Biology subjects

Bustamante, C. D.

Publications and source records attributed to Bustamante, C. D..

At least 19 recordsLinked to original sources

bioRxiv2024

Diverse ancestral representation improves genetic intolerance metrics

Rapidly expanding genomic databases have enabled the identification of regions in the human genome intolerant to variation and thus likely relevant to human disease. However, despite their unprecedented scale, these datasets remain constrained by limited ancestral diversity. Here, we systematically evaluate how genetic diversity impacts gene- and sub-genic intolerance metrics by analyzing whole-exome sequencing data from 460,551 UK Biobank participants and 125,748 gnomAD participants across diverse ancestral backgrounds. Through comprehensive analysis of randomly sampled datasets with varying ancestral compositions, we demonstrate that genetic diversity, rather than sample size alone, drives the performance of intolerance metrics in identifying functionally critical genes and genic sub-regions. Notably, scores trained on variation observed in African and Admixed American ancestral groups showed superior resolution in detecting neurodevelopmental disease risk genes and haploinsufficient genes compared to scores trained on variation observed in European ancestry cohorts. Most strikingly, the Missense Tolerance Ratio (MTR) trained on 43,000 multi-ancestry exomes demonstrated greater predictive power than when trained on a nearly 10-fold larger dataset of 440,000 non-Finnish European exomes, indicating that European ancestry-based scores are approaching saturation. These findings establish ancestral diversity as the fundamental determinant for advancing intolerance metrics and underscore the urgent scientific and ethical imperative for enhanced population representation in genomic resources to fully realize the potential of precision medicine and drug discovery.

genetics↗

bioRxiv2021

Archetypal Analysis for Population Genetics

The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in this fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids. Author summaryThis work introduces a method that combines the singular value decomposition (SVD) with Archetypal Analysis to perform fast and accurate genetic clustering by first reducing the dimensionality of the space of genomic sequences. Each sequence is described as a convex combination (admixture) of archetypes (cluster representatives) in the reduced dimensional space. We compare this interpretable approach to the widely used genetic clustering algorithm, ADMIXTURE, and show that, without significant degradation in performance, Archetypal Analysis outperforms, offering shorter run times and representational advantages. We include theoretical, qualitative, and quantitative comparisons between both methods.

bioinformatics↗

bioRxiv2021

High Resolution Ancestry Deconvolution for Next Generation Genomic Data

As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individuals genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

genomics↗

bioRxiv2019

Ancient DNA reconstructs the genetic legacies of pre-contact Puerto Rico communities

Indigenous peoples have occupied the island of Puerto Rico since at least 3000 B.C. Due to the demographic shifts that occurred after European contact, the origin(s) of these ancient populations, and their genetic relationship to present-day islanders, are unclear. We use ancient DNA to characterize the population history and genetic legacies of pre-contact Indigenous communities from Puerto Rico. Bone, tooth and dental calculus samples were collected from 124 individuals from three pre-contact archaeological sites: Tibes, Punta Candelero and Paso del Indio. Despite poor DNA preservation, we used target enrichment and high-throughput sequencing to obtain complete mitochondrial genomes (mtDNA) from 45 individuals and autosomal genotypes from two individuals. We found a high proportion of Native American mtDNA haplogroups A2 and C1 in the pre-contact Puerto Rico sample (40% and 44%, respectively). This distribution, as well as the haplotypes represented, support a primarily Amazonian South American origin for these populations, and mirrors the Native American mtDNA diversity patterns found in present-day islanders. Three mtDNA haplotypes from pre-contact Puerto Rico persist among Puerto Ricans and other Caribbean islanders, indicating that present-day populations are reservoirs of pre-contact mtDNA diversity. Lastly, we find similarity in autosomal ancestry patterns between pre-contact individuals from Puerto Rico and the Bahamas, suggesting a shared component of Indigenous Caribbean ancestry with close affinity to South American populations. Our findings contribute to a more complete reconstruction of pre-contact Caribbean population history and explore the role of Indigenous peoples in shaping the biocultural diversity of present-day Puerto Ricans and other Caribbean islanders.

genomics↗

bioRxiv2019

Population history and gene divergence in Native Mexicans inferred from 76 human exomes

Native American genetic variation remains underrepresented in most catalogs of human genome sequencing data. Previous genotyping efforts have revealed that Mexicos indigenous population is highly differentiated and substructured, thus potentially harboring higher proportions of private genetic variants of functional and biomedical relevance. Here we have targeted the coding fraction of the genome and characterized its full site frequency spectrum by sequencing 76 exomes from five indigenous populations across Mexico. Using diffusion approximations, we modeled the demographic history of indigenous populations from Mexico with northern and southern ethnic groups splitting 7.2 kya and subsequently diverging locally 6.5 kya and 5.7 kya, respectively. Selection scans for positive selection revealed BCL2L13 and KBTBD8 genes as potential candidates for adaptive evolution in Raramuris and Triquis, respectively. BCL2L13 is highly expressed in skeletal muscle and could be related to physical endurance, a well-known phenotype of the northern Mexico Raramuri. The KBTBD8 gene has been associated with idiopathic short stature and we found it to be highly differentiated in Triqui, a southern indigenous group from Oaxaca whose height is extremely low compared to other native populations.

