A Snapshot of #bigdatamed tweets and Facebook Posts

CLICK HERE FOR FEED

As you can see from the curated Twitter and Facebook feed included at the link above, Stanford’s Center for Computational, Evolutionary and Human Genomics (CEHG) had a strong presence at this year’s Big Data Conference. In addition to the numerous CEHG community members in the audience (and livestreaming the event), we also had CEHG faculty members Russ Altman, Euan Ashley, Carlos Bustamante, Mildred Cho, Hank Greely, Susan Holmes, and Julia Salzman on the stage, serving as session moderators and featured speakers

We hope you enjoy this Storification of Facebook posts and tweets posted before, during, and after the event. Please note: this stream is not comprehensive, but is rather a snapshot of the conversations surrounding each event, identified by the hashtag #bigdatamed. Want to learn more about CEHG? Follow us on Facebook and @StanfordCEHG and read our blog at stanfordcehg.wordpress.com. Want more info about Big Data in Biomedicine? For the detailed agenda, videos of 2015 conference videos, and more, click here.

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BAPGXII Saturday May 30, 2015

logo with APG-p19lu5oeag1iikbhs1s351gbf18j8Stanford is hosting the 12th Bay Area Population Genomics (BAPG) meeting. The Bay Area Population Genomics meeting is a great place to (re)connect with your pop gen/genomics colleagues in the area and to present your work in a talk or a poster.

BAPGXI, held in December at UC Davis, was a great event with over 100 participants and a line up of excellent talks. Thanks to the Coop lab! You can read more here, including the storified tweets. We are excited to continue this success at Stanford!

Logistics

UPDATE: Click here for detailed event program.

The meeting will take place on May 30th on the Stanford campus in the Alway building, room M106. We start at 8:30AM with breakfast and registration, Dr. Dmitri Petrov’s opening remarks will begin at 9:25am, and the first talk will be at 9:30am. The last talk (Dr. Jonathan Pritchard’s keynote) ends at 2:10pm, followed by a poster session with amazing wine, beer, and cheese! Here is a general outline of the agenda, to help you plan your day:

Breakfast and Registration in Alway Courtyard 8:30-9:25am (pick up your BAPGXII gift!)
Opening Remarks 9:25-9:30am
Talk Session 1 9:30-10:30am (20 mins per talk)
Coffee Break in Courtyard 10:30-11am
Talk Session 2 11am-12pm (20 mins per talk)
Lunch in Courtyard 12-1pm
Talk Session 3 and Keynote 1-2:10pm (2 20 min talks and 1 30 min talk)
Poster Session with Wine, Beer, and Cheese Reception at 2:10pm, ends at 3pm

Talks and Posters

Sorry. Speaker and poster slots are now full. No longer accepting sign-ups.

How to Attend BAPGXII

1. Please register here by 10am Friday, May 29th to join us at BAPGXII. Registration is free and open to all, but required.

2. Encourage your colleagues to sign up! Forward this email to your lab mailing list and watch for updates on the CEHG Facebook page and on Twitter @StanfordCEHG. Help us get the momentum going by tweeting us using #BAPGXII.

3. And finally, once you’ve signed up, all you need to do is get up early and ride-share, VTA/Caltrain or bike to our beautiful campus on May 30th. Come for the science, stay for the social! Use the Stanford campus map and this Google Map to find the Alway Building, located at 500 Pasteur Drive, Stanford, CA. Be green and consider ride-sharing: there is a dedicated tab for making travel plans in the sign up doc!

We hope to see you at Stanford!

The BAPGXII organizing committee: Bridget Algee-Hewitt (@BridgetAH), David Enard (@DavidEnard), Katie Kanagawa (@KatieKanagawa), Alison Nguyen, Dmitri Petrov (@PetrovADmitri), Susanne Tilk, and Elena Yujuico. If you have any questions, feel free to contact Bridget Algee-Hewitt at bridgeta@stanford.edu.

To follow BAPGXII on twitter, check out the hashtag: #BAPGXII and also follow @StanfordCEHG .

