A report on the Cold Spring Harbor Laboratory 27th annual meeting on the Biology of Genomes, held in Cold Spring Harbor, New York, USA, 6-10 May 2014

Carr, Ambrose James

Pe'er, Dana

Genome Biology

PubMed

Carr, Ambrose

Columbia University Academic Commons

Carr and Pe’er Genome Biology 2014, 15:416http://genomebiology.com/2014/15/7/416MEETING REPORTBroadening horizons: holistic viewpoints from theBiology of GenomesAmbrose Carr and Dana Pe’er*AbstractA report on the Cold Spring Harbor Laboratory 27thannual meeting on the Biology of Genomes, held inCold Spring Harbor, New York, USA, 6-10 May 2014.ation is responsible for a large amount of functionalFine mapping in cis discovers substantialfunctional regulatory variation‘Biology of Genomes’ is a broad annual meeting thatshowcases the year’s progress in the many subfields ofgenomics. With so many talks, it is impossible to coverall of them. However, despite the variety of subject mat-ter, several trends recurred throughout the conference.One result that consistently resurfaced across multiplesessions is the ability of enlarged studies of tens of thou-sands of individuals to move beyond linkage to genesand finely map traits to individual regulatory polymor-phisms. Jeffrey Barret (Wellcome Trust Sanger Institute,Hinxton, UK) was able to identify specific genes as con-tributors to disease by using data from 12,000 patientsin the UK10K project. Hailiang Hailiang (MIT, USA)presented results from the inflammatory bowel disease(IBD) consortium that allowed the identification of spe-cific variants that contribute to IBD, and Stephen Parker(NIH Bethesda, USA) presented the results of the on-going FUSION study of type 2 diabetes, where a com-bination of RNA-seq and genotyping was used toidentify muscle expression quantitative trait loci (eQTLs)associated with different physiological stages of type 2diabetes.With the enormous amount of data being gener-ated, it is logical to ask: how much functional vari-ation is found in coding versus noncoding regions ofthe genome? Alexander Gusev (Harvard University,USA) answered this question by annotating the gen-ome using data from Encyclopedia of DNA Elements* Correspondence: dpeer@biology.columbia.eduDepartments of Biological Sciences and Systems Biology, ColumbiaUniversity, 1130 St Nicholas Avenue, New York, NY 10032, USA© 2014 Carr and Pe'er; licensee BioMed Centr12 months following its publication. After thisAttribution License (http://creativecommons.oreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.(ENCODE) and Roadmap Epigenomics projects. Hefound substantial enrichment of psychophrenia QTLsin DNase hypersensitive sites, whereas there were es-sentially no QTLs in intron and intergenic sites.Together, these studies suggest that regulatory vari-variation, and we are now capable of finding manyconcrete examples in cis, where chromatin state influ-ences the cell or organism phenotype.Even with these massive studies, there was still nomention of trans-QTL discoveries, suggesting that evenuse of tens of thousands of individuals leaves studiesunder-powered to locate distal effectors. Luke Jostins(Wellcome Trust Centre for Human Genetics, Oxford,UK) supported this hypothesis directly by reporting aninability to detect trans-eQTLs despite using a modelspecifically designed to detect them.Transcriptional and translational regulationJacob Degner (European Molecular Biology Lab,Heidelberg, Germany) finely examined one method bywhich regulatory variation can have a functional im-pact on gene expression by using cap analysis of geneexpression (CAGE), a method that precisely identifiestranscriptional start sites (TSSs), on 80 cell lines atthree time-points. By integrating genotype informa-tion, Degher defined promoter-shape QTLs, identify-ing two main classes of variants: those that cause theprecise location of transcription initiation to vary at ahigher rate, and those that shift the location of tran-scriptional initiation to a new position. It is not yetclear how these QTLs influence expression, but itsuggests at least two mechanisms by which regulatoryvariation might regulate the 5′ mRNA, which couldhave downstream effects on mRNA secondary struc-ture, splicing and stability.Beyond transcription, there are many downstreamregulatory layers that are under genetic influence.Protein translation and protein expression are twosuch layers that received substantial attention. Alexisal. The licensee has exclusive rights to distribute this article, in any medium, fortime, the article is available under the terms of the Creative Commonsrg/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,Carr and Pe’er Genome Biology 2014, 15:416 Page 2 of 3http://genomebiology.com/2014/15/7/416Battle (Stanford University, USA) and Yoav Gilad(University of Chicago, USA) presented two sides ofa collaboration that examines this question by com-paring the incidence of eQTLs, ‘ribo-QTLs’ and ‘pro-tein-QTLs’, the latter of which were measured bycomparing genotype information with ribosome profil-ing and high-throughput ‘stable isotope labeling withamino acids in cell culture’ (SILAC) mass spectrom-etry. The results were heartening, showing that atleast half of the functional variation that evokes aneQTL also associates with a protein-QTL. This indi-cates that RNA-based analyses