genomics↗

bioRxiv2018

Mitogenomes illuminate the origin and migration patterns of the indigenous people of the Canary Islands

The Canary Islands indigenous people have been the subject of substantial archaeological, anthropological, linguistic and genetic research pointing to a most probable North African Berber source. However, neither agreement about the exact point of origin nor a model for the indigenous colonization of the islands has been established. To shed light on these questions, we analyzed 48 ancient mitogenomes from 25 archaeological sites from the seven main islands. Most lineages observed in the ancient samples have a Mediterranean distribution, and belong to lineages associated with the Neolithic expansion in the Near East and Europe (T2c, J2a, X3a...). This phylogeographic analysis of Canarian indigenous mitogenomes, the first of its kind, shows that some lineages are restricted to Central North Africa (H1cf, J2a2d and T2c1d3), while others have a wider distribution, including both West and Central North Africa, and, in some cases, Europe and the Near East (U6a1a1, U6a7a1, U6b, X3a, U6c1). In addition, we identify four new Canarian-specific lineages (H1e1a9, H4a1e, J2a2d1a and L3b1a12) whose coalescence dates correlate with the estimated time for the colonization of the islands (1st millennia CE). Additionally, we observe an asymmetrical distribution of mtDNA haplogroups in the ancient population, with certain haplogroups appearing more frequently in the islands closer to the continent. This reinforces results based on modern mtDNA and Y-chromosome data, and archaeological evidence suggesting the existence of two distinct migrations. Comparisons between insular populations show that some populations had high genetic diversity, while others were probably affected by genetic drift and/or bottlenecks. In spite of observing interinsular differences in the survival of indigenous lineages, modern populations, with the sole exception of La Gomera, are homogenous across the islands, supporting the theory of extensive human mobility after the European conquest.

genetics↗

bioRxiv2018

SNAPPY: Single Nucleotide Assignment of Phylogenetic Parameters on the Y chromosome

SummaryThe assignment of Y chromosome data to related clusters, or haplogroups, is a common application in human population genetics. To enable this at scale, we developed SNAPPY. SNAPPY is a software program used to assign Y-chromosome phylogeny-informed haplotypes using dense genotype data. The program efficiently tests all haplotypes in a provided Y-chromosome database to find the haplogroup that is best supported by the input genotypes. Importantly, the method considers both the amount of support for the specific haplogroup, as well as its ancestral haplogroups via parsimony. This accounts for the underlying genealogy the haplotypes represent, strengthening the accuracy of the assignments. SNAPPY is fast, scalable, and uses standard file formats, making it easy to integrate into analytical pipelines.\n\nAvailability and ImplementationThe program is implemented in python. The program, a user manual, haplotype databases, and test datasets are available for download at github.com/chrisgene/snappy.\n\nContactJonathan.shortt@ucdenver.edu, Chris.gignoux@ucdenver.edu

bioinformatics↗

bioRxiv2018

Caring without sharing: Meta-analysis 2.0 for massive genome-wide association studies

Genome-wide association studies have been effective at revealing the genetic architecture of simple traits. Extending this approach to more complex phenotypes has necessitated a massive increase in cohort size. To achieve sufficient power, participants are recruited across multiple collaborating institutions, leaving researchers with two choices: either collect all the raw data at a single institution or rely on meta-analyses to test for association. In this work, we present a third alternative. Here, we implement an entire GWAS workflow (quality control, population structure control, and association) in a fully decentralized setting. Our iterative approach (a) does not rely on consolidating the raw data at a single coordination center, and (b) does not hinge upon large sample size assumptions at each silo. As we show, our approach overcomes challenges faced by meta-studies when it comes to associating rare alleles and when case/control proportions are wildly imbalanced at each silo. We demonstrate the feasibility of our method in cohorts ranging in size from 2K (small) to 500K (large), and recruited across 2 to 10 collaborating institutions.

bioinformatics↗

bioRxiv2018

Deep learning facilitates rapid cohort identification using human and veterinary clinical narratives

