AGBT: Sequencing’s Debutante Ball or Déjà vu

February 15, 2013 by Dan Koboldt

It’s less than a week until the Advances in Genome Biology & Technology meeting (#AGBT2013) in Marco Island, Florida. Historically, this meeting has been like a debutante ball for the sequencing community — the place where new technologies are showcased, promised, and occasionally delivered upon.

The trends of past meetings often set the tone for the year that followed:

AGBT 2010 highlighted new emerging sequencing technologies like Pacific Biosciences and IonTorrent.
AGBT 2011 was all about smaller, more affortable benchtop sequencers like the MiSeq and PGRN, culminating with a huge guy literally carrying in a prototype IonTorrent PGM.
AGBT 2012 focused on sequencing’s clinical applications and saw the stunning announcement of Oxford Nanopore’s jump drive sequencers.

AGBT 2013 Anticipation

This year, AGBT is a bit later in February than usual — good news for the 15 minutes of beach time managed by most attendees — and I won’t be attending myself. Looking at the meeting agenda confused me for a minute. It felt like déjà vu … PacBio talking about structural variation, Liz Worthey on clinical applications, my friend Aaron Quinlan presenting a cleverly-named informatics tool (this year it’s “LUMPY”). I read it and wondered, “Am I looking at an old agenda?”

If we take the agenda as a whole and do some basic text mining, the recurrent themes of most presentations are quick to emerge:

count    phrase
21    sequencing
18    genome
11    cancer
10    analysis
9    whole, human, genomic
8    clinical
7    single
6    gene, cell
5    rna, mutations, discovery
4    medicine, mechanisms, exome, complex
3    tumors, somatic, de novo, metagenomic
3    ngs, genetic, expression, applications

Sequencing, of personal genomes and cancer genomes, is the weather-tested mainstay of agenda topics from recent years. There’s a substantial interest in getting sequencing into the clinic, much like we saw last year. Popular applications of next-gen sequencing, like RNA-seq and cancer profiling, will have their say. Interestingly, exome sequencing seems under-represented… one gets the feeling that this community, at least, is beginning to consider it routine (ho hum). I’m pleased to see the term “metagenomic” a few times, which suggests a growing appreciation of the importance of the human microbiome and similar topics.

Following AGBT Live

Thanks to Twitter and some dedicated “tweeps” (people who tweet things live), this meeting is easy to follow. I’ll have a steady feed of the #AGBT13 hashtag, which isn’t nearly as good as being there, but the best I can manage. There are some good talks to look forward to:

Eric Boerwinkle (Univ. of Texas) is giving a talk on ““Whole Genome and Exome Sequencing and Analysis of Large Numbers of Deeply-phenotyped Individuals Reveal the Genetic Architecture of Complex Traits: the CHARGE Consortium”.
Matthew Wiggin of Boreal Genomics will present “Multiplexed Detection of Low Abundance, Tumor Related Nucleic Acids in the Plasma of Cancer Patients”.
Malachi Griffith, of the Genome Institute at Washington University, has a talk on “Clinical Cancer Sequencing and Integrated Analysis of Whole Genomes, Exomes and Transcriptomes”.
Mark DePristo of the Broad Institute (a lead contributor to GATK) will discuss “Overcoming Today’s Limitations in Sequencing Technology for Human Medical Genetics” and hopefully commercial attendees won’t have to pay $300K to listen.

The Twitter hashtag is usually cluttered with commercial stuff, so you might want to pick some prolific Twitter-aholics like Nick Loman and Aaron Quinlan.

Update: AGBT 2013 Wordle Tag Cloud

My friends at @nextgenseq made a Wordle tag cloud that nicely illustrates the theme of this year’s AGBT meeting.

Disease-causing Mutations Discovered by NGS in 2011

December 29, 2011 by Dan Koboldt

The number of human genetic diseases unraveled by next-generation sequencing skyrocketed this year. Several factors contributed to this growth, two of which were the ever-increasing throughput of sequencing instruments and widespread availability of commercial exome platforms. A number of large-scale initiatives to discovery disease genes by exome sequencing, particularly for Mendelian disorders, got off the ground. I’d also argue that the rapid pace of discovery is also aided by a growing acceptance of sequencing as a clinical tool.

A PubMed search restricted to keywords “exome” and “sequencing” and year 2011 returned over 100 publications, of which more than 60 were studies linking genetic variation to human disease. I’ve whittled the list down to around 40 and (after consulting a medical dictionary for most) divided them down by rough disease categories.

Developmental Disorders

The largest of these was what I call “developmental disorders” – mental retardation, dysplasia (abnormal growth), dyskinesia (impaired movement), and the like. There were at least 14 gene-disease associations published this year, many of them in the Americal Journal of Human Genetics.

