Genome Content and Function
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Medical genomics in the post-genome era
New Insights into Long Noncoding RNAs
March 3, 2017 by Dan Koboldt
As long-time readers of MassGenomics probably know, I'm fascinated by studies that interrogate functional elements of the human genome. The ENCODE Project is perhaps the most visible consortium effort in the United States, employing a variety of high-throughput genomic technologies such as RNA sequencing (expression), DNase I sequencing (open chromatin), and CHiP-Seq (DNA-protein interactions).
However, other groups have made major contributions recently, notably RIKEN-led FANTOM consortium. In the past few years, FANTOM researchers have applied Cap Analysis Gene Expression (CAGE), a genomic technology developed at RIKEN, to numerous mammalian cell and tissue types. CAGE isolates the 5′ end of long RNA molecules, providing high-resolution mapping of the transcription start sites and core promoters of genes. The current project iteration, FANTOM5, has mapped enhancers and transcription start sites in hundreds of primary human cell types.
This week in Nature, they've published another genomic annotation: an atlas of long non-coding RNAs with accurate 5′ ends. By integrating 1,829 CAGE profiles from the FANTOM project with transcript models from a variety of sources (GENCODE, Human BodyMap, ENODE, and miTranscriptome), the authors constructed a "CAGE-associated transcriptome" assembly cleverly branded FANTOM CAT.
Atlas of Long Noncoding RNA Genes
The 27,919 long noncoding RNA genes in FANTOM CAT represent the most comprehensive catalogue of human lncRNAs so far. The authors next sought to classify lncRNA genes according to genomic and epigenomic context. I found the way this was presented in the paper to be very confusing, so I've broken it down differently here.
First, the authors assigned lncRNAs to an epigenomic class based on the overlap between their strongest transcription start site (TSS) with DNase I hypersensitive regulatory regions defined by the RoadMap Epigenomics Consortium:
Adapted from Hon et al, Nature 2017.
Dyadic here means a DHS that looks like both a promoter and an enhancer. So that's the epigenomic context. The authors also assigned lncRNAs to a genomic context based on their location and transription relative to messenger RNAs:
Adapted from Hon et al, Nature 2017
Divergent here means that a lncRNA seems to use the same TSS as the mRNA, but is transcribed in the other direction. Using these epigenomic and genomic classifications, the authors defined some lncRNA categories for deeper analysis:
Divergent p-lncRNAs (n=5,827) have promoter-like epigenomic signatures and use the same TSS as a known messenger RNA, but are transcribed in the other direction.
Intergenic p-lncRNAs (n=1,725) also have promoter-like epigenomic signatures but do NOT use the same TSS as a known messenger RNA.
e-lncRNAs (n=9,339) have enhancer-like epigenomic signatures (genomic context is ignored). These are the orange slice from the top pie chart.
There were also 10,543 lncRNAs that had have dyadic or undefined epigenomic signatures, or that had a promoter epigenomic signature but were antisense or intronic. These "other" lncRNAs are essentially set aside; in most of their analyses, the authors compare/contrast the three lncRNA categories described above with traditional mRNAs.
Credit: Hon et al, Nature 2017
Conservation of lncRNAs
One way to assess the functional relevance of a genomic region (or set of regions) is to assess the extent of evolutionary conservation across species. In this study, the authors examined whether the transcription initiation region (TIR) and exonic sequences were conserved for each class of lncRNA gene, using mRNAs as a comparator. This type of analysis was motivated by something that I did not know: at some lncRNA loci, the mere act of transcription is functionally relevant, but the actual sequence of the transcript is not.
The image to the right (adapted from Figure 1c) shows the proportion of transcription initiation regions (Y-axis) and exonic regions (X-axis) that overlap GERP-predicted conserved sequences for mRNAs (red), divergent p-lncRNAs (purple), intergenic p-lncRNAs (blue), and enhancer-lncRNA (green). Classic mRNAs, as expected, show conservation at both the TIR and the exonic portions.
In general, exonic regions from all three lncRNA categories were less conserved by comparison. Divergent p-lncRNAs showed high conservation for the TIR (75%) and a reasonable amount for the exon (54%), but remember that their "exonic" portion, by definition, is immediately upstream of an mRNA core promoter. In intergenic regions, 42% of promoter-like lncRNAs and 36% of enhancer-like lncRNAs did not overlap conserved elements.
