Bay Area Population Genomics Meeting XI

Dear population genomicists of the Bay Area,

The tenth Bay Area Population genomics meeting was great, thanks to CEHG Stanford for hosting it. We will be hosting BAPG XI at UC Davis on December 6, 2014, and we hope to see you all there!

9.30am-10am Breakfast

10am-11am Session 1
10.00am Gideon Bradburd (UC Davis)
The Geography of Genetic Admixture
10.20am Rajiv McCoy (Stanford)
Causes and consequences of aneuploidy in human embryos
10.40am Laurie Stevison (UCSF)
Time-scale of recombination rate evolution in great apes

11am-11.30am Coffee

11.30am-12.30pm  Session 2
11.30am Kelley Harris (UC Berkeley)    
Recent evolution of the mutation rate and spectrum in Europeans
11.50am Tim Beissinger (UC Davis)       
Patterns of demography and selection since maize domestication
12.10pm Yoosook Lee (UC Davis)            
Genome evolution of malaria vectors in response to the increased usage of insecticide-treated bed nets

12.30-1.30 pm Lunch

1.30pm Session 3
1.30pm Daniel Weissman (UC Berkeley)
Minimal-assumption inference from genomic data
1.50pm Sandeep Venkataram (Stanford)
The adaptive mutation spectrum from yeast experimental evolution
2.10pm Chris Ellison (UC Berkeley)          
Transposable elements have rewired and fine-tuned the dosage compensation gene regulatory network in Drosophila miranda
2.30pm Andres Moreno (Stanford)           
Native American genomics and beyond

2.50pm Posters/Social

Thanks to Ancestry.com and the Population Biology group at Davis for sponsorship.

Travel options: The Amtrak from Richmond arrives at 9:22am into Davis, and the lecture hall is just ~15 minute walk from the station. There’s also a carpool sheet on the google doc, so that people can coordinate ride-shares. We’ll send out links for maps, parking, etc closer to the time.

Here’s what we need from you:

1. Register! Registration is free but required, so we can make sure we order enough food and get a big enough room. Registration closes November 22nd. To register add your name to the google doc.

2. Sign up for a talk! On the second page of the google doc .

3. Sign up for a poster! Sign up as part of registration.

4. Please forward this email along to other lab members who aren’t on the mailing list. Folks can sign up for future BAPG announcements at the google group.

5. Show up in Davis on December 6th!

Looking forward to seeing you in Davis.

The Coop Lab: Simon Aeschbacher, Chenling Antelope, Jeremy Berg, Gideon Bradburd, Vince Buffalo, Graham Coop, Ivan Juric, Kristin Lee, Alisa Sedghifar
P.S. the twitter hashtag will be #BAPGXI.

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Some thoughts on our polygenic selection paper.

Our paper on a general method to detect divergent selection among populations on quantitative traits was published today in PLOS Genetics (Berg and Coop 2014, code, the paper was previously up on the bioRxiv and Haldane’s sieve). Our method uses the knowledge gained about the genetic basis of a trait from GWAS, which allows us to compare allele frequency patterns across many loci to determine whether there is evidence for that they are all responding in concert to some selective pressure.

The method is, in large part, an attempt to set up the correct null model for the distribution of among population variation, and to circumvent the problem of environmental confounding in trying to determine whether certain differences between human populations may have been caused by selection.

The central idea underlying our tests, as well as many quantitative genetics approaches that have been developed over the last 2 decades (see paper for references), is that we can use genetic marker data to get a good measurement of how much allele frequencies vary among populations. Then, if the environment can be held constant across populations, we ought to be able to use the allele frequency information to predict how large a range of phenotypic differences among populations we would expect under a neutral model of genetic drift. Once we have this prediction in hand, a neutrality test for that phenotype follows.

The difficulty in approaching this question for human populations is that we of course cannot hold the environment constant across human populations, and so we can’t tell from direct phenotypic measurements whether any unusually large differences among populations that are observed are due to a history of natural selection, genetic drift, or entirely due to environmental differences among populations.

To get around this problem, we rely on the GWAS data, which comes in the form of a list of a specific set of SNPs associated with a phenotype, as well an estimate of the additive effect size for each associated variant. Importantly, these alleles were identified as associated with the phenotype in a specific population. We use the estimated effect sizes, in combination with allele frequencies at these loci, to predict the mean value of the trait we would expect to observe in each population, under a model in which all effects are additive and the environment constant. We can then test whether these genetic values differ among populations more than we would expect under a model of genetic drift.

We think that this test is potentially a good way to look for divergent selection pressures acting on traits. We expect that over the coming decade approaches such as ours, and more sophisticated developments, will be applied to many different traits. This offers a fascinating chance to study the role of natural selection in shaping phenotypic diversity among populations, in a range of species including humans.

