The Genome that Won A Nobel Prize

My group met recently to discuss the in-press-at-Nature publication of Jim Watson’s genome – the first diploid human genome to be sequenced with next-generation technology. I’ve been waiting for this since 454 announced the project’s completion at the HGM2007 meeting last year in Montreal. It’s a landmark publication in terms of human genetic variation, and of particular interest to me since I work on our center’s 454 analysis pipeline.

In two months Roche/454 generated ~106.5 million genomic reads from Watson’s DNA in 234 runs. Using BLAT they mapped 93.2 million reads (87.5%) to hg36, yielding an average coverage of about 7.4x. No doubt the expense of this effort was substantial, though the authors claim it was 1/100^th of what capillary sequencing would have cost. It probably also hurt to throw away 2.5 million “unmapped” reads, though they did some post-processing of these with interesting results.

After a few filters were applied, the authors produced a set of 3.32 million SNPs in Watson’s genome, a number deliciously comparable to Craig Venter’s 3.47 million SNPs. In both men >80% of the SNPs are already known (to dbSNP). The most recent build of dbSNP (build 128), which doesn’t yet include novel Watson/Venter SNPs, has 9.89 million SNPs. The authors didn’t say but I estimate that the men share about 300,000 novel SNPs. Together they’ll add about 10% to the set of known SNPs, and only 1-2% of nonsynonymous SNPs. I hate to break it to you, but the sun is setting for nsSNPs. We know about 95% of them already and in Jim Watson only 7% are likely to be deleterious.

Also, over at GeneticFuture Daniel MacArthur discusses how the Watson Genome may be gloomy news for the field of personal genomics.  He points out that we’re perhaps five years away from affordable whole-genome sequencing, and by then we will no doubt have a much better understanding of how functional variation affects human phenotypes.

Indels are why I love 454 technology. In Watson’s genome they identified >200,000 indels of at least 2bp. Insertion detection is limited by read length, and so most were <200 bp. The largest deletion, however, was nearly 40 kbp. Only a fraction of the indels (~350) affected coding sequence. They saw a validation rate of 70% for a sampling of coding indels between 2 and 50 bp, which is pretty good. Single-base indels were treated with extreme caution, as over 80% of these were associated with homopolymers, the Achilles heel of 454 sequencing.

This paper was worth the wait. Not only was it an impressive demonstration of the power of 454 sequencing for whole-genome sequencing, but it openly addressed many of the informatics challenges therein and answered some interesting questions along the way. We can now confidently say that an individual carries ~3.7 million SNPs relative to the reference sequence, of which perhaps 10,000 are protein-altering. Ten of Watson’s nsSNPs were Mendelian-recessive, highly penetrant, disease-causing alleles according to HGMD, suggesting that each of us carries many more deleterious alleles than was previously believed. Yet analysis of the unplaced 454 reads suggests that as many as 100 protein-coding genes are still absent from the reference sequence. It seems like the work on the human genome is never done. I certainly know the feeling.

Annotation of Insertions and Deletions

We’re working on a project with ~2.2 million 454 reads from two cDNA libraries and my job is to find and classify the insertion/deletion variants (indels). As you might guess, since these are reads of transcribed sequence, there’s a lot of noise due to mRNA processing. Spliced-out introns look like deletions. Partially-processed transcripts might look like they contain insertions. So, once I made indel predictions based on aligning 454 data to the hg36 reference sequence, the next priority was to remove the noise.

Fortunately, two colleagues in my group, Ken Chen (the developer of PolyScan) and Brian Dunford-Shore (our resident physicist) have built a “transcriptome” based on all of the known transcripts in CCDS, Ensembl, and Vega databases. One of the files generated with the transcriptome is the refseq “footprint” which contains all of the UTRs and exons of all transcripts. It seems to me this file offers the most comprehensive source for annotating the indels from cDNA data.

So, I wrote a script, annotate_with_footprint.pl, which cross-references a set of indels with the footprint file. Insertions are classified as either within-CDS-exon, within-UTR-exon, or noncoding. Deletions are a bit more complicated – they could be within-CDS-exon, within-UTR-exon, or noncoding. They could also span multiple CDS or UTR exons, span intron-exon-junctions, etc.

As it turned out, only about 12% of the insertions and 1% of the deletions were in exons; The vast majority were in UTR/intron regions or intron-exon splice artifacts. Another 4% of the deletions appeared to span one or more CDS exons, but many of these may be exon-skipping events, not true deletions.

Even with strong 454 cDNA support, I won’t be confident that these are real coding mutations until we validate them in genomic DNA.