Imagining Phylogenetics and Recombination as Art

DAFAbout the Artist:
Daniel Friedman is a 1st year Ph.D. student in the Ecology and Evolution program. Working from the Gordon lab, he mainly studies the evolution of collective behavior in ants. Other research interests include fractals, burritos, and metaphors. Contact: dfri@stanford.edu.

 

1. Phylogenetics
Ever since “I Think…”, the idea of a bifurcating tree of species relations has guided evolutionary biology. This piece of paper with ink on it plays with the idea of an “evolutionary I”, styled as an evolving Eye. Whether we perform molecular studies on the ontogenetic role of Pax6, or psychophysical explorations into the Self, we are confronted with questions of homology and convergence. Our time-reversible phylogenetic algorithms, so designed for computational simplicity, only contribute to this problem of post hoc ergo propter hoc – “after this, therefore because of this.” The Modern Synthesis was clearly a rEvolutionary moment – now are we ready for a Post-Modern Synthesis?
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2. Recombination
DNA recombination is key to many biological processes. Recombination between homologous chromosomes during meiosis creates novel combinations of alleles, and to many, is the teleological “Why?” of Sex. But the reach of recombination goes far, far beyond Sex. Recombination between alleles of the same locus allows a kaleidoscope of DNA error-correcting mechanisms to proceed. And over evolutionary time scales, “errors” in recombination provide large structural creativities in the genome, such as duplication, deletions, and inversions. Recombination during immune cell maturation allows the human body to recognize an essentially infinite cohort of potential invaders. And now that recombination has been mechanistically deconvoluted, derived technologies facilitate guided DNA editing in vitro and in vivo . Recombination is molecular innovation embodied, a topological whirligig, and the workhorse of the genome.
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Fast Algorithm Infers Relationships in High-Dimensional Datasets

Post author Henry Li is a graduate student in the Wong Lab.

Post author Henry Li is a graduate student in the Wong Lab.

New research harnesses the powers of single value decomposition (SVD) and sparse learning to tackle the problem of inferring relationships between predictors and responses in large-scale, high-dimensional datasets.

Addressing problems in computation speed, assumptions of scarcity, and algorithm sensitivity

One major challenge that statisticians face when inferring relationships is that modern data is big and the underlying true relationships between predictors and responses are sparse and multilayered. To quickly establish connections in these datasets, Ma et al. utilize a combination of SVD and sparse learning, called thresholding SVD (T-SVD). This new algorithm solves many issues that plagued the Statistics and Big Data communities, such as the problems of computation speed, the assumption of sparcity, and the sensitivity of the algorithm to positive results. In their simulation study, T-SVD is shown to be better in relation to speed and sensitivity than existing methods such as the sequential extraction algorithm (SEA) and the iterative exclusive extraction algorithm (IEEA). As a result, the multilayered relationships between predictors and responses, which come in the form of multidimensional matrices, can be learned quickly and accurately.

Uncovering new regulatory networks

Demonstrating the application of T-SVD, Ma et al. showed that new biological insights can be gained from using T-SVD to analyze datasets from The Cancer Genome Atlas consortium. The authors focused on the ovarian cancer gene expression datasets, in which the sample size is much smaller than the number of regulators and responses measured in the study. As in a typical genomic experiment, tens of thousands of genes were probed for their expression levels; from pathway studies, we know that very few of these genes form control switches that govern the expression levels for the rest of the genome. Ma et al. inferred two different relationships, based on microRNA (miRNA) or long noncoding RNA (lncRNA). The authors showed that these regulatory relationships specifically match established cancer pathways very well. Geneticists now have two new regulatory networks to mine for understanding the roles of miRNAs and lncRNAs.

In short, T-SVD is an exciting algorithm that pushes the Statistics field forward by offering a new lens to look at large-scale multidimensional datasets. With this approach, statisticians and users of statistics, like geneticists, can gain new insights into existing datasets and tackle new research problems.

References

Ma, Xin, Luo Xiao, Wing Hung Wong. Learning regulatory programs by threshold SVD regression. Proc Natl Acad SCI USA. 2014 Nov 4; 111 (44). DOI 10.1073/pnas.1417808111

Paper author, Xin (Maria) Ma is a research associate in the Wong Lab.

Paper author, Xin (Maria) Ma is a research associate in the Wong Lab.

