The road ahead in genetics and genomics

McGuire, Amy L.; Gabriel, Stacey; Tishkoff, Sarah A.; Wonkam, Ambroise; Chakravarti, Aravinda; Furlong, Eileen E. M.; Treutlein, Barbara; Meissner, Alexander; Chang, Howard Y.; López‐Bigas, Núria; Segal, Eran; Kim, Jin-Soo

doi:10.1038/s41576-020-0272-6

Cited by 143 publications

(99 citation statements)

References 132 publications

(134 reference statements)

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“…When incorporating population data, it is also important for fairness and equity to understand how it changes the accuracy of methods for individuals with ancestries outside of those used in the development of the population resources. It is known that many genomic databases have collected more data for the European population than others [16][17][18]. We demonstrate that even using frequencies from a genetically distinct population, the population-aware model still performs similarly as the baseline.…”

mentioning

confidence: 83%

Improving variant calling using population data and deep learning

Chen

Kolesnikov

Goel

et al. 2021

Preprint

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Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we modify DeepVariant to add a new channel encoding population allele frequencies from the 1000 Genomes Project. We show that this model reduces variant calling errors, improving both precision and recall. We assess the impact of using population-specific or diverse reference panels. We achieve the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

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mentioning

confidence: 83%

Improving variant calling using population data and deep learning

Chen

Kolesnikov

Goel

et al. 2021

Preprint

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“…The high prevalence of HbC in West Africa, even though HbS associated genotypes are known to demonstrate excess of average fitness higher than HbC associated genotypes, indicate that HBB-β S is recent in this West African region ( F ); This needs to be properly investigated from an evolutionary genetic point of view. Lastly, from the time HBB-β S attained equilibrium in populations [currently estimated at 5000 years ago ( 1 )], it is likely that the pressure because of SCA on the human genome could have contributed to the enrichment of additional adaptive signatures, that still need to be investigated, as suggested by the enrichment of recurrent variants in numerous genes that are relevant to pathophysiology of SCD among patients in Africa ( 57 ).…”

Section: Common Variants In Haemoglobin Genesmentioning

confidence: 99%

“…Other loci recently associated with HbF level include BCL2L1 (OMIM: 600039, 20q11.21) ( 54 ) (which is yet to be replicated in Africa), and FRMPD4 (OMIM: 300838, Xp22.2) found among patients living with SCD in Tanzania ( 55 ). While, it is estimated that the HbF trait is highly heritable (87%) ( 56 ), the current known catalogue of variants explain just ~20–50% of the HbF phenotypic variance, indicating that many more variants remain to be uncovered, particularly in African populations, which harbour the highest genetic diversities with usually more extreme phenotypes ( 57 ). A particularly interesting new area of research would be to perform genome scans of SCA patients, particularly the long-term survivors, for signatures of adaptation, with specific interest to genomic loci involved in stage-specific expression of globin genes.…”

Section: Common Variants In Haemoglobin Genesmentioning

confidence: 99%

Evolutionary history of sickle-cell mutation: implications for global genetic medicine

Esoh

Wonkam

2021

Human Molecular Genetics

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Resistance afforded by the sickle-cell trait against severe malaria has led to high frequencies of the sickle-cell mutation [HBB; c.20 T > A, p.Glu6Val; OMIM: 141900 (HBB-βS)] in most parts of Africa. High-coverage sequencing and genotype data have now confirmed the single African origin of the sickle-cell gene variant [HBB; c.20 T > A, p.Glu6Val; OMIM: 141900 (HBB-βS)]. Nevertheless, the classical HBB-like genes cluster haplotypes remain a rich source of HBB-βS evolutionary information. The overlapping distribution of HBB-βS and other disease-associated variants means that their evolutionary genetics must be investigated concurrently. In this review: 1) we explore the evolutionary history of HBB-βS and its implications in understanding human migration within and out of Africa: e.g. HBB haplotypes and recent migration paths of the Bantu expansion, occurrence of ~ 7% of the Senegal haplotype in Angola reflecting changes in population/SCD dynamics, and existence of all five classical HBB haplotype in Cameroon and Egypt suggesting a much longer presence of HBB-βS in these regions; 2) we discuss the time estimates of the emergence of HBB-βS in Africa; and finally, 3) we discuss implications for genetic medicine in understanding complex epistatic interactions between HBB-βS and other gene variants selected under environmental pressure in Africa e.g. variants in HBB, HBA, G6PD, APOL, APOE, OSBPL10, RXRA.

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“…The forecasting or designating genotypes that cannot be assayed directly in individuals’ samples are known as genotype imputation ( Ashburner et al, 2000 ; Naj, 2019 ; Chen S. F. et al, 2020 ). In recent years, the studies on genome-wide association (GWA) have revealed many significantly replicated associations for various complicated diseases ( McGuire et al, 2020 ). In the analysis of GWA studies, genotype imputation has been widely used to enhance power, fine-map connections, and expedite the integration of results throughout the studies using meta-analysis.…”

Section: Identification Of Therapeutic Biomolecules Of Plants Through the Multi-omics Approachmentioning

confidence: 99%

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

Singh

Kumar

et al. 2021

Front. Pharmacol.

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COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.

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The road ahead in genetics and genomics

Cited by 143 publications

References 132 publications

Improving variant calling using population data and deep learning

Improving variant calling using population data and deep learning

Evolutionary history of sickle-cell mutation: implications for global genetic medicine

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

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