1. Interpreting exomes and genomes: a beginner’s guide
Daniel MacArthur Analytic and Translational Genetics Unit Massachusetts General Hospital Broad Institute of Harvard and MIT

2. Overview Fundamentals of next-generation sequencing
Genomes, exomes and targeted panels
Genomic diagnosis: how do we filter causal variants from a patient’s entire genome?
Major challenges for NGS diagnosis

3. Next-generation sequencing
Many different technologies
Can chop up DNA and read bits of fragment all at the same time – massively parallel sequencing
Illumina
Pacific Biosciences
Oxford Nanopore

4. Sequencing yields billions of reads per run
TTTGAACTTTCATAG
CGTTACGGCAGACG
GGGACATATTCGAAAT
ACGGGATGTACG
TAGACATAGACGACT
GGGATGTACGAA
GTACTGACCAG
GACCAGTAGAC
GACATAGACGACT
CCAGTAGACATA
ACGAGCCGTAGCTA
TTTGACGGGATG
GGGATGTACGA
What does the data “look like”
The machines generate fragments of DNA sequence – depending on the application these can be 75 to 150bp long
Our reads are paired so we can read in from each end of the library fragment
CGAGCCGTAGCTA
AGACGACTTTGAC
ATAGACGACTTTGA
GGGATGTATGAG
GGGATGTACGAG
TACGAGCCGTA
TGTACGAGCCGTA

5. Compare the reads to a reference genome
GTACTGACCAGTAGACATAGACGACTTTGACGGGATGTACGAGCCGTAGCTA
ACGGGATGTACG
TAGACATAGACGACT
GGGATGTACGAA
GTACTGACCAG
GACCAGTAGAC
GACATAGACGACT
CCAGTAGACATA
ACGAGCCGTAGCTA
TTTGACGGGATG
GGGATGTACGA
As part of our data processing we then compare these reads to a reference genome – human or any other reference that is applicable
CGAGCCGTAGCTA
AGACGACTTTGAC
ATAGACGACTTTGA
GGGATGTATGAG
GGGATGTACGAG
TACGAGCCGTA
TGTACGAGCCGTA

6. C -> T Challenges: Mapping short reads Variable coverage
NGS allows us to sample the sequence position many times over
GTACTGACCAGTAGACATAGACGACTTTGACGGGATGTACGAGCCGTAGCTA
TAGACATAGACGACT
ACGGGATGTATG
GTACTGACCAG
GGGATGTATGA
TTTGACGGGATG
ATGAGCCGTAGCTA
GACCAGTAGAC
GTACGAGCCGTA
CCAGTAGACATA
TGAGCCGTAGCTA
GACATAGACGACT
GGGATGTATGAG
GGGATGTACGAG
ATAGACGACTTTGA
AGACGACTTTGAC
TACGAGCCGTA
TGTACGAGCCGTA
C -> T
(5 C / 5 T)
Challenges:
Mapping short reads
Variable coverage
Base calling quality
Tend to be worse for insertions and deletions compared to SNPs
With NGS we are able to sample the position many times, so here we have many looks at this mutation and this kind of information gives us confidence in the call.

7. Percent of Genome Sequenced
Which technology to choose?
Technology
Percent of Genome Sequenced
Cost
Depth of Coverage
Whole Genome Sequencing
>95%
Whole Exome Sequencing
~1.5%
(protein-coding regions)
Targeted Sequencing
0.005% - 0.1%
(100s – 1000s of genes)
High level overview of the types of sequencing
WGS = complete DNA sequence of the person/organism
Exome = all mutations in all exons PLUS other variations (such as small insertions/deletions)
Targeted = all mutations in all targeted exons PLUS other variations (such as small insertions/deletions) – use on a large collection of genese

8. Targeted sequencing

9. Targeted sequencing

11. The problem with exome data
Clinically and genetically heterogeneous conditions
x 30,000 rows

12. Sifting signal from noise in exomes
Every genome contains many rare, potentially functional variants
~500 rare missense variants
~100 LoF variants: ~20 homozygous, ~20 rare
~100 rare variants in known disease genes
5-10 recessive disease-causing mutations
1-2 de novo coding mutations
sequencing errors
In Mendelian disease patients we need to find 1-2 true causal mutations amidst this “noise”

13. How do we find pathogenic variants?
Is the variant a known pathogenic variant? How much evidence supports the claim of pathogenicity?
Is the variant rare?
Is it predicted to have a functional impact (change a protein sequence)?
Does it segregate with disease?
Is the gene associated with the disease?

14. Making sense of one genome requires tens of thousands of genomesvs
More than 500K exomes and 50K genomes have been sequenced worldwide but these data are siloed by project and inconsistently processed

15. Exome Aggregation Consortium (ExAC)
Latino
African
European
South Asian
East Asian
Other
1000 Genomes
ESP
ExAC
exac.broadinstitute.org

16. Value of reference databases
Provide variant frequency in a large population (either healthy, or “reference” i.e. population sample)
Provide frequency across multiple human populations
Allow us to assess how many variants we see in a particular gene
Provide an unbiased estimate of variant penetrance

17. Lessons from ExAC
Many “healthy” people carry apparently disease-causing variants
over 20,000 reported disease variants are seen in our “healthy” samples
average ~2/person after filtering
What’s causing this?
carriers of recessive variants
some undiagnosed disease cases
lots of false positive variants (20-25%)

18. Databases of disease mutations
Drawn from literature collected over decades with variable standards
Five years ago: no large frequency databases, = any rare protein-altering variant is causal
New databases more careful about evidence

20. xBrowse: Rapid exploration of multiple inheritance patterns

21. xBrowse: Filtering by function and frequency

22. xBrowse: Digestible information for all candidate variants and genes

23. xBrowse: Digestible information for all candidate variants and genes
exac.broadinstitute.org

24. xBrowse: Following up candidate genes with external resources

25. The big (largely) unsolved challenges
NGS data still misses a non-trivial number of genetic variants, also has errors
Our reference databases are still missing many populations
Uncertainty even about “known” pathogenic variants in databases
For many variants, penetrance is not robustly established
Huge difference between interpretation in “healthy” and “disease” samples