Afterword: CEHG Genetics and Society Symposium 2015

CEHG_Logo_Mono_Black

Founded in 2012, CEHG is a research program that fosters interdisciplinary research. Home to more than 25 faculty and more than 200 grads and postdocs, CEHG bridges the divides between various member labs across Stanford campus.

The 2015 CEHG Genetics and Society Symposium (GSS15), which took place on April 13th and 14th in Stanford’s Paul Brest Hall, was a smashing success. It featured 25 speakers from Stanford campus and the San Francisco Bay academic and scientific industry communities. Approximately 175 Stanford affiliates and non-affiliates came together to celebrate the Center’s spirit of interdisciplinary collaboration and meet with experts in the fields of computational, evolutionary and human genomics This is a significant increase from last year’s 150 attendees!

The Mission:

The Genetics and Society Symposium is integral to CEHG’s mission: it provides postdocs and graduate fellows with the opportunity to share their developing research with faculty advisors and their colleagues, encourages conversation between faculty working in diverse scientific disciplines across campus, and introduces CEHG members to speakers from around the Bay Area and beyond (and vice versa).

The Venue:

As you can see from our photos of the space and catering service, Paul Brest Hall was the perfect home for this year’s two-day symposium. The hall was spacious, the food delicious, the staff hands on, and the outdoor picnic area well suited for our lunch and coffee breaks. We enjoyed the venue so much, in fact, that CEHG staff are currently in the process of booking the space for next year!

The Speakers:

GSS15 featured four brilliant keynote speakers, each distinguished in his/her field of research.

Gene Myers and CEHG Exec Committee members Marc Feldman, Chiara Sabatti, and Carlos Bustamante

Gene Myers and CEHG Exec Committee members Marc Feldman, Chiara Sabatti, and Carlos Bustamante

Founding director of a new Systems Biology Center at the Max-Planck Institute of Molecular Cell Biology and Genetics, Dr. Eugene (Gene) Myers presented his open-sourced research on the resurrection of de novo DNA sequencing. Best known for the development of BLAST, the most widely used tool in bioinformatics and the assembler he developed at Celera that delivered the fly, human, and mouse genomes in a three-year period, Dr. Myers participated in GSS15, courtesy of DNAnexus. Follow his blog: https://github.com/thegenemyers.

Co-founding director Carlos Bustamante and Ed Green catch up during a break at GSS15.

Co-founding director Carlos Bustamante and Ed Green catch up during a break at GSS15.

Assistant Professor in Biomolecular Engineering at the University of California, Santa Cruz, Richard (Ed) Green presented his research on a novel approach for highly contiguous genome assemblies, which draws on his work as an NSF Fellow at the Max Planck Institute in Leipzig, Germany and head of an analysis consortium responsible for publishing the draft genome sequence of Neanderthal. Click here for his 2014 CARTA talk, “The Genetics of Humanness: The Neanderthal and Denisovan Genomes.

Dr. Michelle Mello, Stanford Law School and School of Medicine

Dr. Michelle Mello, Stanford Law School and School of Medicine

Michelle Mello, Professor of Law at Stanford Law School and Professor of Health Research and Policy in Stanford’s School of Medicine, presented findings from her extensive research on the ethics of data sharing. As the author of more than 140 articles and book chapters on the medical malpractice system, medical errors and patient safety, public health law, research ethics, the obesity epidemic, and pharmaceuticals, Dr. Mello provided a valuable perspective from the intersections of law, ethics, and health policy. Click here to read Dr. Mello’s SLS profile.

Dr. Ami Bhatt, Stanford Medicine

Dr. Ami Bhatt, Stanford Medicine

Ami Bhatt shared her passion for improving outcomes for patients with hematological malignancies in her talk, “Bugs, drugs, and cancer.” Best known for her recent work demonstrating the discovery of a novel bacterium using sequence-based analysis of a diseased human tissue, her research has been presented nationally and internationally and published in 2013 in the New England Journal of Medicine. Click here for links to Dr. Bhatt’s CAP profile and lab homepage.

 

We had a large group of CEHG faculty members at this year’s event, showcasing the cutting edge research being done in CEHG labs across Stanford campus and indicating considerable faculty commitment to ensuring the Center’s continuing success.