are capturing a sub-stantial portion of the cell phenotype. However,significantly, it shows the promise and importance ofprotein-based technologies that are capable of gener-ating more-representative images of cell phenotypes.Mutations and deletionsClustered regularly interspaced short palindromic re-peats (CRISPRs) were one of the big stories of the pastyear, bringing major improvements in the ability of sci-entists inexpensively to achieve complete gene knock-out disruption. Neville Sanjana (Broad Institute ofHarvard and MIT, USA) presented a genome-scaleCRISPR screen, over every gene in the genome, devel-oped in Feng Zhang’s group (Broad Institute, USA).They screened melanoma cell lines treated with vemura-fenib, a highly effective late-stage melanoma drug againstwhich most patients eventually develop resistance. Intheir screen, they identified many genes known to con-tribute to vemurafenib resistance, as well as some newcandidates, highlighting the potential of CRISPR screensin drug discovery.Several other groups asked questions about what canbe learned from examining natural mutations. MichaelStratton (The Wellcome Trust Sanger Institute, Hinxton,UK) catalogued all the different mutation types presentin the TP53 gene across numerous tissues and cell linesand compared the mutation types with the effects ofknown mutagens. He found that over 50% of the muta-tions could be attributed to known mutagens such asUV light or aflatoxin and was able to identify the per-centage of mutations that each mutagen was responsiblefor. However, much of the mutation load could not beexplained by his panel, suggesting that there are novelmechanisms of DNA mutation that have not yet beeninvestigated.Minyoung Wyman (Columbia University, USA) lever-aged mutation rates in an innovative way by comparingthe rate at which germ cells accrue two different sets ofmutations: mutations resulting from mitosis that requirecell division, and non-mitotic mutations. As germ cellsdo not divide between birth and puberty, the two muta-tion types have different rates, and the historicaldifference in these rates allowed Wyman to estimate theaverage generation time throughout human history: aslow increase from 25 to 29 years.Computation and technologyPerhaps the most consistent message throughout theconference was that, as data increase in magnitude, theirutility is limited by the sophistication of our computa-tional approaches. There were several excellent presenta-tions that highlighted this fact, perhaps none morestrongly than that of Joseph Pickrell (New York GenomeCenter, USA), who showed that, by using factor analysisto integrate multiple correlated variables, much moreinformative eQTLs can be discovered and used to distin-guish causative linkages between symptoms and comor-bid ones caused by the same functional variant. DanaPe’er (Columbia University, USA) showed how integrat-ing multiple data types improves statistical powerenough to detect novel driver mutations that are missedby any data type alone.Matthew Stephens (University of Chicago, USA)reexamined the foundations of a ubiquitous statistic:the false discovery rate (FDR). Stephens showed thatthe bimodal null model using common FDR algo-rithms has the effect of depressing the corrected P-values output by the procedure. Therefore, we arelikely over-estimating our experiments’ false discoveryrates. Finally, Oliver Stegle (European BioinformaticsInstitute, Hinxton, UK) examined the effect of the cellcycle on gene expression in single-cell sequencing.Unlike bulk expression analyses, which average over apopulation of cells, single-cell sequencing is very sen-sitive to the cell cycle, and Stegle demonstrated howan algorithm that corrects for cell-cycle stage revealshidden information on the stage of differentiation inembryonic stem cells. Together, these talks demon-strate the importance of modeling the sources of vari-ance in biological experiments and the power ofintegrative analyses to uncover biologically meaningfulphenomena obscured under any single assay.ConclusionsHolistically, Biology of Genomes 2014 provided a broadview into many cutting-edge projects. Promising resultswere reported from many corners of genomes: for ex-ample, large studies leveraged their improved power toidentify specific regulatory SNPs; functional variationthat affects expression corresponded to matched vari-ation in protein expression in more than half of thecases; some early results of genome-wide deletionscreens successfully identified genes involved in drug re-sistance; and new computational approaches were ableto integrate multiple assays profitably to uncover awhole greater than its parts.Carr and Pe’er Genome Biology 2014, 15:416 Page 3 of 3http://genomebiology.com/2014/15/7/416AbbreviationsCAGE: Cap analysis of gene expression; CRISPR: Clustered regularlyinterspaced short palindromic repeat; eQTL: Expression quantitative traitlocus; FDR: False discovery rate; IBD: Inflammatory bowel disease;SILAC: Stable isotope labeling with amino acids in cell culture;TSS: Transcriptional start site.Competing interestsThe authors declare that they have no competing interests.doi:10.1186/s13059-014-0416-0Cite this article as: Carr and Pe’er: Broadening horizons: holisticviewpoints from the Biology of Genomes. Genome Biology 2014 15:416.

