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

Author

Osifo, Ogochukwu Francis, Moddibo, Zahra Isa, Ado-Wanka, Aisha Nabila, Yakubu, Aminu, Oyedele, Olubukunola, Popoola, Jumi, Attipoe, Delali Attiogbe, Eze-Echesi, Golibe, Modibbo, Fatima Z., Ado-Wanka, Nabila, Osakwe, Oluyemisi, Braimah, Onome, Julius-Enigimi, Eramoh, Akindigh, Terver Mark, Kusimo, Bolutife, Akpulu, Chinenye, Nwuba, Chiamaka, Ebong, Ofonime, Anyika, Chinyere, Adewunmi, Oluwatimilehin, Ibrahim, Yusuf, Kashimawo, Janet, Nkwocha, Chidi, Iyitor, Peter, Abiwon, Temi, Adeleye, Adeola, Ode, Abayomi, Ayo-Lawal, Anjola, Akpabio, Kasiena, Edu, Emame, Njoku, Chiemela, Ballew, Bari, Palmer, Cameron, Joshi, Esha, Biddanda, Arjun, O’Dushlaine, Colm, Ene-Obong, Abasi, Bost, Teresia L., Fatumo, Segun, Musa, Abdullahi, Mujtaba, Abdulrasheed M., Popoola, Abiodun, Bello, Abubakar M., Anyanwu, Anthony, Yusuf, Ashiru, Bozimo, Gesiye E.L., Bassey, Goddy, Bala, Hadiza, Bosan, Istifanus Bala, Edah, Jemimah, Jimoh, Mutiu Alani, Nwankwo, Kenneth, Ojo, Olalekan, Inyama, Marcus, Apanpa, Maryam, Mustapha, Mohammed Inuwa, Ali-Gombe, Musa, Ojo, Olubukola, Adeyemi, Oludare F., Ajayi, Samuel, Bala, Sanusi, Ojo, Temitope, Aliyu, Usman Malami, Raji, Yemi, Tanko, Zainab, Mohammed, Amina, Oladele, David, Hamzat, Muhammed, Agaba, Emmanuel, Nwankwo, Emeka, Ulasi, Ifeoma, Musa, Jonah, Odidika, Umeora, Salako, Omolola, Nash, Oyekanmi, Salako, Babatunde L., Nwankwo, Kenneth Chima, Asuquo, Marcus Inyama, Ekwere, Timothy, Aniekwensi, Ezechukwu, Ezeude, Chidi, Awopeju, Olayemi, Kolawole, Tolutope, Adesina, Olubiyi, Ghyi, Vandi, Oni, Olaolu, Gimba, Zumnan, Attipoe, Delali, Dogbo, Estelle, and Salako, Babatunde

Published
2023
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5. Whole-genome sequencing across 449 samples spanning 47 ethnolinguistic groups provides insights into genetic diversity in Nigeria

