MassGenomics

As promised, NGS instruments are yielding thousands of new genome sequences. Read lengths and throughputs are increasing. Alignment and analysis algorithms are getting more mature. Databases of sequence variants are growing exponentially.  Things are looking pretty good, right? Sure, there are lots of variants still waiting to be discovered. Sure, some of those already reported simply aren’t real. But I think we’re rapidly approaching a point where finding the variants won’t be much of a problem.

Instead, we are facing two significant challenges. First, identifying the subset of variants have functional significance – separating the wheat from the chaff, if you will. Second, understanding how these functional variants contribute to a phenotype. This is soon to be the frontier in genetics and genomics. It merits, I think, a discussion of some of the strategies that have been used to go beyond variant detection, to isolate disease-causing variants and assess their functional impact.

Strategy 1: Process of Elimination

This approach (to my knowledge) is best demonstrated in whole-genome, exome, or pooled sequencing of samples from individuals with rare inherited diseases. It’s essentially a filtering strategy where you start with a list of candidate variants and whittle it down using several criteria:

• Pedigree information, especially variants that do not segregate with the disease in Mendelian disorders.
• Control variants, usually identified in HapMap samples or other individuals not affected by the disease.
• Gene structure information, which serves to eliminate synonymous or non-coding variants.
• Evolutionary conservation, to prioritize variants in sequences that are conserved across species.

This strategy has worked well for a handful of rare, inherited diseases like Miller syndrome and severe hypercholesterolemia. There are, however, so many things that can go wrong. The pedigree or assumed mode of inheritance could be wrong. The causal variant might be synonymous or even noncoding (e.g. in a transcription factor binding site). The conservation trick in particular worries me. True, many of the known disease-causing mutations map to conserved amino acid residues, but certainly not all of them.

Strategy 2: Recurrence

This is a developing strategy to identify key mutations and pathway alterations in cancer genomes. Because tumors are genetically unique, and often possess thousands of acquired (somatic) mutations, pedigree analysis and control samples are less informative. Instead, we reason that passenger mutations should occur randomly, mutations key to tumor development and progression are likely to be recurrent (i.e. found in other tumors of the same type). By this reasoning, the more important a mutation, the higher its rate of recurrence. TP53 mutations are a good example of this; in ovarian cancer, more than 80% of tumors carry a TP53 mutation. This is why databases like Sanger’s Catalogue of Somatic Mutations in Cancer (COSMIC) are such powerful tools. As these catalogues grow, having an available panel of additional tumors to screen for novel mutations may become less critical.

Strategy 3: Computational Evaluation

A growing suite of tools and annotation databases enable computational assessments of putative variants to predict their effect in vivo. SIFT and Polyphen are well-known examples of these. The UCSC Genome Browser Database contains dozens of genome-wide annotation datasets (both computational and experimental); many of these are presumed-regulatory regions that form the basis for our “Tier 2″ classification (non-coding conserved/regulatory variants). There are also motif-scoring algorithms that evaluate a mutation’s effect on the binding affinity of trancription or splicing factors. These types of inferences are both interesting and helpful, when assessing a mutation’s functional effect. They’re not convincing, however, without supporting experimental evidence.

Strategy 4: Molecular Validation

This may be the most difficult strategy, but potentially the most informative one. A myriad of experimental techniques can be applied to assess a mutation’s functional effect in vivo or in vitro. For coding mutations, the first thing we typically assess is mRNA expression (by RT-PCR or RNA-Seq), to determine (1) if the affected gene is expressed in the tissue of interest (e.g. the retina for studies of retinitis pigmentosa) and (2) whether the mutant allele affects it. Many known disease-causing mutations ablate expression of the mature mRNA, because they introduce splicing defects, mRNA instability, or other effects. A number of other molecular biology tools can also be applied:

• Western blot, to determine protein expression
• Enzyme activity assays, such as the complex I rescue technique that has been applied to characterize mutations in patients with complex I deficiency (see my last post).
• Recombinant DNA techniques, such as a luciferase assay to assess mutations in gene promoters
• Colony growth assays, especially for somatic mutations, to determine if mutations confer a growth advantage or invasion potential.

