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Genomic analysis discussion


Exome sequence data is usually about 4 GB of information per person. Whole genomes are approximately 50GB of data. The analysis of whole genome sequencing is almost identical to the exome pipeline outlined here. While there is much more information (for not much of a higher cost), a lot of the non-coding sequence contains information that we can’t yet interpret. For Mendelian disease the whole exome often uncovers the coding variants that explain disease. We may not understand anything else outside the exome (and the surrounding splice regions) in relation to a patients’ disease. Mutations in the promoters or enhancers that prevent transcription may not be as readily interpretable as the majority of coding variant effects. Therefore, whole genome is often not required. This excuse for performing exome sequencing rather than whole genome mostly depends on value for money. Performing all the different kinds of analysis, including non-coding genome analysis, requires many people with expertise in each topic. Even if whole genome data was available to smaller research teams, it is often the case that they cannot carry out all the work required to interpret it. For national level genomics, there is no question that whole genome sequencing is preferential. We can retain the data for decades with hundreds of experts to share the work-load, while the cost is essentially a political factor. An important question to address is the right for a person to agree to genetic forfeiture. We are at the brink of preventative medicine using genome sequencing in newborns. Regardless of the popular ethical consensus, any preventative non-consenting genomic analysis can be considered coercion.

Figure [fig:immune_cluster] illustrates how not only can very rare or damaging variants be clustered, but the same network can be expanded to include peripherally interacting genes. This modification may be used for downstream functional work such as looking at pathway-level expression data. An important consideration for protein network cohort analysis is evident in Figure [fig:network_size_nodes]. About 50% of genes with a functional variant are do not get clustered into a PPI network (protein pathway). However, some of these genes could still harbour a potential loss-of-function or damaging variant. If we found 3 significantly enriched protein networks, a potential 4th missed network (false negative), because of unclustered genes, would not detract from the significant findings. Therefore, the singleton genes remaining from MCL clustering should be listed and reassessed based on traditional interpretations; variant effect, loss-of-function intolerance, etc. The converse, a false positive because of over clumping weakly related proteins, would be negative.

The analysis of genomic data is an iterative process. Therefore, access to raw unprocessed genetic information is often required to utilize cutting edge methods [@Auwera2013From; @Poplin2018Scaling]. Furthermore, genetic analysis is a complex, multi-stage procedure. Due to the inherent complexity, there is a number of output streams which consist of different data types. To provide seamless integration with current best practices in precision medicine, it is valuable to adhere to standard genomic data types, including CRAM, SAM/BAM, FASTQ, and VCF [@Li2009Sequence; @Hsi2011Efficient; @cock2009sanger; @Danecek2011Variant]. There is benefit to creating new data formats that increase efficiency. However, by focusing on key data types in genomics, one can enable integration with most current software [@pabinger2014survey; @Auwera2013From].

An interesting caveat to genetic data is that at pre-processing stages, several data types cannot be currently provided with protection through the use of modern cryptographic methods [@froelicher2017unlynx; @juvekar2018gazelle]. There is currently a severe lack of tools that complement current methods required to interrogate genetic data at different stages while protecting individual personal genetic records. Furthermore, despite the attempts to promote data privacy and integrity through global initiatives, such as Global Aliance 4 Genome Health, little has been done to produce queryable data that protects the genetic identity of a subject.

The privacy concerns at the early stages of data processing are often overlooked. Almost every method offered for data security relies on protecting only fully process data (e.g. already variant called VCF format data) or summary statistics. In worst cases, privacy concerned genomics falls back to “trust-based” systems where data generators or researchers are required to accept responsibility for preventing any re-anonymisation. Of course, researcher trust is an important factor, however, relying on this method for protecting subject information is immoral. Unlike nearly all clinical data, genetic data is inherently identifiable and is not readily anonymised. The information that makes up the data is itself the identity or commodity. In nearly every other type of clinical data, it is only a commodity when there is an identity to which it is paired or if it is part of a large dataset-of-normals. The lack of strong methods of genetic data protection is not an apparent risk generally. Extrapolating the risk which differentiates other types of data that requires informed consent is a difficult task for many experts. Relying on patient consent and trust in data protection is not sufficient for the future of global genomics. Successfully overcoming these challenges will allow for the use of analysis methods that otherwise provide vulnerabilities against the protection of private data [@Li2009Fast], [GA4GH (].


A pipeline of routine exome analysis was outlined. Important points on tailored analysis are demonstrated. A new method was developed for the unbiased detection of a protein network, driving disease, based on potential loss of function variants.

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