Author

Joshi, Esha, primary, Biddanda, Arjun, additional, Popoola, Jumi, additional, Yakubu, Aminu, additional, Osakwe, Oluyemisi, additional, Attipoe, Delali, additional, Dogbo, Estelle, additional, Salako, Babatunde, additional, Nash, Oyekanmi, additional, Salako, Omolola, additional, Oyedele, Olubukunola, additional, Eze-Echesi, Golibe, additional, Fatumo, Segun, additional, Ene-Obong, Abasi, additional, O’Dushlaine, Colm, additional, Osifo, Ogochukwu Francis, additional, Moddibo, Zahra Isa, additional, Ado-Wanka, Aisha Nabila, additional, Attipoe, Delali Attiogbe, additional, Modibbo, Fatima Z., additional, Ado-Wanka, Nabila, additional, Braimah, Onome, additional, Julius-Enigimi, Eramoh, additional, Akindigh, Terver Mark, additional, Kusimo, Bolutife, additional, Akpulu, Chinenye, additional, Nwuba, Chiamaka, additional, Ebong, Ofonime, additional, Anyika, Chinyere, additional, Adewunmi, Oluwatimilehin, additional, Ibrahim, Yusuf, additional, Kashimawo, Janet, additional, Nkwocha, Chidi, additional, Iyitor, Peter, additional, Abiwon, Temi, additional, Adeleye, Adeola, additional, Ode, Abayomi, additional, Ayo-Lawal, Anjola, additional, Akpabio, Kasiena, additional, Edu, Emame, additional, Njoku, Chiemela, additional, Ballew, Bari, additional, Palmer, Cameron, additional, Joshi, Esha, additional, Bost, Teresia L., additional, Musa, Abdullahi, additional, Mujtaba, Abdulrasheed M., additional, Popoola, Abiodun, additional, Bello, Abubakar M., additional, Anyanwu, Anthony, additional, Yusuf, Ashiru, additional, Bozimo, Gesiye E.L., additional, Bassey, Goddy, additional, Bala, Hadiza, additional, Bosan, Istifanus Bala, additional, Edah, Jemimah, additional, Jimoh, Mutiu Alani, additional, Nwankwo, Kenneth, additional, Ojo, Olalekan, additional, Inyama, Marcus, additional, Apanpa, Maryam, additional, Mustapha, Mohammed Inuwa, additional, Ali-Gombe, Musa, additional, Ojo, Olubukola, additional, Adeyemi, Oludare F., additional, Ajayi, Samuel, additional, Bala, Sanusi, additional, Ojo, Temitope, additional, Aliyu, Usman Malami, additional, Raji, Yemi, additional, Tanko, Zainab, additional, Mohammed, Amina, additional, Oladele, David, additional, Hamzat, Muhammed, additional, Agaba, Emmanuel, additional, Nwankwo, Emeka, additional, Ulasi, Ifeoma, additional, Musa, Jonah, additional, Odidika, Umeora, additional, Salako, Babatunde L., additional, Nwankwo, Kenneth Chima, additional, Asuquo, Marcus Inyama, additional, Ekwere, Timothy, additional, Aniekwensi, Ezechukwu, additional, Ezeude, Chidi, additional, Awopeju, Olayemi, additional, Kolawole, Tolutope, additional, Adesina, Olubiyi, additional, Ghyi, Vandi, additional, Oni, Olaolu, additional, and Gimba, Zumnan, additional

Published
2023
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7. Development of patient “profiles” to tailor counseling for incidental genomic sequencing results

Author

Mighton, Chloe, Carlsson, Lindsay, Clausen, Marc, Casalino, Selina, Shickh, Salma, McCuaig, Laura, Joshi, Esha, Panchal, Seema, Graham, Tracy, Aronson, Melyssa, Piccinin, Carolyn, Winter-Paquette, Laura, Semotiuk, Kara, Lorentz, Justin, Mancuso, Talia, Ott, Karen, Silberman, Yael, Elser, Christine, Eisen, Andrea, Kim, Raymond H., Lerner-Ellis, Jordan, Carroll, June C., Glogowski, Emily, Schrader, Kasmintan, Bombard, Yvonne, and on behalf of the Incidental Genomics Study Team

Published
2019
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10. The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information

Author

Gill, Ivan S., primary, Griffiths, Emma J., additional, Dooley, Damion, additional, Cameron, Rhiannon, additional, Savić Kallesøe, Sarah, additional, John, Nithu Sara, additional, Sehar, Anoosha, additional, Gosal, Gurinder, additional, Alexander, David, additional, Chapel, Madison, additional, Croxen, Matthew A., additional, Delisle, Benjamin, additional, Di Tullio, Rachelle, additional, Gaston, Daniel, additional, Duggan, Ana, additional, Guthrie, Jennifer L., additional, Horsman, Mark, additional, Joshi, Esha, additional, Kearny, Levon, additional, Knox, Natalie, additional, Lau, Lynette, additional, LeBlanc, Jason J., additional, Li, Vincent, additional, Lyons, Pierre, additional, MacKenzie, Keith, additional, McArthur, Andrew G., additional, Panousis, Emily M., additional, Palmer, John, additional, Prystajecky, Natalie, additional, Smith, Kerri N., additional, Tanner, Jennifer, additional, Townend, Christopher, additional, Tyler, Andrea, additional, Van Domselaar, Gary, additional, and Hsiao, William W. L., additional

Published
2023
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11. A survey of proteomic variation across two ethnic groups in Nigeria and its relationship to obesity risk

Author

Biddanda, Arjun, primary, de Arce, Karen Perez, additional, Eze-Echesi, Golibe, additional, Nwuba, Chiamaka, additional, Ibrahim, Yusuf, additional, Oyedele, Olubukunola, additional, Joshi, Esha, additional, Alalade, Boladale, additional, Ajayi, Olanrewaju, additional, Nwatu, Chidimma, additional, Yakubu, Aminu, additional, Ene-Obong, Abasi, additional, Popoola, Jumi, additional, O’Dushlaine, Colm, additional, and Fekkes, Peter, additional

Published
2022
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13. GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Sloan School of Management, Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., Sloan School of Management, Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., and Kim, Raymond H.