Specialized Sequencing Techniques

A number of recently-developed applications of massively parallel sequencing can be used to assess the functional impact of candidate mutations. RNA-Seq can detect allele-specific expression and alternative splicing. ChIP-Seq can assess protein-DNA interactions and theoretically detect allele-specific DNA binding. Methyl-Seq can be used to profile DNA methylation, either at specific loci or (for methylation pathway mutations) genome-wide. MiRNA-Seq and HITS-CLIP, techniques that measure microRNA expression or isolate miRNA-transcript interactions, also have potential for characterizing mutation effects. Many of these high-throughput techniques stand poised to supplant their traditional experimental counterparts.

Given the wide array of experimental tools, it’s disappointing when reports of new (possible) disease-causing mutations lack sufficient functional validation. I find myself unconvinced when the answer is supported by “it segregates with the disease” or worse, “we filtered everything else.” So when I read new papers that claim to have identified disease-causing variants, my answer is this: Great mutation. Is it functional?

When it comes to massively parallel sequencing, few areas of human health stand to benefit as much as rare genetic diseases. Indeed, both whole-genome and exome sequencing strategies have identified disease-causing mutations in probands with Charcot-Marie Tooth disease, Miller syndrome, severe brain malformations, and a few other disorders. The Mito10K project took a different approach. They assembled a cohort of mostly unrelated individuals with complex I deficiency (n=103), the most common cause of human respiratory chain diseases.

Mitochondrial Electron Transport Chain (Wikipedia)

Forty-two HapMap samples were included as controls. Instead of employing a whole-genome or exome strategy, they performed deep resequencing of carefully-chosen candidate genes in pools of ~20 samples. And they did it all using a single Illumina flowcell.

Pooled Sequencing of Candidate Genes

The candidates included 103 genes that (i) encoded known complex I proteins, (ii) were implicated in the disease, or (iii) were identified by phylogenetic profiling. The 145 kb target space comprised 653 exons from nuclear genes (138 kb) and two mtDNA regions (7 kb). About 90% of target regions achieved at least 100x coverage; the median redundancy was 3,359x per pool, which works out to ~168x per individual. Next, the authors developed a method (“Syzygy”) to model sequencing error and call variants at very low frequencies. A comparison of calls for the HapMap samples to existing genotype data suggested 92% sensitivity and 99.6% specificity, at sites where coverage was 100x or greater.

Although the pooling strategy worked well for nuclear DNA, there were some problems with the targeted regions in mtDNA. Basically, the distribution of mtDNA was not uniform between samples. That may be due to the fact that while each cell contains exactly 2 copies of each nuclear chromosome, it contains numerous mitochondria and thus numerous copies of the MT chromosome (possibly 20-25 per cell, by one estimate). The resulting shift in sample representation can be quite dramatic. In one pool, for example, 96% of the mtDNA came from a single individual (5% of the pool). The bottom line is that sensitivity to call mutations in pooled samples is going to be lower for mtDNA.

Variant Calling and “Deleteriousness” Prioritization

The unfortunately-named Syzygy method identified 652 variants (high confidence); to boost sensitivity, the authors also employed an ad-hoc approach that called 246 more variants supported by at least 3 reads on each strand (low confidence). The 898 calls were filtered to prioritize variants that seemed likely to underlie a rare and devastating phenotype. In short, the authors removed:

• Variants present in healthy individuals (HapMap controls) or public databases (dbSNP, mtDB, 1000 Genomes).
• Synonymous or noncoding variants, unless they affected tRNA or splice sites.
• Missense variants at positions of low evolutionary conservation

Of 898 detected variants, 216 remained and were validated by multiplexed Sequenom genotyping. Some 82 sites were also Sanger-sequenced to assess the accuracy of the genotyping platform. The comparison revealed 11% false positives and 2% het/hom miscalls, for an overall error rate of 13% for Sequenom assays. Ouch. As for the variant calls, the validation rate was pretty good for high-confidence calls (91/109, or 84%) but rather abysmal for the low-confidence ones (12/107, or 11%).   Intriguingly, validation assays identified 12 additional pathogenic variants that were missed by the discovery screen. Based on these data, the sensitivity of the Syzygy method alone was 79.1% (91/115). That’s not bad, but probably not enough for a study whose goal is to identify rare disease-causing variants.

New Diagnoses from Validated Mutations

Some 60 of the sequenced cases lacked a previous molecular-genetic diagnosis. Among these, the authors were able to provide 11 new diagnoses based on mutations in known disease-causing genes. Several lines of supporting evidence were given to support the diagnoses:

• 6 patients had mutations that were previously known to be disease-causing.
• 3 patients were homozygous for deleterious mutations that caused splicing defects (observed in cDNA) and no detectable protein (by SDS-page and protein blot).
• 2 patients had mutations in highly conserved protein domains.