Developmental Disorders
SMOC2	Major dental developmental defects	Bloch-Zupan et al. AJHG.
SYT14	Spinocerebellar ataxia with psychomotor retardation	Doi et al. AJHG.
TECR	Non-syndromic mental retardation	Caliskan et al. Hum Mol Genet.
PRRT2	Paroxysmal kinesigenic dyskinesia	Chen et al. Nat Genet.
SERPINF1	Osteogenesis imperfecta	Becker et al. AJHG.
KIF22	Spondyloepimetaphyseal dysplasia with joint laxity	Min et al. AJHG.
KAT6B	Say-Barber-Biesecker syndrome	Clayton-Smith et al. AJHG.
POP1	Novel skeletal dysplasia	Glazov et al. PLoS Genet.
CCDC8	3-M syndrome	Hanson et al. AJHG.
SLCO2A1	Primary hypertrophic osteoarthropathy	Zhang et al. AJHG.
WDR62	Recurrent polymicrogyria	Murdock et al. Am J Med Genet A.
FAM20A	Amelogenesis imperfecta	O’Sullivan et al. AJHG.
SHROOM3	Heterotaxy	Tariq et al. Genome Biol.
MCT8	X-linked leucoencephalopathy	Tsurusaki et al. J Med Genet.

Familial Cancer Syndromes

Sequencing of individuals with hereditary cancer syndromes enabled the identification of some new cancer susceptibility genes. This category will undoubtedly explode in the coming year as thousands of cancer patients have their genomes or exomes sequenced.

Hereditary Cancer Syndromes
MAX	Hereditary pheochromocytoma	Comino-Mendez et al. Nat Genet.
RET	Familial medullary thyroid carcinoma	Qi et al. PLoS One.

Metabolic Disorders

Next up, metabolic disorders. Interestingly, a study by Vissers and colleagues linked germline variants in IDH1 — a gene recurrently mutated in leukemia, glioblastoma, and other cancers — to “metaphyseal chondromatosis”, a rare disorder of severe bone dysplasia, neurodevelopmental problems, and strongly increased secretion of D-2-hydroxy-glutaric acid.

Metabolic Disorders
ACSF3	Combined malonic and methylmalonic aciduria	Alfares et al. J Med Genet. and Sloan et al Nat. Genet.
MTHFD1	Novel inborn error of folate metabolism	Watkins et al. J Med Genet.
IDH1	Metaphyseal chondromatosis with aciduria	Vissers et al. Am J Med Genet A.

Blood and Lymphatic Deficiencies

Several inherited deficiencies of the blood and lymphatic system were linked to causal mutations. What I liked about this category was that half of the publications came out in “non-genome” journals — Blood and Haematologica — indicating that medical specialists in the field recognize the importance of (and in some cases, are already applying) exome sequencing to study such diseases.

Blood and Lymphatic Disorders
NBEAL2	Gray platelet syndrome	Albers et al. Nat Genet.
GATA2	Dendritic cell, monocyte, B and NK lymphoid deficiency	Dickinson et al. Blood.
MPL	Familial aplastic anemia	Walne et al. Haematologica.
GJC2	Primary lymphoedema	Ostergaard et al. J Med Genet.

Neurological Diseases

Neurological disorders win the prize for making me look up the layman’s term for virtually every disorder whose causal gene was pinpointed by sequencing this year. These include such genes as lipofuscinosis (excessive accumulation of lipopigments), paraparesis (lower limb paralysis), and dystonia (abnormal muscle tone leading to movement and stature problems).

Neurodegenerative Disorders
DNAJC5	Adult neuronal ceroid-lipofuscinosis	Benitez et al. PLoS One.
KIF1A	Hereditary spastic paraparesis	Erlich et al. Genome Res.
GCDH	Early-onset generalized dystonia	Marti-Masso et al. Hum Genet.
FA2H	Fatty acid hydroxylase-associated neurodegeneration.	Pierson et al. Eur J Hum Genet.
AFG3L2	Spastic ataxia-neuropathy syndrome	Pierson et al. PLoS Genet.
BANF1	Hereditary progeroid syndrome	Puente et al. AJHG.
DYNC1H1	Dominant axonal Charcot-Marie-Tooth disease.	Weedon et al. AJHG.

Myopathies

New disease genes were identified for several muscle fiber diseases (myopathies), including cardiomyopathy (heart muscle deficiency, usually fatal) and ophthalmoplegia, in which the muscles that control eye movement are paralyzed. Interestingly, the two mitochondrial cardiomyopathy disease genes (MRPL3 and AARS2) reported both encode products required for mitochondrial ribosomal function (MRPL3 encodes a ribosomal sub-unit, while AARS2 encodes a t-RNA synthetase).