However, beyond saying that selection, and not merely genetic drift, have acted on the loci involved in the trait, the interpretation of a positive results is challenging. We spend some time in the paper discussing these difficulties. Many of these are not new problems and some of these issues relate more generally to why it is difficult to study adaptation in natural populations. That said, it seems worth reiterating these issues and caveats carefully, as it is easy, and potentially fraught, to over interpret differences among human populations.

To do so, we use our signal of selection involving human height as an example. One of our clearest signals of selection is on height GWAS loci between Northern and Southern Europeans, as previously reported by Turchin et al. Nature Genetics. Turchin et al. and our research shows that alleles involved in height, at least those identified to date, show greater, directional differences between northern and southern European populations than would be expected under a model of genetic drift alone.

The first thing to say is that this signal is really quite subtle, and even where selection has acted, drift and gene flow still undoubtedly contribute to the differences we observe. Moreover, most of these variants are polymorphic in most of these populations, so they say little about an individual, i.e., most the variance is still among individuals within populations rather than among populations.

Nonetheless, average observed height clearly differs among European populations in the direction suggested by Turchin et al. and our results based on genetic data. Therefore, it is plausible that the observed differences in height between European populations are partially genetic in nature and have been subject to selection. We do not know this for certain, however, because of the large environmental contribution, e.g., diet, to traits such as height (as well as most human traits). Certainly the large changes in height over the past centuries in response to changes in nutrition and health highlight the huge role of the environment in height (see graph below). It remains possible that the majority of the observed mean difference among populations is environmental. While the relative order of populations has remained relatively constant, some have changed their ordering substantially, suggesting that we have to be very cautious about interpreting between population differences.

data from Hatton and Bray: Long Run Trends in the Heights of European Men, 19th-20th Centuries Econ Hum Biol. 2010 Dec;8(3):405-13. doi: 10.1016/j.ehb.2010.03.001. url: http://www.ncbi.nlm.nih.gov/pubmed/20399715 data extracted from pdf: http://privatewww.essex.ac.uk/~hatton/Tim_height_paper.pdf . Code here: https://github.com/cooplab/Height_over_time

data from Hatton and Bray: Long Run Trends in the Heights of European Men, 19th-20th Centuries Econ Hum Biol. 2010 Dec;8(3):405-13. doi: 10.1016/j.ehb.2010.03.001. url: http://www.ncbi.nlm.nih.gov/pubmed/20399715 data extracted from pdf: http://privatewww.essex.ac.uk/~hatton/Tim_height_paper.pdf . Code here: https://github.com/cooplab/Height_over_time

Furthermore, only a small fraction of the variance in height within Europeans populations has been mapped to date (even less of the variance has been explained for many other traits). It is possible that as yet unmapped loci could change our understanding of the genetics of height in Europe. We could, for example find alleles that increase height which are common only in Southern Europe. While this is probably unlikely for height, it is exactly the case for skin pigmentation, where the partially convergent evolution of light skin pigmentation leads to confusing signals of adaptation that would be easy to misinterpret if we did not have a relatively good understanding of the genetics of skin pigmentation (see paper for details).

Also confusing the interpretation of the results is the potential for genotype by environment (GxE) or genotype by genotype interactions. Alleles may have different or even opposite effects in different environments and genetic backgrounds. While this cannot generate a false signal of selection, it does mean that the genetic values calculated for populations should not be treated as reliable phenotypic predictions. For height, it appears that many loci do seem to act in a reasonably additive manner and have consistent effects across populations. However, even given this trait, we have to be careful as we do not know if this holds for the subset of height GWAS loci that are driving our signal. Moreover, it is likely that traits other than height may have substantial GxE and so caution is warranted.

Thus, there is still a huge amount we do not understand about how selection and drift have shaped height, or any phenotype, within Europe. We also do not know that the differences in allele frequencies at variants associated with height reflect direct selection on height, or whether our observations are due to selection on some other phenotype (that may not even be on our radar in current environmental conditions, or may not differ among populations today). Even if selection acted directly on height, we would not know what the specific selection pressure that drove this difference. We also do not know the timing of this selection: whether it represents a long-term trend or is just a snapshot of some fluctuation, or whether selection took place in Europe or is the result of differential gene flow from populations who themselves diverged adaptively in height. While we expect that results from other fields, physical anthropology in particular, will be helpful, integrating these results to paint a more complete portrait of the evolution of human height will likely be challenging.

All of these caveats and concerns may seem overly cautious when expressed about studying the genetics of height differences among human populations. It seems quite likely that observed height differences among populations will be partially genetic in nature, and due in part to differential selection, consist with our and Turchin et al’s results. However, to establish this as a scientific finding, rather than a plausible hunch requires much more work. It is really quite humbling that we are only just beginning to understand the long-term role of selection and drift in shaping a phenotype as well studied as height. Undoubtedly, we will learn a lot over the coming decade about how drift, selection, and migration have shaped the genetic basis of phenotypes across populations, but these insights will only come about by the careful study of these phenotypes and the separation of genetic and environmental components.