Broadening horizons: holistic viewpoints from theBiology of Genomes

Broadening horizons: holistic viewpoints from the Biology of Genomes

Ambrose Carr

Dana Pe’er

Springer - Publisher Connector

Carr and Pe’er Genome Biology 2014, 15:416
http://genomebiology.com/2014/15/7/416MEETING REPORTBroadening horizons: holistic viewpoints from the
Biology of Genomes
Ambrose Carr and Dana Pe’er*Abstract
A report on the Cold Spring Harbor Laboratory 27th
annual meeting on the Biology of Genomes, held in
Cold Spring Harbor, New York, USA, 6-10 May 2014.ation is responsible for a large amount of functionalFine mapping in cis discovers substantial
functional regulatory variation
‘Biology of Genomes’ is a broad annual meeting that
showcases the year’s progress in the many subfields of
genomics. With so many talks, it is impossible to cover
all of them. However, despite the variety of subject mat-
ter, several trends recurred throughout the conference.
One result that consistently resurfaced across multiple
sessions is the ability of enlarged studies of tens of thou-
sands of individuals to move beyond linkage to genes
and finely map traits to individual regulatory polymor-
phisms. Jeffrey Barret (Wellcome Trust Sanger Institute,
Hinxton, UK) was able to identify specific genes as con-
tributors to disease by using data from 12,000 patients
in the UK10K project. Hailiang Hailiang (MIT, USA)
presented results from the inflammatory bowel disease
(IBD) consortium that allowed the identification of spe-
cific variants that contribute to IBD, and Stephen Parker
(NIH Bethesda, USA) presented the results of the on-
going FUSION study of type 2 diabetes, where a com-
bination of RNA-seq and genotyping was used to
identify muscle expression quantitative trait loci (eQTLs)
associated with different physiological stages of type 2
diabetes.
With the enormous amount of data being gener-
ated, it is logical to ask: how much functional vari-
ation is found in coding versus noncoding regions of
the genome? Alexander Gusev (Harvard University,
USA) answered this question by annotating the gen-
ome using data from Encyclopedia of DNA Elements* Correspondence: dpeer@biology.columbia.edu
Departments of Biological Sciences and Systems Biology, Columbia
University, 1130 St Nicholas Avenue, New York, NY 10032, USA
© 2014 Carr and Pe'er; licensee BioMed Centr
12 months following its publication. After this
Attribution License (http://creativecommons.o
reproduction in any medium, provided the or
Dedication waiver (http://creativecommons.or
unless otherwise stated.(ENCODE) and Roadmap Epigenomics projects. He
found substantial enrichment of psychophrenia QTLs
in DNase hypersensitive sites, whereas there were es-
sentially no QTLs in intron and intergenic sites.
Together, these studies suggest that regulatory vari-
variation, and we are now capable of finding many
concrete examples in cis, where chromatin state influ-
ences the cell or organism phenotype.
Even with these massive studies, there was still no
mention of trans-QTL discoveries, suggesting that even
use of tens of thousands of individuals leaves studies
under-powered to locate distal effectors. Luke Jostins
(Wellcome Trust Centre for Human Genetics, Oxford,
UK) supported this hypothesis directly by reporting an
inability to detect trans-eQTLs despite using a model
specifically designed to detect them.Transcriptional and translational regulation
Jacob Degner (European Molecular Biology Lab,
Heidelberg, Germany) finely examined one method by
which regulatory variation can have a functional im-
pact on gene expression by using cap analysis of gene
expression (CAGE), a method that precisely identifies