Our symposium would not be complete without our invited CEHG Fellows. These speakers were nominated by organizing committee members to present on topics relating to their CEHG-funded research projects. These young scholars embody CEHG’s continuing commitment to provide funding support to researchers as they transition from graduate studies to postdoctoral scholarships.

The Workshop:

There was standing room only when facilitators Chiara Sabatti (Associate Professor of Health Research and Policy at Stanford), Ken Lange (Chair of the Human Genetics Department at UCLA), and Suyash Shringarpure (postdoctoral scholar in Stanford’s Bustamante Lab) presented their approaches to contemporary problems in statistical genetics!

Social Media:

Did you know? CEHG is on social media!

GSS15 social media moderators, Bridget Algee-Hewitt, Jeremy Hsu, Katie Kanagawa, and Rajiv McCoy were posting live throughout both days of the event. And our efforts to reach the larger community paid off, with a total reach of 815 on Facebook and more than 7,000 impressions on Twitter!

To catch up on our GSS15 coverage, check out our Facebook page at https://www.facebook.com/StanfordCEHG?ref=hl and our Twitter feed @StanfordCEHG. Follow both to make sure you are the first to know when we post CEHG-related news and announcements.

Want to know when speaker videos from the symposium will be available on CEHG’s forthcoming youtube channel? Follow us on Facebook and Twitter!

Special Thanks:

From left to right: Bridget Algee-Hewitt, Cody Sam, Yang Li, Anand Bhaskar, and Katie Kanagawa

From left to right: Bridget Algee-Hewitt, Cody Sam, Yang Li, Anand Bhaskar, and Katie Kanagawa

The GSS15 organizing committee—including Bridget Algee-Hewitt, Anand Bhaskar, Katie Kanagawa, Yang Li, and Cody Sam—would like to take this opportunity to thank CEHG Directors Carlos Bustamante and Marc Feldman, Executive Committee members Hank Greely, Dmitri Petrov, Noah Rosenberg, and Chiara Sabatti, event volunteers Alex Adams, Maude David, and Chris Gignoux, event photographer Deneb Semprum, and everyone who attended this year’s symposium.

We hope you enjoyed attending as much as we enjoyed working behind-the-scenes. We hope to see you all again at GSS16! If you are interested in volunteering for future CEHG events, please contact us at stanfordcehg@stanford.edu.

Upcoming CEHG events:

Don’t miss our popular weekly Evolgenome seminar series, which will continue through Spring term, usually on Wednesdays at noon (location varies). Lunch is always provided. Details will follow, but here is a quick overview so you can mark your calendars!

April 29: Fernando Racimo (Nielsen/Slatkin Lab)
May 6: Pleuni Pennings (UCSF)
May 20: Kelly Harkin
June 3: Sandeep Ventakaram (Petrov Lab)
June 10: Emilia Huerta-Sanchez

Learning from 69 sequenced Y chromosomes

Why the Y?

Blog author Amy Goldberg is a graduate students in Noah Rosenberg's lab.

Blog author Amy Goldberg is a graduate students in Noah Rosenberg’s lab.


While mitochondria have been extensively sequenced for decades because of their short length and abundance, the Y chromosome has been under-studied.  Unlike autosomal DNA, the mitochondria and (most of) the Y chromosome are inherited exclusively maternally and paternally, respectively.  Therefore, they do not undergo meiotic recombination.  Without recombination, mutations accumulate on a stable background, preserving a wealth of information about population history.  Each background, shared through a common ancestor, is called a haplogroup. To leverage this information, Poznik et al. set out to sequence 69 males from nine diverse human populations, including a large representation of African individuals.  The paper, published in Science last summer, is by Stanford graduate student David Poznik and a group lead by CEHG professor Dr. Carlos Bustamante.

The structure of the Y chromosome is complex, with large heterochromatic regions, pseudo-autosomal regions that recombine with the X chromosome, and repetitive elements, making mapping reads difficult.  But, the Y chromosome is haploid, allowing for accurate variant calls at lower coverage than the autosomes, which have heterozygotes.  Using high-throughput sequencing (3.1x mean coverage) and a haploid expectation-maximization algorithm, Poznik et al. called genotypes with an error rate around 0.1%. The paper developed important methods for analyzing high-throughput sequences of the difficult Y chromosome, including determining the subset of regions within which accurate genotypes can be called.

