Human Genetics

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The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Barry Wolf - One of the best experts on this subject based on the ideXlab platform.

David Altshuler - One of the best experts on this subject based on the ideXlab platform.

  • validating therapeutic targets through Human Genetics
    Nature Reviews Drug Discovery, 2013
    Co-Authors: Robert M Plenge, Edward M Scolnick, David Altshuler
    Abstract:

    More than 90% of the compounds that enter clinical trials fail to demonstrate sufficient safety and efficacy to gain regulatory approval. Most of this failure is due to the limited predictive value of preclinical models of disease, and our continued ignorance regarding the consequences of perturbing specific targets over long periods of time in Humans. 'Experiments of nature' - naturally occurring mutations in Humans that affect the activity of a particular protein target or targets - can be used to estimate the probable efficacy and toxicity of a drug targeting such proteins, as well as to establish causal rather than reactive relationships between targets and outcomes. Here, we describe the concept of dose-response curves derived from experiments of nature, with an emphasis on Human Genetics as a valuable tool to prioritize molecular targets in drug development. We discuss empirical examples of drug-gene pairs that support the role of Human Genetics in testing therapeutic hypotheses at the stage of target validation, provide objective criteria to prioritize genetic findings for future drug discovery efforts and highlight the limitations of a target validation approach that is anchored in Human Genetics.

Jurgen K Naggert - One of the best experts on this subject based on the ideXlab platform.

Irene Ceballospicot - One of the best experts on this subject based on the ideXlab platform.

Darren C Ames - One of the best experts on this subject based on the ideXlab platform.

  • functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across Human Genetics projects
    Nature Communications, 2018
    Co-Authors: Allison Regier, Yossi Farjoun, David E Larson, Olga Krasheninina, Hyun Min Kang, Daniel P Howrigan, Bo Juen Chen, Manisha Kher, Eric Banks, Darren C Ames
    Abstract:

    Hundreds of thousands of Human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide Human Genetics studies.

  • functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across Human Genetics projects
    bioRxiv, 2018
    Co-Authors: Allison Regier, Yossi Farjoun, David E Larson, Olga Krasheninina, Hyun Min Kang, Daniel P Howrigan, Bo Juen Chen, Manisha Kher, Eric Banks, Darren C Ames
    Abstract:

    Hundreds of thousands of Human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population Genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes. Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce "functionally equivalent" (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results — including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) — and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide "big-data" Human Genetics studies.

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