Myopathies
MRPL3	Mitochondrial cardiomyopathy	Galmiche et al. Hum Mutat.
AARS2	Infantile mitochondrial cardiomyopathy	Gotz et al. AJHG.
RRM2B	Progressive external ophthalmoplegia	Takata et al. Genome Biol.
BAG3	Dilated cardiomyopathy	Norton et al. AJHG.

Vision-loss Disorders

The last disease category I’ll mention is that of vision (loss) disorders. A number of new disease-causing genes were identified this year, mostly by exome sequencing. Two studies were particularly interesting. First, Bowne and colleagues (including myself) identified a mutation in the RPE65 gene causing autosomal dominant retinitis pigmentosa. This gene had only been associated with autosomal recessive RP; finding that it acts in dominant fashion suggests previously unknown routes of disease pathogenesis and new therapeutic possibilities. Second, Shi et al linked mutations in the ZNF644 gene to high myopia (severe nearsightedness), a common cause of blindness. Have you ever heard of a ZNFxxx gene that actually does something? Most of the time, you look these up and it says “May be involved in transcriptional regulation.” It’s good to know that at least one of them serves a purpose, namely, keeping most of us from virtual blindness.

Vision Disorders
RPE65	Retinitis pigmentosa with choroidal involvement	Bowne et al. Eur J Hum Genet.
MAK	Retinitis pigmentosa	Ozgul et al. AJHG.
ZNF644	High myopia	Shi et al. PLoS Genet.
MAK	Retinitis pigmentosa	Tucker et al. PNAS.
ALMS1 IQCB1 CNGA3 MYO7A	Leber congenital amaurosis	Wang et al. Hum Mutat.
DHDDS	Retinitis pigmentosa	Zuchner et al. AJHG.

And there you have it. The genetic basis of dozens of inherited disorders, pinpointed by next-generation sequencing. There is simply no plausible way to deny the importance of next-generation sequencing to advancing human health and medicine. One can only imagine what we’ll know by next December, as large federally-funded initiatives ramp up their efforts to systematically apply exome and whole-genome sequencing to inherited disorders.

References
Shendure, J. (2011). Next-generation human genetics Genome Biology, 12 (9) DOI: 10.1186/gb-2011-12-9-408

Recurrent splicing mutations in MDS and leukemia

December 15, 2011 by Dan Koboldt

Myelodysplastic syndrome (MDS, also called preleukemia) is a blood disorder characterized by ineffective production of myeloid cells, or leukocytes. The disorderly and ineffective production of blood cells from stem cells in the bone marrow results in low blood counts, or cytopenias. As many of 30% of MDS cases progress to full-blown, chemotherapy-resistant secondary AML. This week in Nature Genetics, two studies report recurrent mutations in splicing-related genes in blood tumors.

First, Tim Graubert and colleagues describe the whole-genome sequencing of an MDS-derived secondary AML tumor and a matched normal (skin) sample. They detected and validated 507 somatic single-nucleotide variants in the tumor, nearly all of which (505) were detected in the MDS sample. Among these were 30 coding SNVs, of which one was a missense mutation in the U2AF1 gene. The same codon of U2AF1 was also mutated in two other MDS cases evaluated by WGS, highlighting it as a potential recurrently mutated gene. The authors undertook systematic sequencing of U2AF1 exons in 150 MDS cases, and found that 8.7% had mutations at Ser34.

Characterization of Recurrent U2AF1 Mutations

The authors undertook deep genomic resequencing, cDNA sequencing, and other experiments to characterize the nature of the U2AF1 mutations, finding that:

Mutant allele frequencies were at 40-50%, suggesting that the mutation was present in most or all tumor cells.
• SNP arrays and WGS indicated no large deletions or uniparental disomy spanning the U2AF1 locus.
• Deep cDNA sequencing demonstrated that both wild-type and mutant alleles were expressed.
• There were no apparent differences in the amount of U2AF1 mRNA between wild-type and mutated samples.
• In the 150 cases examined, no other positions in the gene were mutated (other than residue 34).

Taken together, these observations suggest that U2AF1 alteration was an early, initiating event and likely represents a gain-of-function mutation.