Interestingly, those non-conserved intergenic TIRs were significantly enriched for retrotransposons, suggesting that retrotransposon activity contributes to the "birth" of new transcription activity in noncoding regions.
lncRNAs Expression Patterns
Next, the authors examined the expression patterns of lncRNAs across a variety of primary cell types. The expression levels of each category were relatively consistent across cell types. Around 45% of divergent p-lncRNAs (purple) and 31% of intergenic p-lncRNAs (blue) were expressed in each primary cell type, but the proportion of enhancer-like lncRNAs expressed (12%, green) was much lower:
Enhancer-lncRNAs were also much more likely to be cell-type-specific, which is consistent with previous studies of enhancer activity. On average, 5,666 lncRNA genes were expressed in each cell type, though the range was fairly wide (3,000-10,000). I notice that many of the higher-activity cell types in the bottom panel are immune cells (basophils, NK cells, etc), which makes sense.
Association with Human Traits
The authors cross-referenced lncRNAs from FANTOM CAT with established GWAS loci, finding that 40.7% of lncRNA genes were associated with at least one trait. Unsupervised clustering of cell-type-specific lncRNAs and trait associations showed that related cell types and traits tend to clump together in biologically plausible ways: for example, lncRNAs enriched in nervous system tissues tended to be associated with neuropathy and behavior traits, and the odds ratios of lncRNA genes were comparable to those of mRNA genes.
The authors identified groups of mRNAs and lncRNAs that are active in the same cell types and associated with the same traits, i.e. "significant cell type-trait pairs." Some 5,490 FANTOM CAT genes were involved in such pairs:
Credit: Hon et al 2017
Most of those were protein-coding genes (mRNAs), but a significant proportion, around 36%, were lncRNA genes. Before we react too much to the distribution among gene categories, let's remember that the relative sizes differ. If I compare the number of genes associated with cell-trait pairs to the total number in FANTOM-CAT, here's what it looks like:
Based on Hon et al, Nature 2017
As we might expect, protein coding genes had the highest proportion of associations (21.7%), and they were about twice as likely as divergent/intergenic p-lncRNAs and thrice as likely as enhancer-like lncRNAs to be involved in a cell type-trait association. Even so, given the massive disparity in research emphasis, I find it compelling that considerable numbers of lncRNAs are implicated in human traits.
It suggests that all of those noncoding genetic associations are not random, but indicative of the intricate regulatory genetic networks underlying complex human traits. Furthermore, it highlights the importance of divergent p-lncRNAs, which utilize the same TSS as protein-coding mRNAs but are divergently transcribed. Given their close proximity and the tendency of investigators to assign a GWAS hit to the nearest protein-coding gene, I wonder how often a genetic signal from a p-lncRNA is erroneously assigned to the mRNA instead.
Filed Under: Genome Content and Function, Human Genetics
Whole Genome Sequence Analysis of Complex Traits
January 27, 2017 by Dan Koboldt
As you probably know, I'm a fan of exome sequencing, particularly for studies of rare inherited disorders. While not a perfect (or comprehensive) assay, exome sequencing offers an efficient screen of the regions most likely to harbor disease-causing mutations. Ironically, another reason people like exome sequencing is because of the limited scope: it essentially doesn't interrogate the regions (noncoding) and variant types (SVs) that are more difficult to interpret.
Well, the party's over.
Several large scale whole-genome sequencing studies of human disease are hurtling forward in the U.S., the U.K., and other countries. We've put it off as long as we can, but now we're faced with the daunting task of identifying and interpreting biologically-relevant variants outside of protein-coding exons.
There's good reason to do so, by the way, especially studies of common disease in which regulatory activity is likely to play an important role. Many, if not most of the genomic loci associated with human traits lie in noncoding regions. According to datasets generated by projects like ENCODE and FANTOM5, noncoding regions also exhibit an astonishing amount of biochemical activity suggestive of diverse functions.
Sometimes with a daunting analysis task, it's hard to know where to start. Fortunately, there's a nice paper in the upcoming issue of AJHG that provides some practical guidance. Alanna C. Morrison et al present a series of integrated steps for whole-genome analysis and apply them to study 10 heart- and blood-related traits in 1,860 African Americans.
The authors perform aggregation tests (sometimes called burden tests) to test sets of rare variants for association. Such tests require that one define a unit by which to group variants. With exome sequencing, this was commonly done on a per-gene or per-exon basis. In this study, with WGS available, the authors aggregated variants:
In a sliding 4-kb window across the entire genome
In the first introns of protein-coding genes, which are known to harbor regulatory elements
In pre-defined regulatory domain motifs (promoters, enhancers, and UTRs) near genes.