Abstract

Background Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually searching through a multitude of independent databases, often with the aid of several, mostly independent, computational tools. To streamline variant interpretation, we developed the GeneTerpret platform which collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in the genome(s). The platform assigns quantitative validity scores to genes by query and assembly of the genotype–phenotype data, sequence homology, molecular interactions, expression data, and animal models. It also uses the American College of Medical Genetics and Genomics (ACMG) criteria to categorize variants into five tiers of pathogenicity. The final output is a prioritized list of potentially causal variants/genes. Results We tested GeneTerpret by comparing its performance to expert-curated genes (ClinGen’s gene-validity database) and variant pathogenicity reports (DECIPHER database). Output from GeneTerpret was 97.2% and 83.5% concordant with the expert-curated sources, respectively. Additionally, similar concordance was observed when GeneTerpret’s performance was compared with our internal expert-interpreted clinical datasets. Conclusions GeneTerpret is a flexible platform designed to streamline the genome interpretation process, through a unique interface, with improved ease, speed and accuracy. This modular and customizable system allows the user to tailor the component-programs in the analysis process to their preference. GeneTerpret is available online at https://geneterpret.com .

Published
2022

14. The Dataharmonizer: a Tool for Faster Data Harmonization, Validation, Aggregation, and Analysis of Pathogen Genomics Contextual Information

Author

Gill, Ivan, primary, Griffiths, Emma, additional, Dooley, Damion, additional, Cameron, Rhiannon, additional, Savić Kallesøe, Sarah, additional, John, Nithu Sara, additional, Sehar, Anoosha, additional, Gosal, Gurinder, additional, Alexander, David, additional, Chapel, Madison, additional, Croxen, Matthew, additional, Delisle, Benjamin, additional, Di Tullio, Rachelle, additional, Gaston, Daniel, additional, Duggan, Ana, additional, Guthrie, Jennifer, additional, Horsman, Mark, additional, Joshi, Esha, additional, Kearney, Levon, additional, Knox, Natalie, additional, Lau, Lynette, additional, LeBlanc, Jason, additional, Li, Vincent, additional, Lyons, Pierre, additional, MacKenzie, Keith, additional, McArthur, Andrew, additional, Panousis, Emilie, additional, Palmer, John, additional, Prystajecky, Natalie, additional, Smith, Kerri, additional, Tanner, Jennifer, additional, Townend, Christopher, additional, Tyler, Andrea, additional, Van Domselaar, Gary, additional, and Hsiao, William, additional

Published
2022
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16. Additional file 1 of GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., and Hosseini, S. Mohsen

Abstract

Additional file 1: Materials file. Table S2. GeneTerpret modules and respective databases with links to the used data. Table S3. The required VCF file annotation, headers and descriptions. Table S4. The standard format for the PED file. Table S5. Variant Interpretation Program (VIP) logic (pseudocode) for variant classification following ACMG criteria. Table S6. Bins Used for Ranking GeneTerpret Output. Table S7. Comparison of GeneTerpret output and previous manual interpretation of 10 trios. Table S8. Comparison of GeneTerpret output and previous manual interpretation of a cohort with 20 TOF patients. Findings from manual interpretation have been reported for five individuals in this cohort.

Published
2022
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17. Additional file 4 of GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., and Hosseini, S. Mohsen

Subjects
InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI)
Abstract

Additional file 4: Figure S1. A snapshot of the GeneTerpret graphical user interface (GeneTerpret GUI). A general interpretation routine is depicted as an example. The user selects the needed modules from the top right panel; then drags and drops them one by one in the left workspace panel. Furthermore, the tissue or phenotype/disease of interest can be directly entered by the user as an input in the bottom right panel and the generated module could be dragged and dropped in the left workspace panel. The users can upload their annotated VCF file, gene list(s), family information (PED file) and phenotypes/diseases list as further input for GeneTerpret by tapping on the upload tab in the bottom right panel and drag and drop the assigned generated module for the uploaded file in the workspace panel in the left side.

