Research and Reports on Genetics

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
Reach Us +44 7460731551

Perspective - Research and Reports on Genetics (2022) Volume 4, Issue 1

Clinical genetics and genomics advancements.

Shen Li*

Department of Biostatistics, University of Pennsylvania Perelman School of Medicine, USA

*Corresponding Author:
Shen Li
Department of Biostatistics
University of Pennsylvania Perelman School of
Medicine
USA
E-mail: [email protected]

Received: 20-Dec-2021, Manuscript No. AARRGS-22-53386; Editor assigned: 22-Dec-2021, PreQC No. AARRGS-22- 53386 (PQ); Reviewed: 05- Jan-2022, QC No. AARRGS-22- 53386; Revised: 07-Jan-2022, Manuscript No. AARRGS-22- 53386 (R); Published: 14-Jan-2022, DOI:10.35841/2591-7986-4.1.103

Citation: Li S. Clinical genetics and genomics advancements. J Res Rep Genet. 2022; 4(1):103

Visit for more related articles at Research and Reports on Genetics

Introduction

The field of genetics and genomics is advancing at an unparalleled rate. The human genome project, which began twenty years ago, provided the first peeks into the human genome sequence and ushered in a new era of human genetics. Following the introduction of next-generation sequencing (NGS) in 2005, comprehensive genetic testing such as exome sequencing and genome sequencing became possible. Meanwhile, much effort has gone into optimising bioinformatic pipelines in order to perform increasingly fast and accurate variant analyses based on NGS data. The diagnostic journey of suspected genetic disorders has been changed thanks to developments in sequencing technologies and analytical methodologies. More recently, the genotype-phenotype link and polygenic risk scores (PRSs) derived from genome-wide association studies have broadened our view beyond rare genetic alterations to a genomic landscape implicated by both rare variants and polymorphisms. Simultaneously, doctors and genetic counsellors are confronted with enormous obstacles posed by overwhelming genomic knowledge and extensive sheets of test data for full genomic sequencing. Semiautomatic pipelines aided by artificial intelligence approaches could pave the way for "next-generation" clinical genetics and genomics.

The tools and clinical applications of genetics and genomics have advanced dramatically during the last two decades. The human genome project, which began twenty years ago, provided the first glimpses into the human genome sequence and ushered in a new era of human genetics: for the first time, biologists and geneticists had access to almost the entire human genome, allowing for high-throughput and genome-wide analyses previously unavailable. The introduction of next-generation sequencing (NGS) in 2005 accelerated the advancement of genetics and genomics, which had relied on Sanger sequencing for the previous 30 years [1]. NGS-based genome sequencing (GS) can already be accomplished in hours for less than 1000 dollars [2]. Large genomic sequencing efforts, such as the 1000 Genomes Project, the UK10K project, the 100,000 genomes project, and, more recently, the China MAP project, have been made possible by this technical breakthrough. The most critical issues in genetic and genomic medicine have shifted from data generation to data mining and integrative analytics, thanks to the rapidly increasing volume of human genome sequenced [3]. The genome aggregation database research team performed comprehensive analyses on large genome and exome datasets to give insight into gene constraint, population mutational architecture, and potential drug targets; the deciphering disorders involving scoliosis and comorbidities study promoted data exchange and despite this, the vast majority of the functional human genome and its relationship to disease features are still unknown. The search for the "future generations" of experimental and analytic tools continues apace [4].

Despite the fact that today's genetic testing can cover the entire exome or genome, our clinical interpretation is mostly focused on high-penetrant pathogenic variations that cause Mendelian illnesses due to their straightforward gene-disease link. Common illness features, such as diabetic habit and autoimmune disorders, are generated by a complicated genetic background and a combination of environmental circumstances [5].

A genome-wide association study has had significant success uncovering genetic components of complex phenotypes during the last 20 years using genotyping array or NGS technology. While this study is being written in the midst of the coronavirus disease-2019 (COVID-19) pandemic, GWASs conducted around the world have found a handful of host risk alleles linked to COVID-19 infection susceptibility or severity.

Medicine in the fields of genetics and genomics is progressing at a breakneck pace. The diagnostic odysseys of suspected genetic disorders have been changed by new experimental and analytical tools. A large-scale genotype-phenotype association, as well as PRS models derived from GWAS datasets, broaden our horizons beyond rare genetic changes to the genomic landscape. Clinicians and genetic counsellors are also confronted with unprecedented hurdles as a result of overwhelming genomic knowledge and the growing number of long sheets of test reports generated by comprehensive genomic sequencing. Semiautomatic pipelines aided by artificial intelligence approaches could pave the way for future clinical genetics and genomics.

References

  1. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409 (6822):860-921.
  2. Indexed at, GoogleScholar

  3. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Sci. 2001; 291(5507):1304-1351.
  4. Indexed at, GoogleScholar, Cross Ref

  5. ET Cirulli, S White, RW Read, et al. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts. Nat Commun. 2020; 11(1): 542.
  6. Indexed at, GoogleScholar, Cross Ref

  7. Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020; 58:434-443.
  8. Indexed at, GoogleScholar, Cross Ref

  9. Li HR, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-1760.
  10. Indexed at, GoogleScholar, Cross Ref

Get the App