Intriguingly, half of the cases with known mutations (3/6) were compound heterozygotes; that is, they inherited a different defect in the same gene from mother and father. This apparent prevalence of compound hets in monogenic disease is unsettling because they tend to make pedigree analysis complicated and require detection of both variants in heterozygous form, which is more difficult to do by sequencing.

Detection and Characterization of Novel Disease Genes

The key finding of this paper (as suggested by the title) was the implication of two new genes in complex I deficiency: NUBPL and FOXRED1. Pathogenicity of each mutated genes was confirmed by a “rescue” assay in which introduction of wild-type cDNA into patient fibroblasts restored complex I activity. In the absence of rescue, residual complex I activity was markedly reduced (19-40%) in the NUBPL-mutated fibroblasts and strikingly reduced (9-15%) in the FOXRED1-mutated fibroblasts.

The case with NUBPL mutations was particularly interesting. RT-PCR showed that the dominant mRNA species was truncated, and the full-length transcript hardly expressed at all. Sequencing revealed that the shortened fragment had a branch site mutatation that likely caused exon 10 skipping, as well as a missense mutation (Gly56Arg), both on the paternal chromosome. The maternal allele wasn’t expressed. Array-based copy number analysis, however, showed that the maternal chromosome had a complex rearrangement of NUBPL in which exons 1-4 were deleted and exon 7 was duplicated. Obviously this structural variation was not detected in the discovery screen. I think this highlights two things: the importance of structural variation in human disease, and the limitations of targeted sequencing on NGS platforms.

Success and Limitations

As the authors note in their discussion, key to the success of this study was the availability of cellular models of disease, with which the pathogenicity of newly discovered mutations in individual patients could be established. With the two new findings, the 11 newly diagnosed cases, and the 40 or so already-diagnosed cases, the authors now have identified the genetic defect for about half of the cases in their cohort.  What about the rest?  The authors admit that the causal mutations were likely missed because:

1. They occur in genes not targeted in this study
2. They affect targeted genes, but reside in noncoding regulatory regions or novel/unknown exons
3. They were targeted, but not detected due to limited sensitivity (especially in mtDNA)
4. They were detected, but filtered out as not likely to be deleterious
5. They are large-scale deletions or rearrangements, which this approach can’t detect

Despite these limitations, the authors have demonstrated that sequencing carefully-chosen candidate genes in pooled samples, with follow-up validation and experimental support, can successfully identify disease-causing mutations in a good-sized patient cohort. Not bad for a single flowcell.

References

Calvo, S., Tucker, E., Compton, A., Kirby, D., Crawford, G., Burtt, N., Rivas, M., Guiducci, C., Bruno, D., Goldberger, O., Redman, M., Wiltshire, E., Wilson, C., Altshuler, D., Gabriel, S., Daly, M., Thorburn, D., & Mootha, V. (2010). High-throughput, pooled sequencing identifies mutations in NUBPL and FOXRED1 in human complex I deficiency Nature Genetics, 42 (10), 851-858 DOI: 10.1038/ng.659

Ng SB, Buckingham KJ, Lee C, et al (2010). Exome sequencing identifies the cause of a mendelian disorder. Nature genetics, 42 (1), 30-5 PMID: 19915526

Bilgüvar K, Oztürk AK, Louvi A, et al (2010). Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature, 467 (7312), 207-10 PMID: 20729831

Lupski JR, Reid JG, Gonzaga-Jauregui C, et al (2010). Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. The New England journal of medicine, 362 (13), 1181-91 PMID: 20220177

Lalonde E, Albrecht S, Ha KC, et al (2010). Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next-generation exome sequencing. Human mutation, 31 (8), 918-23 PMID: 20518025

Vivian Cheung and colleagues have published a landmark study on polymorphic cis- and trans-regulation of human gene expression that combines genetic association and transcriptome sequencing (RNA-Seq) to identify 1,000 polymorphic cis- and trans-regulators of gene expression.

Two key features distinguish this from other recent studies on genetics of gene expression (GOGE) in humans: first, linkage mapping followed by RNA-Seq allowed association-based fine mapping of regulatory regions with unprecedented resolution. Second, the authors supported their findings with a series of molecular assays, bridging the gap between genetic association and mechanistic validation.