transcriptional start sites (TSSs), on 80 cell lines at
three time-points. By integrating genotype informa-
tion, Degher defined promoter-shape QTLs, identify-
ing two main classes of variants: those that cause the
precise location of transcription initiation to vary at a
higher rate, and those that shift the location of tran-
scriptional initiation to a new position. It is not yet
clear how these QTLs influence expression, but it
suggests at least two mechanisms by which regulatory
variation might regulate the 5′ mRNA, which could
have downstream effects on mRNA secondary struc-
ture, splicing and stability.
Beyond transcription, there are many downstream
regulatory layers that are under genetic influence.
Protein translation and protein expression are two
such layers that received substantial attention. Alexisal. The licensee has exclusive rights to distribute this article, in any medium, for
time, the article is available under the terms of the Creative Commons
rg/licenses/by/4.0), which permits unrestricted use, distribution, and
iginal work is properly credited. The Creative Commons Public Domain
g/publicdomain/zero/1.0/) applies to the data made available in this article,
Carr and Pe’er Genome Biology 2014, 15:416 Page 2 of 3
http://genomebiology.com/2014/15/7/416Battle (Stanford University, USA) and Yoav Gilad
(University of Chicago, USA) presented two sides of
a collaboration that examines this question by com-
paring the incidence of eQTLs, ‘ribo-QTLs’ and ‘pro-
tein-QTLs’, the latter of which were measured by
comparing genotype information with ribosome profil-
ing and high-throughput ‘stable isotope labeling with
amino acids in cell culture’ (SILAC) mass spectrom-
etry. The results were heartening, showing that at
least half of the functional variation that evokes an
eQTL also associates with a protein-QTL. This indi-
cates that RNA-based analyses are capturing a sub-
stantial portion of the cell phenotype. However,
significantly, it shows the promise and importance of
protein-based technologies that are capable of gener-
ating more-representative images of cell phenotypes.
Mutations and deletions
Clustered regularly interspaced short palindromic re-
peats (CRISPRs) were one of the big stories of the past
year, bringing major improvements in the ability of sci-
entists inexpensively to achieve complete gene knock-
out disruption. Neville Sanjana (Broad Institute of
Harvard and MIT, USA) presented a genome-scale
CRISPR screen, over every gene in the genome, devel-
oped in Feng Zhang’s group (Broad Institute, USA).
They screened melanoma cell lines treated with vemura-
fenib, a highly effective late-stage melanoma drug against
which most patients eventually develop resistance. In
their screen, they identified many genes known to con-
tribute to vemurafenib resistance, as well as some new
candidates, highlighting the potential of CRISPR screens
in drug discovery.
Several other groups asked questions about what can
be learned from examining natural mutations. Michael
Stratton (The Wellcome Trust Sanger Institute, Hinxton,
UK) catalogued all the different mutation types present
in the TP53 gene across numerous tissues and cell lines
and compared the mutation types with the effects of
known mutagens. He found that over 50% of the muta-
tions could be attributed to known mutagens such as
UV light or aflatoxin and was able to identify the per-
centage of mutations that each mutagen was responsible
for. However, much of the mutation load could not be
explained by his panel, suggesting that there are novel
mechanisms of DNA mutation that have not yet been
investigated.
Minyoung Wyman (Columbia University, USA) lever-