Reconstructing the human Y-chromosome tree

Poznik et al. constructed a phylogenetic tree of the Y chromosome using sequence data and a maximum likelihood approach.  While the overall structure of the tree was known, Poznik et al. were able to accurately calculate branch lengths based on the number of variants differing between individuals and resolve previously indeterminate finer structure.

Figure 2 of the paper: Y-chromosome phylogeny inferred from genomic sequencing.

Figure 2 of the paper: Y-chromosome phylogeny inferred from genomic sequencing.

Incredible African Diversity: One of the key findings of the paper was the depth of diversity within Africans lineages.  While both uniparental and autosomal markers have indicated an African root for human diversity, Poznik et al. find lineages within a single population, the San hunter-gatherers, that coalesce almost at the same time as the entire tree (see haplogroup A). This indicates African diversity and structure has existed for tens of thousands of years, and there is likely more to discover.  A large sample of African populations were considered, which lead to previously unseen structure within haplogroup B2, including structure not mirrored by modern population clustering, that dates to approximately 35,000 years ago.

Evidence of population expansionShort internal branches of the tree, such as those seen within haplogroup E and the non-African group FT, indicate periods of rapid population growth.  When a population expands quickly, new variants that might otherwise drift to extinction can persist.  A large number of coalescence events occur at the time of growth, as there were fewer lineages alive in the population before this time.  For non-African haplogroups, this pattern is likely a remnant of the Out of Africa migration.  For haplogroup E, this corresponds to the Bantu agricultural expansion.

Resolved Eurasian polytomy: Previously, the topology of the Eurasian tree separating haplogroups G-H-IJK was unresolved.  Because of the higher coverage sequencing for this study, Poznik et al. found a single variant, a C to T transition, that differentiates G from the other groups.  Haplogroup G retains the ancestral variant, while H-IJK share the derived variant and are therefore more closely related to each other.

Sequencing vs. genotyping

In contrast to previous studies, which analyzed small repetitive elements called microsatellites or small sets of single base-pair changes called SNPs, whole-genome sequencing data contains not only more information, but potentially more accurate information.  In particular, before the advent of high-throughput sequencing, SNPs were usually ascertained in a subset of individuals that did not capture worldwide diversity levels.  Therefore, diversity measures are often underestimated and biased.  Without sequence data, the branch lengths of the tree did not have a meaningful interpretation, and the depth of variation within Africa was not seen.

MRCA of Human Maternal and Paternal Lineages

There was a lot of public discussion spurred by the publication of Poznik’s paper last year.  The discussion mainly focused on their result that, contrary to previous estimates, the most recent common ancestor (MRCA) of all mitochondrial DNA lived at a similar time as that of all Y chromosomes.  Previous estimates put the mitochondrial TMRCA around 200 thousand years ago, with the Y chromosome coalescing a bit over 100 thousand years ago.  These different estimates for Y and mitochondria were often obtained through different sequencing and analysis methods, and are therefore less comparable.  In particular, varying estimates of the mutation rates have led to different TMRCA estimates.  By analyzing both the Y and mitochondria in the same framework, calibrated by archeological evidence and within-species comparisons, Poznik et al. found largely overlapping confidence intervals for the TMRCA of both Y and mitochondria.

But, should the coalescence times of the mitochondria and the Y chromosome be the same? Not necessarily.  While discrepancies between the mitochondria and Y chromosome have often been interpreted as sex-biased population histories or sizes, strictly neutral models can predict large differences between the two, as well.  Because neither the analyzed part of the Y chromosome nor the mitochondria undergo recombination, each acts as a single locus – and therefore represents the history of a single lineage.  For a population, there is a wide distribution of the ages when lineages would coalesce for a given population history, and these loci represent only two with largely independent histories (given the overall population history), therefore they may differ by chance alone.  Similarly, different loci across autosomal DNA have TMRCA ranging from thousands to millions of years. Additionally, as single loci, any effects of selection would distort the entire genealogy of the Y chromosome and mitochondria.