This is, in modern-day terms, a modestly powered study of complex quantitative traits. Even so, the authors found several significant associations, some near known loci for these traits, and others in potentially novel regions. The results of their complementary methods are illustrated in Figure 3:
Morrison et al, Am. J Hum Genet, 2017
You're looking at the association of Lp(a) levels in the well-known LPA gene locus. The sliding window approaches nicely captured association over the LPA gene region, but also in other nearby regions, some of which were also supported by the first-intron or regulatory-motif analyses. It's a complicated picture, to be sure, but it suggests some specific areas in which noncoding variants are exerting a cis-regulatory effect on the LPA gene. Very cool stuff.
What's especially nice about this paper is that it provides relatively straightforward methodology for tackling the daunting analysis task I talked about above:
Obtain WGS data for a well-phenotyped cohort
Define some common-sense strategies for aggregating (grouping) rare variants
Apply aggregation tests on a genome-wide basis
Replicate significant findings in an independent cohort (in this case, about 2,000 European-Americans)
This study demonstrated both the feasibility and the justification for interrogating noncoding regions for association with medically important traits. Imagine how much we'll be able to discover as we get our hands on massive WGS cohorts, and extend these principles to other regions and regulatory motifs of the genome.
Inherited retinal disease genomics at ASHG 2016
October 18, 2016 by Dan Koboldt
Inherited retinal diseases (IRDs) comprise a heterogeneous group of retinal degenerations, including retinitis pigmentosa, choroideremia, Leber congenital amaurosis, and other dystrophies. These disorders exhibit remarkable genetic heterogeneity; mutations in numerous different genes can cause a retinal disorder, and they can be inherited in dominant, recessive, or X‐linked fashion.
I proposed an invited session for the 2016 ASHG meeting because I believe that inherited retinal diseases offer several key advantages as a model system for gene discovery, functional validation, genetic counseling, and individualized treatment.
IRDs as a Model for Discovery and Translation
The genetic heterogeneity within IRDs encompasses a broad set of genes, inheritance patterns, and disease pathways. While the number of patients affected by a single one of these may be small, they provide a wider set of options for targeted therapies.
IRDs typically affect only a single tissue (the retina). Although the genes implicated in these disorders are involved in a variety of pathways, most of them share a molecular phenotype: high (and often transcript‐specific) expression in retinal tissue. This offers a compelling avenue of experimental approaches to functionally validate new candidate disease genes.
The fact that most IRDs are not life threatening allows for long‐term studies and recruitment from an actively engaged patient population. Thanks to decades of work to identify the genes and mutations responsible for IRDs, more than half of patients who undergo routine genetic testing receive a definitive molecular diagnosis. This success, combined with the customary slow onset of many retinal disorders, suggests that new therapeutic interventions that could dramatically improve the quality of life for thousands of patients. Indeed, Leber Congenital Amaurosis (LCA) was one of the first recessive disorders to undergo clinical trials for gene replacement therapy.
The catalogue of pathogenic mutations and disease‐associated genes for IRDs has grown considerably over the past two decades, fueled by both technological advances (i.e. next‐generation sequencing) and well‐established genetic testing programs. So, too, has our knowledge of the molecular mechanisms linking pathogenic mutations to distinct disease phenotypes. Yet there remains a pressing need for innovative approaches to functionally validate genetic discoveries, and to incorporate that information into patient counseling and clinical care.
Our Session at ASHG 2016
I'm pleased to say that the ASHG program committee selected our proposed session for the 2016 meeting! It brings together a diverse panel of experts in gene discovery, molecular characterization, genetic counseling, and treatment for inherited retinal diseases. Together, they will provide a snapshot of the current state of the art in IRD research, use it as a context to discuss the best practices for transforming genetic discoveries into personalized precision medicine.
Title: Gene discovery, genetic counseling, and clinical care of patients with inherited retinal diseases (Session #61).
When: Friday, October 21st, 11:00 a.m. to 1:00 p.m.
Where: Convention Centre Room 302, West Building.
Twitter-friendly? Hell yes.
Here's the schedule:
Gene discovery and mutation detection in families with dominant retinitis pigmentosa. Steve Daiger, University of Texas Health Science Center, Houston.
Integrating genetic testing into the inherited retinal disease clinic. Kari Branham, Kellogg Eye Center, University of Michigan, Ann Arbor.
Functional validation and genetic intervention for retinal disease genes. Val Sheffield, University of Iowa Carver College of Medicine, Iowa City.
From gene discovery to clinical trials for inherited retinal diseases. Eric Pierce, Massachusetts Eye & Ear Infirmary, Boston.