ObjectiveCurrently, dedicated tagging staff spend considerable effort assigning clinical codes to patient summaries for public health purposes, and machine-learning automated tagging is bottlenecked by availability of electronic medical records. Veterinary medical records, a largely untapped data source that could benefit both human and non-human patients, could fill the gap. Materials and MethodsIn this retrospective study, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We established relevant baselines by training Decision Trees (DT) and Random Forests (RF) on the same data. We finally investigated the effect of merging data across clinical settings and probed model portability. ResultsWe show that the LSTM-RNNs accurately classify veterinary/human text narratives into top-level categories with an average weighted macro F1, score of 0.735/0.675 respectively. The evaluation metric for the LSTM was 7 and 8% higher than that of the DT and RF models respectively. We generally did not find evidence of model portability albeit moderate performance increases in select categories. DiscussionWe see a strong positive correlation between number of training samples and classification performance, which is promising for future efforts. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort selection, which could in turn better address emerging public health concerns. ConclusionDigitization of human and veterinary health information will continue to be a reality. Our approach is a step forward for these two domains to learn from, and inform, one another.

epidemiology↗

bioRxiv2018

Standardized biogeographic grouping system for annotating populations in pharmacogenetic research

The varying frequencies of pharmacogenetic alleles between populations have important implications for the impact of these alleles in different populations. Current population grouping methods to communicate these patterns are insufficient as they are inconsistent and fail to reflect the global distribution of genetic variability. To facilitate and standardize the reporting of variability in pharmacogenetic allele frequencies, we present seven geographically-defined groups: American, Central/South Asian, East Asian, European, Near Eastern, Oceanian, and Sub-Saharan African, and two admixed groups: African American/Afro-Caribbean and Latino. These nine groups are defined by global autosomal genetic structure and based on data from large-scale sequencing initiatives. We recognize that broadly grouping global populations is an oversimplification of human diversity and does not capture complex social and cultural identity. However, these groups meet a key need in pharmacogenetics research by enabling consistent communication of the scale of variability in global allele frequencies and are now used by PharmGKB.

genetics↗

bioRxiv2018

The Clinical Imperative for Inclusivity: Race, Ethnicity, and Ancestry (REA) in Genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: 1) acquisition of REA data via clinical laboratory requisition forms, and 2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites as determined by variants in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about variants at clinically relevant sites in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with \"pathogenic\" and \"likely pathogenic\" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed and adopted across clinical genomics.

genomics↗

bioRxiv2018

Structural variation detection by proximity ligation from FFPE tumor tissue

The clinical management and therapy of many solid tumor malignancies is dependent on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call \"Fix-C\", which is similar in concept to Hi-C. Fix-C enables structural variation detection from fresh and archival FFPE samples. We applied this method to 15 clinical adenocarcinoma and sarcoma specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with FISH assays - the current clinical gold standard. Additionally, we are able to identify novel structural variation undetected by other methods and recover long-range chromatin configuration information from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material, and inform the development of targeted molecular diagnostic assays for patient care.

genomics↗

bioRxiv2017

Genetic architecture drives seasonal onset of hibernation in the 13-lined ground squirrel

Hibernation is a highly dynamic phenotype whose timing, for many mammals, is controlled by a circannual clock and accompanied by rhythms in body mass and food intake. When housed in an animal facility, 13-lined ground squirrels exhibit individual variation in the seasonal onset of hibernation, which is not explained by environmental or biological factors, such as body mass and sex. We hypothesized that underlying genetic architecture instead drives variation in this timing. After first increasing the contiguity of the genome assembly, we therefore employed a genotype-by-sequencing approach to characterize genetic variation in 153 13-lined ground squirrels. Combining this with datalogger records, we estimated high heritability (61-100%) for the seasonal onset of hibernation. After applying a genome-wide scan with 46,996 variants, we also identified 21 loci significantly associated with hibernation immergence, which alone accounted for 54% of the variance in the phenotype. The most significant marker (SNP 15, p=3.81x10-6) was located near prolactin-releasing hormone receptor (PRLHR), a gene that regulates food intake and energy homeostasis. Other significant loci were located near genes functionally related to hibernation physiology, including muscarinic acetylcholine receptor M2 (CHRM2), involved in the control of heart rate, exocyst complex component 4 (EXOC4) and prohormone convertase 2 (PCSK2), both of which are involved in insulin signaling and processing. Finally, we applied an expression quantitative loci (eQTL) analysis using existing transcriptome datasets, and we identified significant (q<0.1) associations for 9/21 variants. Our results highlight the power of applying a genetic mapping strategy to hibernation and present new insight into the genetics driving its seasonal onset.

