, we can also gain a better understanding of the role of genetic variants in the regulation of the proteome and subsequent disease mechanisms. To address the gap in our understanding of proteomic variation in individuals of African ancestry, we collected proteomic data from 176 individuals across two ethnic groups (Igbo and Yoruba) in Nigeria. These individuals were also stratified into high BMI (BMI > 30 kg/m2) and normal BMI (20 kg/m2 < BMI < 30 kg/m2) categories. We characterized differences in plasma protein abundance using the Olink Explore 1536 panel between high and normal BMI individuals, finding strong associations consistent with previously known signals in individuals of European descent. We additionally found 73 sentinel cis-pQTL in this dataset, with 21 lead cis-pQTL not observed in catalogs of variation from European-ancestry individuals. In summary, our study highlights the value of leveraging proteomic data in cohorts of diverse ancestry for investigating trait-specific mechanisms and discovering novel genetic regulators of the plasma proteome.

genomics↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

Integrating genetic and oral histories of Southwest Indian populations

Over the past decade, genomic data has contributed to several insights on global human population histories. These studies have been met both with interest and critically, particularly by populations with oral histories that are records of their past and often reference their origins. While several studies have reported concordance between oral and genetic histories, there is potential for tension that may stem from genetic histories being prioritized or used to confirm community-based knowledge and ethnography, especially if they differ. To investigate the interplay between oral and genetic histories, we focused on the southwestern region of India and analyzed whole-genome sequence data from 158 individuals identifying as Bunt, Kodava, Nair, and Kapla. We supplemented limited anthropological records on these populations with oral history accounts from community members and historical literature, focusing on references to non-local origins such as the ancient Scythians in the case of Bunt, Kodava, and Nair, members of Alexander the Greats army for the Kodava, and an African-related source for Kapla. We found these populations to be genetically most similar to other Indian populations, with the Kapla more similar to South Indian tribal populations that maximize a genetic ancestry related to Andaman Islanders. We did not find evidence of additional genetic sources in the study populations than those known to have contributed to many other present-day South Asian populations. Our results demonstrate that oral and genetic histories may not always provide consistent accounts of population origins and motivate further community-engaged, multi-disciplinary investigations of non-local origin stories in these communities.

genetics↗

bioRxiv2021

Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits

Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from [~]65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3x) and loss-of-function (4.5x) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

genetics↗

bioRxiv2021

Properties of Two-Locus Genealogies and Linkage Disequilibrium in Temporally Structured Samples

Archaeogenetics has been revolutionary, revealing insights into demographic history and recent positive selection in many organisms. However, most studies to date have ignored the non-random association of genetic variants at different loci (i.e., linkage disequilibrium, LD). This may be in part because basic properties of LD in samples from different times are still not well understood. Here, we derive several results for summary statistics of haplotypic variation under a model with time-stratified sampling: 1) The correlation between the number of pairwise differences observed between time-staggered samples ({pi}{Delta}t) in models with and without strict population continuity; 2) The product of the LD coefficient, D, between ancient and modern samples, which is a measure of haplotypic similarity between modern and ancient samples; and 3) The expected switch rate in the Li and Stephens haplotype copying model. The latter has implications for genotype imputation and phasing in ancient samples with modern reference panels. Overall, these results provide a characterization of how haplotype patterns are affected by sample age, recombination rates, and population sizes. We expect these results will help guide the interpretation and analysis of haplotype data from ancient and modern samples.

genetics↗

bioRxiv2020

Geographic patterns of human allele frequency variation: a variant-centric perspective

A key challenge in human genetics is to describe and understand the distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, in each case drawing inferences over a large number of variants. Here, we present an alternative representation of human genetic variation that reveals the relative abundance of different allele frequency patterns across populations. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are common globally, regardless of whether the individuals are from the same region or different regions. To guide interpretation of the results, we also apply the visualization to contrasting theoretical scenarios with varying levels of divergence and gene flow. Our variant-centric visualization clarifies the major geographic patterns of human variation and can be used to help correct potential misconceptions about the extent and nature of genetic differentiation among populations.

evolutionary biology↗