Future directions

Human population history is far from fully fleshed out, and Poznik et al. provide a framework to leverage increasingly available high-throughput sequencing of Y chromosomes.  The method used to calculate the mutation rate and TMRCA is a valuable contribution in itself, with applications to a wide range of evolutionary and ecological questions.  This study demonstrated that we have only characterized a fraction of worldwide diversity, particularly in Africa, and that increased sampling will be critical to parsing close and far ties in human history.

Reference

Poznik GD, Henn BM, Yee MC, Sliwerska E, Euskirchen GM, Lin AA, Snyder M, Quintana-Murci L, Kidd JM, Underhill PA, Bustamante CD. Sequencing Y chromosomes resolves discrepancy in time to common ancestor of males versus females. Science. 2013 Aug 2;341(6145):562-5. doi: 10.1126/science.1237619.

Paper author David Poznik is a PhD student in Carlos Bustamante's lab.

Paper author David Poznik is a PhD student in Carlos Bustamante’s lab.

Which genetic variants determine histone marks?

JoeDavis

Blog author Joe Davis is a graduate student with Stephen Montgomery & Carlos Bustamante.

The wealth of genetic variation in the human genome is found not within protein-coding genes but within non-protein coding regions. This comes as no surprise given that only 1% percent of the genome codes for proteins. Until recently, efforts to determine the effects of genetic variation on trait variation and disease have focused on coding regions. Results of genome-wide association studies (GWAS), however, have shown that trait and disease associated variants are often regulatory variants such as expression quantitative trait loci (eQTLs) found in non-coding regions. These results have spurred an effort to understand the functional role of non-coding, regulatory variation. Efforts have thus far relied on characterizing the association between variants and gene expression. This association alone, however, will not reveal the complete functional mechanism by which non-coding variants influence gene expression. Recent efforts have therefore begun to characterize numerous molecular phenotypes such as transcription factor (TF) binding, histone modification, and chromatin state to determine the mechanisms by which regulatory variants affect gene expression.

One issue, four papers

In the November 8 issue of Science, three papers were published that address the role of non-coding genetic variation on TF binding, histone modifications, and chromatin state (i.e. active versus inactive enhancer status). The first study was completed by the Dermitzakis Lab at the University of Geneva. They analyzed three TFs, RNA polymerase II (Pol II), and five histone modifications using chromatin immunoprecipitation and sequencing (ChIP-Seq) in lymphoblastoid cell lines (LCLs) from two parent-child trios [1]. The second was completed by the Pritchard Lab, which has recently moved to Stanford, and the Gilad Lab at the University of Chicago. They identified genetic variants affecting variation in four histone modifications and Pol II occupancy in ten unrelated Yoruba LCLs [2]. The third study was performed by the Snyder Lab at Stanford. They characterized the genetic variation underlying changes in chromatin state using RNA-Seq and ChIP-Seq for four histone modifications and two DNA binding factors in 19 LCLs from diverse populations [3]. This work was the subject of a recent CEHG Evolgenome talk given by Maya Kasowski, the study’s first author. Finally, the fourth study, published in the November 28 issue of Nature, was performed by the Glass Lab at UCSD. They characterized the effect of natural genetic variation between two mouse strains on the binding of two TFs involved in cell differentiation (PU.1 and C/EBPα) using ChIP-Seq [4]. In this post, I will analyze primarily the work presented by the Pritchard Lab, but I strongly recommend reading all four papers to understand the challenges in characterizing non-coding variation and the methods available to do so.

Motivation

The four studies seek to answer the general question of how regulatory variation affects gene expression. They characterize diverse molecular phenotypes such as histone modifications and TF binding to understand the mechanisms of action for non-coding variants. The Pritchard Lab focused their study on four histone modifications (three active and one repressive: H3K4me3, H3K4me1, H3K27ac, and H3K27me3, respectively) and Pol II occupancy.

Table2

Histone modifications 101

Histone modifications refer to the addition of chemical groups such as methyl or acetyl to specific amino acids on the tails of histone proteins comprising the nucleosome. These chemical groups are referred to as histone marks. They can serve a wide range of functions, but in general they are associated with the accessibility of a chromatin region. For example, the tri-methylation of lysine 4 of histone 3 (H3K4me3) is associated with increased chromatin accessibility and gene activation. On the other hand, increased levels of the repressive mark H3K27me3 (tri-methylation of lysine 27 of histone 3) at promoters is associated with gene inactivation.