Published
2022
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18. Additional file 6 of GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., and Hosseini, S. Mohsen

Subjects
ComputingMethodologies_PATTERNRECOGNITION
Abstract

Additional file 6: Figure S3. Variant Interpretation Program (VIP) internal structure. External databases used for VIP are fetched and processed, with the output being stored in a MongoDB collection. In VIP, each database collection is associated with a specific ACMG classification, but not all classifications use these collections. Each row of the input VCF file is inputted to all classifications, flagging them as 1 or 0. Then, using the logic outlined in Supplementary Table S3, the individual classifications are combined to provide the pathogenicity classification for each variant.

Published
2022
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19. Additional file 5 of GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., and Hosseini, S. Mohsen

Abstract

Additional file 5: Figure S2. Gene Validity Module architecture. External databases are first fetched and filtered based on certain criteria, and the results are entered into MongoDB collections. ExPhenosion, CanGene, and KING modules take in user input and the MongoDB collections to perform their functions.

Published
2022
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20. Additional file 7 of GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, DeLong, Sean, Andric, Veronica, Joshi, Esha, Okello, John B. A., Dhir, Priya, Somerville, Cherith, Farncombe, Kirsten M., Kalbfleisch, Kelsey, Jobling, Rebekah K., Scherer, Stephen W., Kim, Raymond H., and Hosseini, S. Mohsen

Abstract

Additional file 7: Figure S4. Overview of the causality module output; (A) the interactive visualization of variant distribution in the validity-pathogenicity space allows users to explore the desired variants. Dark green, light green, yellow, orange, and red colours represent the pathogenicity of variants in a 5-tier system: benign, likely benign, uncertain significance, likely pathogenic, and pathogenic variants. (B) Lasso filter allows the analyst to select the desired variants and filter them to a downloadable VCF file.

Published
2022
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23. GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Author

Manshaei, Roozbeh, primary, DeLong, Sean, additional, Andric, Veronica, additional, Joshi, Esha, additional, Okello, John B. A., additional, Dhir, Priya, additional, Farncombe, Kirsten M., additional, Kalbfleisch, Kelsey, additional, Somerville, Cherith, additional, Jobling, Rebekah K., additional, Scherer, Stephen W., additional, Kim, Raymond H., additional, and Hosseini, S. Mohsen, additional

Published
2020
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24. Detection of the Novel SARS-CoV-2 European Lineage B.1.177 in Ontario, Canada

Author

Guthrie, Jennifer L., primary, Teatero, Sarah, additional, Zittermann, Sandra, additional, Chen, Yao, additional, Sullivan, Ashleigh, additional, Rilkoff, Heather, additional, Joshi, Esha, additional, Sivaraman, Karthikeyan, additional, de Borja, Richard, additional, Sundaravadanam, Yogi, additional, Laszloffy, Michael, additional, Heisler, Lawrence, additional, Allen, Vanessa G., additional, Simpson, Jared T., additional, and Fittipaldi, Nahuel, additional

Published
2020
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27. Evaluation of a decision aid for incidental genomic results, the Genomics ADvISER: protocol for a mixed methods randomised controlled trial

Author

Shickh, Salma, primary, Clausen, Marc, additional, Mighton, Chloe, additional, Casalino, Selina, additional, Joshi, Esha, additional, Glogowski, Emily, additional, Schrader, Kasmintan A, additional, Scheer, Adena, additional, Elser, Christine, additional, Panchal, Seema, additional, Eisen, Andrea, additional, Graham, Tracy, additional, Aronson, Melyssa, additional, Semotiuk, Kara M, additional, Winter-Paquette, Laura, additional, Evans, Michael, additional, Lerner-Ellis, Jordan, additional, Carroll, June C, additional, Hamilton, Jada G, additional, Offit, Kenneth, additional, Robson, Mark, additional, Thorpe, Kevin E, additional, Laupacis, Andreas, additional, and Bombard, Yvonne, additional

Published
2018
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28. Quality of life drives patients’ preferences for secondary findings from genomic sequencing

