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  • Transcriptomics and chromatin accessibility in multiple African population samples. bioRxiv : the preprint server for biology DeGorter, M. K., Goddard, P. C., Karakoc, E., Kundu, S., Yan, S. M., Nachun, D., Abell, N., Aguirre, M., Carstensen, T., Chen, Z., Durrant, M., Dwaracherla, V. R., Feng, K., Gloudemans, M. J., Hunter, N., Moorthy, M. P., Pomilla, C., Rodrigues, K. B., Smith, C. J., Smith, K. S., Ungar, R. A., Balliu, B., Fellay, J., Flicek, P., McLaren, P. J., Henn, B., McCoy, R. C., Sugden, L., Kundaje, A., Sandhu, M. S., Gurdasani, D., Montgomery, S. B. 2023

    Abstract

    Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

    View details for DOI 10.1101/2023.11.04.564839

    View details for PubMedID 37986808

    View details for PubMedCentralID PMC10659267

  • Author Correction: Africa-specific human genetic variation near CHD1L associates with HIV-1 load. Nature McLaren, P. J., Porreca, I., Iaconis, G., Mok, H. P., Mukhopadhyay, S., Karakoc, E., Cristinelli, S., Pomilla, C., Bartha, I., Thorball, C. W., Tough, R. H., Angelino, P., Kiar, C. S., Carstensen, T., Fatumo, S., Porter, T., Jarvis, I., Skarnes, W. C., Bassett, A., DeGorter, M. K., Sathya Moorthy, M. P., Tuff, J. F., Kim, E. Y., Walter, M., Simons, L. M., Bashirova, A., Buchbinder, S., Carrington, M., Cossarizza, A., De Luca, A., Goedert, J. J., Goldstein, D. B., Haas, D. W., Herbeck, J. T., Johnson, E. O., Kaleebu, P., Kilembe, W., Kirk, G. D., Kootstra, N. A., Kral, A. H., Lambotte, O., Luo, M., Mallal, S., Martinez-Picado, J., Meyer, L., Miro, J. M., Moodley, P., Motala, A. A., Mullins, J. I., Nam, K., Obel, N., Pirie, F., Plummer, F. A., Poli, G., Price, M. A., Rauch, A., Theodorou, I., Trkola, A., Walker, B. D., Winkler, C. A., Zagury, J. F., Montgomery, S. B., Ciuffi, A., Hultquist, J. F., Wolinsky, S. M., Dougan, G., Lever, A. M., Gurdasani, D., Groom, H., Sandhu, M. S., Fellay, J. 2023

    View details for DOI 10.1038/s41586-023-06591-7

    View details for PubMedID 37670157

  • Africa-specific human genetic variation near CHD1L associates with HIV-1 load. Nature McLaren, P. J., Porreca, I., Iaconis, G., Mok, H. P., Mukhopadhyay, S., Karakoc, E., Cristinelli, S., Pomilla, C., Bartha, I., Thorball, C. W., Tough, R. H., Angelino, P., Kiar, C. S., Carstensen, T., Fatumo, S., Porter, T., Jarvis, I., Skarnes, W. C., Bassett, A., DeGorter, M. K., Sathya Moorthy, M. P., Tuff, J. F., Kim, E. Y., Walter, M., Simons, L. M., Bashirova, A., Buchbinder, S., Carrington, M., Cossarizza, A., De Luca, A., Goedert, J. J., Goldstein, D. B., Haas, D. W., Herbeck, J. T., Johnson, E. O., Kaleebu, P., Kilembe, W., Kirk, G. D., Kootstra, N. A., Kral, A. H., Lambotte, O., Luo, M., Mallal, S., Martinez-Picado, J., Meyer, L., Miro, J. M., Moodley, P., Motala, A. A., Mullins, J. I., Nam, K., Obel, N., Pirie, F., Plummer, F. A., Poli, G., Price, M. A., Rauch, A., Theodorou, I., Trkola, A., Walker, B. D., Winkler, C. A., Zagury, J. F., Montgomery, S. B., Ciuffi, A., Hultquist, J. F., Wolinsky, S. M., Dougan, G., Lever, A. M., Gurdasani, D., Groom, H., Sandhu, M. S., Fellay, J. 2023

    Abstract

    HIV-1 remains a global health crisis1, highlighting the need to identify new targets for therapies. Here, given the disproportionate HIV-1 burden and marked human genome diversity in Africa2, we assessed the genetic determinants of control of set-point viral load in 3,879 people of African ancestries living with HIV-1 participating in the international collaboration for the genomics of HIV3. We identify a previously undescribed association signal on chromosome 1 where the peak variant associates with an approximately 0.3 log10-transformed copies per ml lower set-point viral load per minor allele copy and is specific to populations of African descent. The top associated variant is intergenic and lies between a long intergenic non-coding RNA (LINC00624) and the coding gene CHD1L, which encodes a helicase that is involved in DNA repair4. Infection assays in iPS cell-derived macrophages and other immortalized cell lines showed increased HIV-1 replication in CHD1L-knockdown and CHD1L-knockout cells. We provide evidence from population genetic studies that Africa-specific genetic variation near CHD1L associates with HIV replication in vivo. Although experimental studies suggest that CHD1L is able to limit HIV infection in some cell types in vitro, further investigation is required to understand the mechanisms underlying our observations, including any potential indirect effects of CHD1L on HIV spread in vivo that our cell-based assays cannot recapitulate.