Histone mark levels are measured in a high-throughput manner using ChIP-Seq. Briefly, an antibody targeting the mark of interest is used to pull down modified genomic regions. These immunoprecipitated regions are then sequenced to determine which genomic segments are modified and at what level. The procedure usually requires a large number of cells (on the order of 10^7). Therefore, the modification level is, in some ways, a population level measurement. Analysis of ChIP-Seq data typically involves testing for genomic regions with more reads than expected by chance. These regions, ranging from 200bp to 1000bp or more, are referred to as peaks that represent a modification level above the genomic background. Repressive marks like H3K27me3 tend to have broad peak regions, while activating marks like H3K4me3 can have much tighter peaks.

Since modification levels represent measurements on a population of cells and histone residues can have multiple modifications, genomic regions can show evidence for multiple marks. The combinations of these marks over a region can mark the function of the region. For example, regions with high levels of H3K27ac and a high ratio of H3K4me1 to H3K4me3 can mark active enhancer regions. Until now, the variation of these marks between individuals and the genetic cause of this variation was uncharacterized. Moreover, the causal impact of these marks remains unknown. Do they alter gene expression directly or are they altered by gene regulation? Therefore, the two guiding questions for this study are:

1. What genetic variants influence histone modifications?

2. Are these modifications “a cause or a consequence of gene regulation?”

Variation in histone modifications, a real whodunit

The authors first seek to identify and characterize genetic variants that influence histone marks. They generated ChIP-Seq data for the four histone marks and Pol II in LCLs derived from ten unrelated Yoruba individuals who were previously genotyped as part of the 1000 Genomes Project. Similar studies of regulatory variants such as eQTL studies require large sample sizes to detect the effects of regulatory variants that often lie outside the gene. Unlike eQTL studies, histone marks cover fairly broad regions often encompassing causal regulatory variants. As a result, the authors can use a smaller sample size and still be confident about interrogating the effects of causal regulatory SNPs. The authors developed a statistical test that models total read depth between individuals and allelic imbalance between haplotypes within individuals to increase power to detect cis-QTLs (i.e. variants that affect histone marks and Pol II occupancy nearby in the genome). Using this method, they identified over 1200 distinct QTLs for histone marks and Pol II occupancy (FDR 20%).

The authors then analyze these histone mark and Pol II QTLs to determine the overlap of these variants with other known regulatory variants. The hypothesis is that regulatory variants that affect gene expression will have effects on diverse molecular phenotypes. Therefore, variants that influence histone marks and Pol II should show significant overlap with known regulatory variants such as eQTLs and DNase I sensitivity QTLs (dsQTLs). DNase I sensitivity is a measure of chromatin accessibility with higher sensitivity associated with higher accessibility. The Pritchard Lab mapped eQTLs and dsQTLs in a larger sample of ~75 Yoruban LCLs in two previous studies that I also recommend reading [5,6]. Their analysis revealed an enrichment of low p-values for dsQTLs and, to a lesser extent, eQTLs when tested as histone mark and Pol II QTLs. In addition, the authors observed a coordinated change in multiple molecular phenotypes at dsQTLs and eQTLs. For example, higher levels of the three histone active marks were observed at dsQTLs for the more DNase I sensitive genotype. At eQTLs, H3K4me3, H3K27ac, and Pol II levels were higher for individuals with the high expression genotype. These results show that non-coding regulatory variants impact multiple molecular phenotypes ranging from chromatin accessibility and transcription to histone modifications. The authors provide strong evidence in response to their first guiding question, namely that non-coding regulatory polymorphisms associate with variation in histone marks and Pol II.

TFs and a question of directionality

The authors then turned to addressing the questions of causality for these marks. To do so, they analyze genetic variants in TF binding sites. The main hypothesis is that regulatory variants that alter a TFBS will modify TF binding which will cause changes in histone mark and Pol II levels nearby. If this is the case, then changes in histone marks are a consequence of how strong the TF binding site is. On the other hand, if these marks were causal, polymorphisms in TF binding sites would not be expected to show strong association with changes in these marks.