U2AF1 and Splicing Factors

U2AF1 encodes a small regulatory subunit of the U2AF splicing factor. It binds the 3′ AG splice acceptor dinucleotide of the pre-mRNA target intron, and forms a heterodimer with U2AF2, which binds the adjacent polypyrimidine tract. U2AF1 is highly conserved, and loss of both copies is lethal in many species. Although it’s not known which domain of U2AF1 binds the mRNA, the Ser34 mutation occurs in a zinc-finger motif that may have RNA binding activity. Interestingly, in vitro reporter assays revealed that the Ser34 mutation causes an increase in splicing activity and more exon skipping relative to wild-type U2AF1. Further, an analysis of differentially expressed genes (by microarray) between samples with or without U2AF1 mutations revealed that three of the top functional categories for down-regulated genes were splicing- or RNA-recognition-motif-related genes. This observation may reflect one or more compensatory mutations for the increased splicing activity of U2AF1 mutants.

Recurrent Mutation of SF3B1 in Chronic Lymphocytic Leukemia

A second study in Nature Genetics, led by Victor Quesada and colleagues, employed exome sequencing to identify recurrent mutations in chronic lymphocytic leukemia (CLL), the most common form of adult leukemia in western nations. The authors sequenced the exomes of tumor samples and matched controls from 105 patients with CLL, 60 of which had mutated IGHV regions (a common alteration in CLL) and 45 of which did not. They reported ~45 somatic mutations per case, and observed more protein-altering mutations in IGHV-mutated samples (12.8 +/- 0.7) than non-IGHV-mutated (10.6 +/- 0.7). Comparing this study to their previous work (WGS of 4 CLL cases), the authors identified several new recurrently-mutated genes, including:

• SF3B1, a subunit of the spliceosomal U2 snRNP11;
• POT1, a nuclear protein involved in telomere maintenance12;
• CHD2, which regulates gene expression by modification of chromatin structure13
• LRP1B, which has recently been defined as a tumor suppressor in different malignancies

The authors focused on SF3B1 mutations, which was altered by somatic point mutations in ~10% of cases. Systematic screening of 279 cases by 3730 sequencing revealed that 9.7% of CLL tumors harbored SF3B1 mutations, making this the most frequently mutated gene in CLL identified to date. The protein encoded by SF3B1 is involved in the binding of the U2 snRNP to the branch point near 3′ splice sites. It interacts with RNA sequences and at least two proteins near the branch point: the early 3′-splice-site recognition factor U2AF65 and the branch point–binding protein SF3B14, as well as the RNA sequences near the branch point.

RNA-seq of SF3B1-mutated cases revealed some patterns of aberrant splicing, most of which paired a known 5′ donor site with a new, abnormal 3′ acceptor site. An analysis of splicing target genes revealed truncated versions of SLC23A2, a vitamin C transporter, and TCIRG1, one of whose gene products is a T-cell immune regulator. Another altered gene was FOXP1, known to be dysregulated in diffuse large B-cell lymphoma; the altered transcript lacked two PEST sequences normally required for protein degradation.

Role of Splicing in Tumor Development and Progression

Most adult tumors harbor hundreds or thousands of somatic mutations, only a fraction of which are likely to drive development and growth. Recurrence of mutations in the same gene or pathway remains the best way to isolate these “driver” mutations from background passenger events. These two studies, and a handful of others published late this year, suggest an important role for aberrant splicing in the early development of myeloproliferative disorders, such as MDS/sAML and CLL. What’s particularly important is that these appear to be gain-of-function mutations, which opens the door to new potential targeted therapies. It’s one step closer to personalized medicine for cancer patients, brought to you by next-generation sequencing.

References

Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, Krysiak K, Harris CC, Koboldt DC, Larson DE, McLellan MD, Dooling DJ, Abbott RM, Fulton RS, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Grillot M, Baty J, Heath S, Frater JL, Nasim T, Link DC, Tomasson MH, Westervelt P, Dipersio JF, Mardis ER, Ley TJ, Wilson RK, & Walter MJ (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature genetics PMID: 22158538

Quesada V, Conde L, Villamor N, Ordóñez GR, Jares P, Bassaganyas L, Ramsay AJ, Beà S, Pinyol M, Martínez-Trillos A, López-Guerra M, Colomer D, Navarro A, Baumann T, Aymerich M, Rozman M, Delgado J, Giné E, Hernández JM, González-Díaz M, Puente DA, Velasco G, Freije JM, Tubío JM, Royo R, Gelpí JL, Orozco M, Pisano DG, Zamora J, Vázquez M, Valencia A, Himmelbauer H, Bayés M, Heath S, Gut M, Gut I, Estivill X, López-Guillermo A, Puente XS, Campo E, & López-Otín C (2011). Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nature genetics PMID: 22158541

Somatic Mutation Detection in Whole Genome Sequencing Data

December 8, 2011 by Dan Koboldt

A paper online at Bioinformatics describes our flagship algorithm for detecting somatic point mutations in whole-genome sequencing of tumor samples. This freely available software package, called SomaticSniper, performs a Bayesian comparison of the genotype likelihoods in tumor and normal samples at every [covered] position in the genome.