genetics↗

bioRxiv2017

In-solution Y-chromosome capture-enrichment on ancient DNA libraries

BackgroundAs most ancient biological samples have low levels of endogenous DNA, it is advantageous to enrich for specific genomic regions prior to sequencing. One approach - in-solution capture-enrichment - retrieves sequences of interest and reduces the fraction of microbial DNA. In this work, we implement a capture-enrichment approach targeting informative regions of the Y chromosome in six human archaeological remains excavated in the Caribbean and dated between 200 and 3,000 years BP. We compare the recovery rate of Y-chromosome capture (YCC) alone, whole-genome capture followed by YCC (WGC+Y) versus non-enriched (pre-capture) libraries.\n\nResultsWe recovered 17-4,152 times more targeted unique Y-chromosome sequences after capture, where 0.01-6.2% (WGC+Y) and 0.01-23.5% (YCC) of the sequence reads were on-target, compared to 0.0002-0.004% pre-capture. In samples with endogenous DNA content greater than 0.1%, we found that WGC followed by YCC (WGC+Y) yields lower enrichment due to the loss of complexity in consecutive capture experiments, whereas in samples with lower endogenous content, WGC+Y yielded greater enrichment than YCC alone. Finally, increasing recovery of informative sites enabled us to assign Y-chromosome haplogroups to some of the archeological remains and gain insights about their paternal lineages and origins.\n\nConclusionsWe present to our knowledge the first in-solution capture-enrichment method targeting the human Y-chromosome in aDNA sequencing libraries. YCC and WGC+Y enrichments lead to an increase in the amount of Y-DNA sequences, as compared to libraries not enriched for the Y-chromosome. Our probe design effectively recovers regions of the Y-chromosome bearing phylogenetically informative sites, allowing us to identify paternal lineages with less sequencing than needed for pre-capture libraries. Finally we recommend considering the endogenous content in the experimental design and avoiding consecutive rounds of capture for low-complexity libraries, as clonality increases considerably with each round.

genomics↗

bioRxiv2017

Genomic insights into the domestication of the chocolate tree, Theobroma cacao L.

Domestication has had a strong impact on the development of modern societies. We sequenced 200 genomes of the chocolate plant Theobroma cacao L. to show for the first time that a single population underwent strong domestication approximately 3,600 years (95% CI: 2481 - 10,903 years ago) ago, the Criollo population. We also show that during the process of domestication, there was strong selection for genes involved in the metabolism of the colored protectants anthocyanins and the stimulant theobromine, as well as disease resistance genes. Our analyses show that domesticated populations of T. cacao (Criollo) maintain a higher proportion of high frequency deleterious mutations. We also show for the first time the negative consequences the increase accumulation of deleterious mutations during domestication on the fitness of individuals (significant negative correlation between Criollo ancestry and Kg of beans per hectare per year, P = 0.000425).

genomics↗

bioRxiv2017

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes

Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.

genomics↗

bioRxiv2017

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans

Fewer than 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed with genetic architecture varying by latitude. We investigate polygenicity in the Khoe and the San, populations indigenous to southern Africa, who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but that known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13 using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.\n\nHighlightsO_LISkin pigmentation in Africans is far more polygenic than light skin pigmentation in Eurasians.\nC_LIO_LIKhoeSan[§] populations, which diverged early in human prehistory from other populations, have lightened skin pigmentation compared to equatorial Africans.\nC_LIO_LISkin color is highly heritable in the KhoeSan, but pigmentation variability is not well explained by previously discovered pigmentation genes.\nC_LIO_LIWe perform the first GWAS for pigmentation in African KhoeSan populations and identify canonical pigmentation loci near TYRP1 and in SLC24A5, as well as novel associations surrounding SMARCA2 and other genes.\nC_LI

genetics↗

bioRxiv2017

Therapeutic importance of timely immunophenotyping of breast cancer in a resource-constrained setting: a retrospective hospital-based cohort study

BackgroundOrganizations that issue guidance on breast cancer recommend the use of immunohistochemistry (IHC) for providing appropriate and precise care. However, little focus has been directed to the identification of maximum allowable turnaround times for IHC, which is necessary given the diversity of hospital settings in the world. Much less effort has been committed to the development of digital tools that allow hospital administrators to monitor service utilization histories of their patients.\n\nMethodsIn this retrospective cohort study, we reviewed electronic and paper medical records of all suspected breast cancer patients treated at one secondary-care hospital of the Mexican Institute of Social Security (IMSS), located in western Mexico. We then followed three years of medical history of those patients with IHC testing.\n\nResultsIn 2014, there were 402 breast cancer patients, of which 30 were tested for some IHC biomarker (ER, PR, HER2). The subtyping allowed doctors to adjust (56.7 %) or confirm (43.3 %) the initial therapeutic regimen. The average turnaround time was 56 days. Opportune IHC testing was found to be beneficial when it was available before or during the first rounds of chemotherapy.\n\nConclusionsThe use of data mining tools applied to health record data revealed that there is an association between timely immunohistochemistry and improved outcomes in breast cancer patients. Based on this finding, inclusion of turnaround time in clinical guidelines is recommended. As much of the health data in the country becomes digitized, our visualization tools allow a digital dashboard of the hospital service utilization histories.

epidemiology↗