We believe that this session therefore has appeal not just for groups working on retinal diseases, but also the wider community of researchers, genetic counselors, and clinicians who seek to translate genetic discoveries into improved clinical care. I hope to see you there. Please take pity on the moderator, and ask the speakers some questions!
dbSNP exceeds a ridiculous 150 million variants
September 12, 2016 by Dan Koboldt
Earlier this week, I took a look at the dbSNP VCF file for build 147 (human) with Ben Kelly from the White Lab at NCH. Even summary statistics took a while to generate, and soon we realized why: dbSNP now contains a jaw-dropping 152.7 million reference variants. Roughly speaking, that's one variant for every 20.5 base pairs in the human genome. They're not all rare variants, either: 86 million variants are classified as common (G5, G5A, or COMMON), with minor allele frequencies >1% in at least one population.
dbSNP's Ridiculous Growth
During the HapMap project in 2003-2004, we were astonished to see dbSNP hit 10 million variants. My boss at the time, Ray Miller, told me that some thought it could one day hit 50 million. We thought it might take decades, but dbSNP surpassed 50 million refSNPs just seven years later.
dbSNP Growth, 2002-2016
Even more astonishing are the 6+ million coding variants: depending on how you define the exome, that's about one variant every 5 or 6 base pairs in coding regions. Compounded by the fact that a single variant might affect multiple transcripts/genes, the number of observed human coding variants exceeds 22 million.
That being said, the fact remains that the vast majority of known variants in our genome lie outside of protein-coding exons. When annotated with snpEff, there are more than 80 million variants within or nearby genes, where they might play a regulatory role (again, multiple transcripts = multiple annotations per variant).
Noncoding dbSNP annotations.
The Evolving Utility of dbSNP
In the early days of next-generation sequencing, dbSNP provided a vital discriminatory tool. In exome sequencing studies of Mendelian disorders, any variant already present in dbSNP was usually common, and therefore unlikely to cause rare genetic diseases. Some of the first high-profile disease gene studies therefore used dbSNP as a filter. Similarly, in cancer genomics, a candidate somatic mutation observed at the position of a known polymorphism typically indicated a germline variant that was under-called in the normal sample. Again, dbSNP provided an important filter.
Now, the presence or absence of a variant in dbSNP carries very little meaning. The database includes over 100,000 variants from disease mutation databases such as OMIM or HGMD. It also contains some appreciable number of somatic mutations that were submitted there before databases like COSMIC became available. And, like any biological database, dbSNP undoubtedly includes false positives.
On the bright side, however, the rapid generation of genomic data worldwide has enabled deeper characterization of the variants that we know about. The 1,000 Genomes Project contributed genome-wide data for 2,504 individuals from several continental groups, while the Exome Aggregation Consortium (ExAC) has compiled gene-centric data from 60,706 individuals at the time of writing.
The Value of Variant Allele Frequencies
As a central repository for variant allele frequency (VAF) data, dbSNP can be a powerful resource for human genetics studies. Of particular relevance for rare disease genetics are the variant allele frequencies (VAFs) in worldwide populations. For a rare autosomal recessive disorder affecting 1 in 100,000 individuals, compound-heterozygous variants with VAFs of 0.01 in a certain population are too common: their combined frequency is 0.0001, or 1 in 10,000. In contrast, most known disease-causing variants — mutations that have been imported from OMIM, for example — are exceedingly rare.
Thus, while the mere presence of a variant in dbSNP is a blunt tool for variant filtering, dbSNP's deep allele frequency data make it incredibly powerful for genetics studies: it can rule out variants that are too prevalent to be disease-causing, and prioritize ones that are rarely observed in human populations. This discriminatory power will only increase as ambitious large-scale sequencing projects like CCDG make their data publicly available.
Ancestry and Inclusion
Importantly, allele frequency data are most useful when the population matches the ancestry of the sample(s) being studied. In their current form, our databases are skewed towards major population groups (northwest European, East African, and East Asian). Many important geographic and ethnic groups are still under-represented. The reasons for this are complex, and not the focus of this post, but I think we can all agree that it's vital to seek out and include samples from diverse ancestries as large-scale sequencing efforts move forward.
At 150+ million variants, dbSNP is a massive beast, and still offers a useful discriminatory tool when used correctly. Proceed with caution.
MassGenomics is Closed, but KidsGenomics is Open
The End of MassGenomics
The Vital Role of Genetic Counselors
Clinical Genome Sequencing Dominates ASHG 2017
The views expressed on this site do not reflect the opinions of Nationwide Children's Hospital or The Ohio State University.
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