Author

Mighton, Chloe, Carlsson, Lindsay, Clausen, Marc, Casalino, Selina, Shickh, Salma, McCuaig, Laura, Joshi, Esha, Panchal, Seema, Semotiuk, Kara, Ott, Karen, Elser, Christine, Eisen, Andrea, Kim, Raymond H., Lerner-Ellis, Jordan, Carroll, June C., Glogowski, Emily, Schrader, Kasmintan, and Bombard, Yvonne

Abstract

There is growing impetus to include measures of personal utility, the nonmedical value of information, in addition to clinical utility in health technology assessment (HTA) of genomic tests such as genomic sequencing (GS). However, personal utility and clinical utility are challenging to define and measure. This study aimed to explore what drives patients’ preferences for hypothetically learning medically actionable and non-medically actionable secondary findings (SF), capturing clinical and personal utility; this may inform development of measures to evaluate patient outcomes following return of SF. Semi-structured interviews were conducted with adults with a personal or family cancer history participating in a trial of a decision aid for selection of SF from genomic sequencing (GS) (www.GenomicsADvISER.com). Interviews were analyzed thematically using constant comparison. Preserving health-related and non-health-related quality of life was an overarching motivator for both learning and not learning SF. Some participants perceived that learning SF would help them “have a good quality of life” through informing actions to maintain physical health or leading to psychological benefits such as emotional preparation for disease. Other participants preferred not to learn SF because results “could ruin your quality of life,” such as by causing negative psychological impacts. Measuring health-related and non-health-related quality of life may capture outcomes related to clinical and personal utility of GS and SF, which have previously been challenging to measure. Without appropriate measures, generating and synthesizing evidence to evaluate genomic technologies such as GS will continue to be a challenge, and will undervalue potential benefits of GS and SF.

Published
2020
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29. Current Drugs and their Therapeutic Targets for Hypoxia-inducible Factors in Cancer.

Author

Joshi E, Pandya M, and Desai U

Subjects
Humans, Hypoxia drug therapy, Hypoxia genetics, Oxygen, Basic Helix-Loop-Helix Transcription Factors genetics, Basic Helix-Loop-Helix Transcription Factors metabolism, Basic Helix-Loop-Helix Transcription Factors therapeutic use, Neoplasms drug therapy, Neoplasms genetics, Neoplasms pathology
Abstract

Hypoxia, a prevalent characteristic of both solid and liquid malignancies, is found to regulate how genes are expressed in a way that promotes cellular adaptability and survival. Metastasis is controlled by hypoxia-inducible factors (HIFs). HIFs are dimeric protein molecules made up of an oxygen (O 2 ) responsive HIF-1α, HIF-2α, or HIF-3α domain and a periodically produced HIF-1β portion (also known as ARNT). Nevertheless, it is important to note that HIFs degrade under normoxic conditions. A large multitude of different biological operations, including vessels generation, oxygen delivery, stemness, pluripotency, multiplication, epithelial to mesenchymal shift, metastatic prevalence and intrusion, redox equilibrium, and programmed cell death, are strictly controlled by over 70 immediate HIF target genes that have been reported. Metabolic reprogramming, which modulates cellular energy generation aside from oxidative phosphorylation and concerning glycolysis, is among the core tasks of HIF target genes. As a result, choosing HIFs as a primary target in the treatment of various tumors is essential. We have a very limited understanding of this extremely complex topic, which is characterised by hypoxia- induced resistance. In order to combat this, scientists are investigating numerous cutting-edge approaches. Traditional chemotherapeutic drugs used to treat cancer are frequently linked to unfavourable side effects and the development of chemoresistance. The use of natural compounds in conjunction with chemotherapy drugs is rising as a result of their capacity to alter a number of molecular practices with a lower detrimental impact. Experimental and clinical research is accumulating evidence that phytochemicals can influence the genesis and progression of cancer by favourably modulating a number of signalling pathways. Combinations of phytochemicals are potent cancer treatment options because they incentivise apoptosis, limit cell prevalence, make cancerous cells more susceptible, and escalate immunity. Despite being characterised, HIF-1-independent mechanisms for medication resistance in hypoxia are still infrequently reported. The prime aim of the article is to summarise the most recent research on the molecular basis of hypoxia-induced chemoresistance and how chemotherapy and phytochemicals can be used to treat cancer patients who are resistant to drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Published
2023
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