aged mutation rates in an innovative way by comparing
the rate at which germ cells accrue two different sets of
mutations: mutations resulting from mitosis that require
cell division, and non-mitotic mutations. As germ cells
do not divide between birth and puberty, the two muta-
tion types have different rates, and the historicaldifference in these rates allowed Wyman to estimate the
average generation time throughout human history: a
slow increase from 25 to 29 years.
Computation and technology
Perhaps the most consistent message throughout the
conference was that, as data increase in magnitude, their
utility is limited by the sophistication of our computa-
tional approaches. There were several excellent presenta-
tions that highlighted this fact, perhaps none more
strongly than that of Joseph Pickrell (New York Genome
Center, USA), who showed that, by using factor analysis
to integrate multiple correlated variables, much more
informative eQTLs can be discovered and used to distin-
guish causative linkages between symptoms and comor-
bid ones caused by the same functional variant. Dana
Pe’er (Columbia University, USA) showed how integrat-
ing multiple data types improves statistical power
enough to detect novel driver mutations that are missed
by any data type alone.
Matthew Stephens (University of Chicago, USA)
reexamined the foundations of a ubiquitous statistic:
the false discovery rate (FDR). Stephens showed that
the bimodal null model using common FDR algo-
rithms has the effect of depressing the corrected P-
values output by the procedure. Therefore, we are
likely over-estimating our experiments’ false discovery
rates. Finally, Oliver Stegle (European Bioinformatics
Institute, Hinxton, UK) examined the effect of the cell
cycle on gene expression in single-cell sequencing.
Unlike bulk expression analyses, which average over a
population of cells, single-cell sequencing is very sen-
sitive to the cell cycle, and Stegle demonstrated how
an algorithm that corrects for cell-cycle stage reveals
hidden information on the stage of differentiation in
embryonic stem cells. Together, these talks demon-
strate the importance of modeling the sources of vari-
ance in biological experiments and the power of
integrative analyses to uncover biologically meaningful
phenomena obscured under any single assay.
Conclusions
Holistically, Biology of Genomes 2014 provided a broad
view into many cutting-edge projects. Promising results
were reported from many corners of genomes: for ex-
ample, large studies leveraged their improved power to
identify specific regulatory SNPs; functional variation
that affects expression corresponded to matched vari-
ation in protein expression in more than half of the
cases; some early results of genome-wide deletion
screens successfully identified genes involved in drug re-
sistance; and new computational approaches were able
to integrate multiple assays profitably to uncover a
whole greater than its parts.
Carr and Pe’er Genome Biology 2014, 15:416 Page 3 of 3
http://genomebiology.com/2014/15/7/416Abbreviations
CAGE: Cap analysis of gene expression; CRISPR: Clustered regularly
interspaced short palindromic repeat; eQTL: Expression quantitative trait
locus; FDR: False discovery rate; IBD: Inflammatory bowel disease;
SILAC: Stable isotope labeling with amino acids in cell culture;
TSS: Transcriptional start site.
Competing interests
The authors declare that they have no competing interests.
doi:10.1186/s13059-014-0416-0
Cite this article as: Carr and Pe’er: Broadening horizons: holistic
viewpoints from the Biology of Genomes. Genome Biology 2014 15:416.


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