    View details for DOI 10.1038/s41586-023-06370-4

    View details for PubMedID 37532928

    View details for PubMedCentralID 3723635

  • Multiple causal variants underlie genetic associations in humans. Science (New York, N.Y.) Abell, N. S., DeGorter, M. K., Gloudemans, M. J., Greenwald, E., Smith, K. S., He, Z., Montgomery, S. B. 2022; 375 (6586): 1247-1254

    Abstract

    Associations between genetic variation and traits are often in noncoding regions with strong linkage disequilibrium (LD), where a single causal variant is assumed to underlie the association. We applied a massively parallel reporter assay (MPRA) to functionally evaluate genetic variants in high, local LD for independent cis-expression quantitative trait loci (eQTL). We found that 17.7% of eQTLs exhibit more than one major allelic effect in tight LD. The detected regulatory variants were highly and specifically enriched for activating chromatin structures and allelic transcription factor binding. Integration of MPRA profiles with eQTL/complex trait colocalizations across 114 human traits and diseases identified causal variant sets demonstrating how genetic association signals can manifest through multiple, tightly linked causal variants.

    View details for DOI 10.1126/science.abj5117

    View details for PubMedID 35298243

  • Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa. Cell Gurdasani, D., Carstensen, T., Fatumo, S., Chen, G., Franklin, C. S., Prado-Martinez, J., Bouman, H., Abascal, F., Haber, M., Tachmazidou, I., Mathieson, I., Ekoru, K., DeGorter, M. K., Nsubuga, R. N., Finan, C., Wheeler, E., Chen, L., Cooper, D. N., Schiffels, S., Chen, Y., Ritchie, G. R., Pollard, M. O., Fortune, M. D., Mentzer, A. J., Garrison, E., Bergstrom, A., Hatzikotoulas, K., Adeyemo, A., Doumatey, A., Elding, H., Wain, L. V., Ehret, G., Auer, P. L., Kooperberg, C. L., Reiner, A. P., Franceschini, N., Maher, D. P., Montgomery, S. B., Kadie, C., Widmer, C., Xue, Y., Seeley, J., Asiki, G., Kamali, A., Young, E. H., Pomilla, C., Soranzo, N., Zeggini, E., Pirie, F., Morris, A. P., Heckerman, D., Tyler-Smith, C., Motala, A., Rotimi, C., Kaleebu, P., Barroso, I., Sandhu, M. S. 2019; 179 (4): 984

    Abstract

    Genomic studies in African populations provide unique opportunities to understand disease etiology, human diversity, and population history. In the largest study of its kind, comprising genome-wide data from 6,400 individuals and whole-genome sequences from 1,978 individuals from rural Uganda, we find evidence of geographically correlated fine-scale population substructure. Historically, the ancestry of modern Ugandans was best represented by a mixture of ancient East African pastoralists. We demonstrate the value of the largest sequence panel from Africa to date as an imputation resource. Examining 34 cardiometabolic traits, we show systematic differences in trait heritability between European and African populations, probably reflecting the differential impact of genes and environment. In a multi-trait pan-African GWAS of up to 14,126 individuals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic traits. We find that several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

    View details for DOI 10.1016/j.cell.2019.10.004

    View details for PubMedID 31675503

  • Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans. Genome medicine Gottlieb, A. n., Daneshjou, R. n., DeGorter, M. n., Bourgeois, S. n., Svensson, P. J., Wadelius, M. n., Deloukas, P. n., Montgomery, S. B., Altman, R. B. 2017; 9 (1): 98

    Abstract

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

    View details for PubMedID 29178968

  • FIRE: functional inference of genetic variants that regulate gene expression. Bioinformatics (Oxford, England) Ioannidis, N. M., Davis, J. R., DeGorter, M. K., Larson, N. B., McDonnell, S. K., French, A. J., Battle, A. J., Hastie, T. J., Thibodeau, S. N., Montgomery, S. B., Bustamante, C. D., Sieh, W. n., Whittemore, A. S. 2017; 33 (24): 3895–3901

    Abstract

    Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies.We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types.FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/.nilah@stanford.edu.Supplementary data are available at Bioinformatics online.

    View details for PubMedID 28961785

  • A TNFRSF14-Fc epsilon RI-mast cell pathway contributes to development of multiple features of asthma pathology in mice NATURE COMMUNICATIONS Sibilano, R., Gaudenzio, N., DeGorter, M. K., Reber, L. L., Hernandez, J. D., Starkl, P. M., Zurek, O. W., Tsai, M., Zahner, S., Montgomery, S. B., Roers, A., Kronenberg, M., Yu, M., Galli, S. J. 2016; 7

    Abstract

    Asthma has multiple features, including airway hyperreactivity, inflammation and remodelling. The TNF superfamily member TNFSF14 (LIGHT), via interactions with the receptor TNFRSF14 (HVEM), can support TH2 cell generation and longevity and promote airway remodelling in mouse models of asthma, but the mechanisms by which TNFSF14 functions in this setting are incompletely understood. Here we find that mouse and human mast cells (MCs) express TNFRSF14 and that TNFSF14:TNFRSF14 interactions can enhance IgE-mediated MC signalling and mediator production. In mouse models of asthma, TNFRSF14 blockade with a neutralizing antibody administered after antigen sensitization, or genetic deletion of Tnfrsf14, diminishes plasma levels of antigen-specific IgG1 and IgE antibodies, airway hyperreactivity, airway inflammation and airway remodelling. Finally, by analysing two types of genetically MC-deficient mice after engrafting MCs that either do or do not express TNFRSF14, we show that TNFRSF14 expression on MCs significantly contributes to the development of multiple features of asthma pathology.

    View details for DOI 10.1038/ncomms13696

    View details for Web of Science ID 000389853400001

    View details for PubMedID 27982078

    View details for PubMedCentralID PMC5171877