Overview
Documentation
Install

The study includes a detailed investigation of common sources of false positive mutation calls (usually from sequencing- or alignment-related artifacts) and describes a filtering strategy to remove them from mutation callsets.

Inception: First Cancer Genomes

Like many bioinformatics algorithms, SomaticSniper reached publication after a long and colorful history. It began in 2008 when we sequenced the first cancer genome, AML1. At the time, we were generating fragment-end, 32 bp reads on early Illumina GA instruments. It took over a hundred lanes to achieve ~30-fold coverage on each sample (tumor and normal). We were in dire need of a short read aligner that could handle this amount of data, and Maq answered the call (see my Maq Top Ten).

In addition to serving as one of the most widely used short read aligners, Maq included a probabilistic genotype calling model for detecting germline SNPs in a single genome. Dave Larson (the lead author) and others from our group developed an algorithm to compare genotype likelihoods between tumor and normal, to compute the probability that a site is not somatic given the sequence data. Putative somatic mutations receive a somatic score, a Phred-scaled value representing the quality of the call. Here’s something interesting: during the data generation phase for AML1, as we added more sequence, the number of candidate mutations went down. This is because only a tiny fraction of variants in a tumor genome are somatic; the vast majority are germline variants also present in the normal. As better coverage was achieved, more and more variants turned out to be germline. By the end, it turned out that there were just ten somatic coding mutations in the tumor genome of AML1, a cytogenetically normal leukemia. A lot of people were flabbergasted. Ten little changes, and a woman got leukemia.

More Genomes, Better Algorithm

This algorithm became the core of our cancer whole-genome sequencing analysis pipeline, evolving and improving over the course of the second cancer genome (AML2) in the New England Journal, a breast cancer genome (BRC1), and others. It found, among others, mutations in IDH1 and DNMT3A that we and others showed to be recurrent across many tumors. The algorithm’s name changed a few times, settling at last on SomaticSniper. It’s now a lean and hungry animal, capable of processing high-coverage whole-genome sequence pairs in a matter of hours.

Filtering Out the Noise

No matter how good the mutation caller, there are going to be some false positives. This is because you’re looking for a one-in-a-million event, a true somatic mutation. Raw SomaticSniper calls therefore undergo a series of Maq-inspired filters. Sites are retained if they meet these criteria:

Covered by at least 3 reads
Consensus quality of at least 20
Called a SNP in the tumor sample with SNP quality of at least 20
Maximum mapping quality of at least 40
No high-quality predicted indel within 10 bp
No more than 2 other SNVs called within 10 bp

Sites passing these criteria are subjected to two additional filters: a screen against germline variants from dbSNP (remove if matches position and allele of known non-cancer dbSNP) and an LOH filter (remove if normal is heterozygous and tumor homozygous for the same variant allele). Sites removed by the former are probably inherited variants under-sampled in the matched normal, while sites removed by the latter are likely due to large-scale structural changes (e.g. deletions) causing the loss of one allele. Finally, the filter-passed mutations are classified as high-confidence (HC) if the somatic score is at least 40 and the mapping quality is at least 40 (for BWA) or 70 (for Maq).

Frequent Sources of False Positives

Even sites that pass the filters above are vulnerable to certain sequencing and alignment artifacts that produce false positive calls. A detailed study revealed (as many in the field know already) a few common sources of false positives: strand bias, homopolymer sequences, paralogous reads (deriving from a paralogous region of the genome, but mapped to the wrong region, usually three or more substitutions), and the read position of the predicted variant. The latter type of artifact is something new; it turned out that variants only seen near the “effective” 3′ end of reads (the start of soft-trimmed bases or the actual end of the read if untrimmed) were more likely to be false positives. This may be a combination of sequencing error, which is higher at the 3′ end of reads, and alignment bias favoring mismatches over gaps near the ends of reads. In any case, false positives deriving from these common causes tend to have certain properties enabling them to be identified and removed while maintaining sensitivity for true mutations.

SomaticSniper adds to the growing arsenal of tools developed by our group to address the significant challenges presented by next-generation sequencing data analysis.

References

Larson, DE., Harris, CC., Chen, K., Koboldt, DC., Abbott, TE., Dooling, DJ., Ley, TJ., Mardis, ER., Wilson, RK., & Ding, L. (2011). SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data Bioinformatics, Online : doi: 10.1093/bioinformatics/btr665