Differential Findings: Model Organisms to Humans

Previous GOGE studies in humans and model organisms seem to disagree on the relative proportion of cis- versus trans- regulators. In humans, most reported regulators have been in cis with their target genes, and the discovery of cis-regulation of disease susceptibility genes (e.g. ORMDL3) reinforces the notion that these play an important role in gene expression variation. However, studies of GOGE in model organisms (yeast, fly, and mouse) identified mostly trans regulators. It seems unlikely that this doesn’t hold true for humans.

It’s a pretty big discrepancy, and one that the authors of this study attribute to sample size, which tends to be large in model organism studies and small in human ones. To address this, they studied gene expression in B-cells of individuals from large families. Linkage scans provided the initial regulatory regions for >1,600 expression phenotypes. The authors applied family-based and association-based analyses to narrow the candidate regions.

Cis-regulatory Variation

There were 107 linkage peaks that were proximal to (nearby) their target genes, and thus likely to act in cis. The authors refined this list with a series of further tests:

• 100 phenotypes had informative genotypes within 50kb for family-based testing
• 63 were significant in families by the quantitative transmission disequilibrium test (QTDT).
• 47 had significant population-based association in 86 unrelated individuals that were tested.

For 17 of the resulting phenotypes, the cis-variants explained 30% or more of the variation in gene expression.

Trans-regulatory Variation

There were 1,611 phenotypes with significant distal linkage peaks using the QTDT. The authors excluded 94 of these whose candidate regulatory regions were >20 mbp in size. That left 1,517 phenotypes to examine. Since these likely acted in trans, there was no obvious nearby gene for testing. One option would be to test all SNPs under the linkage peaks for association, but this would be a statistician’s nightmare multiple testing problem. Instead, the authors applied RNA-sequencing to 41 CEU samples to identify genes expressed in B-cells and test them for association with expression of their target genes. A key advantage of RNA-Seq over, say, microarray-based methods is the sensitivity to detect genes expressed at low levels; often, these include gene expression regulators like transcription factors. Their analysis revealed a total of 1,036 regulator-target gene pairs (103 with p<0.001, 518 with p<0.01, and 917 with p<0.05). Interestingly, the expression levels of 112 genes in the complete set were regulated by two unlinked trans regulators.

Molecular Validation Assays

The authors applied three types of molecular validation to confirm the regulator-target relationships implicated by their findings. For cis-regulatory variants, they used heterozygous SNPs in the RNA-Seq data to detect differential allelic expression (DAE). Of the 67 genes tested, 43 (64%) showed significant evidence of allele-biased expression. Altogether, ~65% of proximal linkages were cis-regulated; the remainder were either regulated in trans by nearby sequences, or did not achieve sufficient RNA-Seq coverage to detect allelic bias.

For trans-regulatory pairs, they performed gene knockdowns with siRNA, showing that the expression level of 72% of target genes tested changed significantly (usually 10-60%) when their regulator was knocked down by siRNA. The authors also examined physical interactions between their regulators and target genes using high-throughput sequencing of chromosome conformation capture (“Hi-C”) data from another study.

Chromosome conformation capture (Wikipedia)

Some 75 of the 1,036 regulator-target pairs were supported by Hi-C data. These data support the genetic findings, and suggest that regulators and their target genes may be co-transcribed in physically associated “transcription factories”.

Conclusions

This study has shed new light on the complexity of gene regulation in humans. Importantly, the majority of regulators act in trans to their target genes. Yet only 34% of the identified trans regulators are transcription or signaling factors, suggesting that many other types of genes can influence gene expression. Many of the regulators, however, were in the same functional pathways as their target genes.

So how do polymorphisms in regulator genes act in trans? One possibility is that polymorphisms near a gene affect its expression, which in turn modulates the expression of its target gene. Another explanation is that sequence variants in the regulators alter the translation, stability, or structure of the regulator, which in turn influences how it regulates the target gene. Alternatively, regulator-target gene pairs may associate physically with one another in so-called “transcription factories” in which polymorphisms in the regulator affect co-transcription of both genes. All of these are intriguing possibilities, and unraveling them mechanistically with functional studies will undoubtedly improve our understanding of gene expression regulation in humans.

References
Cheung VG, Nayak RR, Wang IX, Elwyn S, Cousins SM, Morley M, & Spielman RS (2010). Polymorphic cis- and trans-regulation of human gene expression. PLoS biology, 8 (9) PMID: 20856902