To test their hypothesis, the authors examine ~11.5K TF binding sites with polymorphisms heterozygous in at least 1 of their 10 individuals. They calculate the change in position weight matrix (PWM) score between the two alleles for polymorphic TF binding sites within each individual. They then test for significant association between this change in PWM and allelic imbalance of ChIP-Seq reads at nearby heterozygous sites. The idea is that if a variant improves (or disrupts) TF binding for one allele at a TF binding site then active histone marks nearby on the same allele will increase (or decrease). Repressive histone marks (in this case H3K27me3) are expected to have the opposite response. Indeed, when they apply their test, they find a significant positive association for the active marks and a negative association for the repressive mark. This result supports the hypothesis of changes histone marks as a consequence of TF binding and gene regulation. However, this result does not rule out other possibilities. Histone marks can still play a causal role in the establishment of TF binding. In other words, the relationship between TF binding and histone marks does not have to be unidirectional. In addition, there is evidence that long non-coding RNAs may play a role in the establishment and regulation of histone marks.

dsQTLs and eQTLs, a match made on chromatin

In their final analysis, the authors examine dsQTLs that are also eQTLs. Since these variants associate with both gene expression and chromatin accessibility at distal regulatory regions (>5kb from associated TSS), the authors can assign the regulatory region to a specific gene. A variant that is both a dsQTL and an eQTL likely disrupts a distal regulatory region. In addition to disrupting the accessibility of the regulatory region, the variant also perturbs the expression of a gene influenced by the regulatory region. For example, a variant may decrease the chromatin accessibility of an enhancer region and thereby decrease the level of active histone marks for the enhancer. This decreased enhancer activity can result in decreased transcription from a nearby gene and similarly decreased active mark levels for the gene. Therefore, the hypothesis guiding this analysis is that variants influencing the histone marks of a distal regulatory region will have a coordinated effect on histone marks at genes under the control of the regulatory region. The authors examine the allelic imbalance in ChIP-Seq reads at regulatory regions and their associated transcription start sites (TSS). Indeed, the authors observe that variants that increase DNase I sensitivity have a significant positive allelic imbalance for active marks at both the regulatory region and the TSS. The opposite is true for the repressive mark. This result again emphasizes the complexity of gene regulation and the impact of non-coding variation. Not only do regulatory variants influence diverse molecular phenotypes nearby, they can direct changes at distal loci. As the authors note, this coordinated change in histone marks between distal regions possibly reflects the 3D organization of chromatin. Regulatory variants that impact chromatin looping interactions between distal regulatory regions and genes may cause changes in activity levels for both the gene and the regulatory region.

Conclusions

This paper provides clear evidence that regulatory variation has very complex impacts affecting multiple and diverse molecular phenotypes at multiple regions simultaneously. This complexity implies potentially numerous and diverse mechanisms by which regulatory variants act on gene regulation. The authors set out to find evidence for one of these mechanisms, namely perturbation of TF binding sites. They begin by showing that variation in histone modifications has a strong genetic basis and that the polymorphisms influencing these marks overlap with known regulatory variants such as eQTLs. They then show that polymorphisms in TF binding sites associate with changes in histone marks, providing evidence for directionality in the relationship between these marks and gene regulation. In essence, their results suggest that histone modifications are directed, at least in part, by TF binding. Finally, they find that regulatory variants can have an impact on the molecular phenotypes of distal regions.

I found this paper, as well as the other three previously mentioned, to be quite interesting. I think these papers show that our understanding of gene regulation is still very simplistic. With the advent of high-throughput molecular assays like ChIP-Seq and DNase-Seq, we can begin to interrogate the complex role of regulatory variation on many phenotypes. In doing so, it is of primary interest to ask questions regarding directionality. How do a given set of molecular phenotypes relate? Do these phenotypes represent a cause or a consequence of genome function? How do the diverse elements of gene regulation function together to build complex phenotypes?

References

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Paper author Jonathan Pritchard is a professor in the Departments of Genetics and Biology.

Paper author Jonathan Pritchard is a professor in the Departments of Genetics and Biology.