While much of this probably seems obvious to most human geneticists, it is clear that there is huge public interest in genetic studies of human evolution and phenotypic differences and an almost equal potential for misunderstanding such studies. A sad case in point is the recent book by Nicholas Wade, which many reviews and blogs have already rightly criticized extensively. We hope that this blog post will help to layout some of the caveats that come with studying adaptation of complex traits even when good methods and GWAS are available.

Jeremy Berg and Graham Coop

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Coop lab talks from Evol2014

Our talks from Evolution are below. Much of the work is unpublished and provisional, so please contact us for updates/details if you are interested in the work.

Alisa’s talk on a Model of Genome-Wide Patterns of Ancestry in a Secondary Contact Zone
Alisa_SedghifarPresentation.pdf

Graham’s Evolution talk on adaptation to patchy environments (also given in slightly modified form at SMBE)
Coop-Evolution-2014.pdf
the first half is joint work with Peter Ralph, the second is a collaboration with Chenling, Kevin Wright, and John Willis.

Gideon’s poster on a novel method for visualizing spatial structure and admixture (given at SMBE, and as guerrilla poster at Evolution by graham):
gideon_bradburd_SMBE_poster.pdf

Simon’s talk on Exploring genome-wide signals of selection against gene flow
Simon_Aeschbacher_Evolution2014.pdf, Simon’s talk was videoed and posted to youtube.

Jeremy’s talk on testing for adaptive phenotypic divergence
Figshare

Yaniv’s talk on why sperm might evolve to help keep female meiosis fair:
yaviv_brandvain_evolution2014.pdf

Alex Cagan’s [@ATJCagan] sketch of Graham’s talk from SMBE:
Bpy0ra4CQAA9c0L.jpg_large
original tweet. All of his sketches of the talks were great!

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Coop lab at Evolution 2014

Evolution_logo

We’re looking forward to seeing old friends and meeting new folks at Evolution 2014. Here’s a list of the talks by the Coop lab at the conference.


Jeremy Berg. General extensions of Qst/Fst for detecting adaptation in quantitative traits.

2A_306A Methods for Migration. Sunday, 9:00 AM – 9:15 AM Room: 306 A

Graham Coop. Parallel evolution during local adaptation. ASN Vice Presidential Symposium:Modern approaches to local adaptation. Date: Monday, 3:15 PM – 3:45 PM. Room 402

Alisa Sedghifar. A Model of Genome-Wide Patterns of Ancestry in a Secondary Contact Zone
3C_303 Hybridization. Monday, 2:00 PM – 2:15 PM Room 303

Yaniv Brandvain. Sperm do not evolve to collaborate in female meiotic drive. 4C_306A Sex and Evolution Tuesday, 2:30 PM – 2:45 PM Room: 306 A

Simon Aeschbacher. Exploring genome-wide signals of selection against gene flow 4D_301A Genome Evolution. Tuesday, 3:15 PM – 4:30 PM. Room: 301 A

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Coop Lab T-shirt

The theme for the coop lab T-shirt this year is POPGEN IN SPACE!!! A bunch of the research interests in the lab currently focus on spatial population genetics, so it seemed like a great opportunity to bust out a homage to “pigs in space” featuring Fisher, Haldane, and Wright. Images below, we’ll post some pics of the lab in the t-shirts later.

John Novembre and I held a symposium by this name at SMBE a few years ago, we had this idea for the T-shirt then but never quite got around to it.

Slide4

Slide8

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Genomic variation in sharing between siblings

Siblings of the same sex resemble each other to varying degrees. For most traits this is mostly due to differences in the environment between them, and its effects on their development. However, siblings also subtly differ in their genomic similarity, due to the randomness of segregation and recombination. I thought I’d extend our previously discussion of genomic sharing between relatives (see here) to show how variable genomic sharing is between siblings. Again using data from real transmissions.

Below is a picture of the sharing between a pair of sibs. The parent genome is shown as 2 pairs of chromosomes, for each of 22 autosomes. These are coloured by the genomic material they transmitted to the child. The third plot of each row shows the overlap between the siblings’ genomes in light purple. So, for example, the two sibs (on page 1) share all of chromosome 21 as inherited from the father, but only the right tip of the chr21 in the mother. You can also see genomic stretches where the pair of sibs would share their both of their genotype (i.e. both alleles), e.g. the sibs share both maternal and paternal alleles for the first ~1/3 of chr22.

overlap_btwn_siblings1

Here’s a slide show illustrating this across 10 pairs of siblings.

This slideshow requires JavaScript.

I’m posting these as we are currently doing a reading group on recent advances in Quantitative Genetics. This week Gideon and Reid are leading a discussion of Visscher et al “Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings.”. In that paper they have a plot of the variation of how much of their autosomal genomes siblings share:
journal.pgen.0020041.g001
note that the distribution is centered on a half but with a small amount of scatter around that due to the randomness of mendelian segregation and the fact that chromosomes are inherited in big chunks. (I apologize for the default excel graph, but I didn’t make it ;) )

Visscher et al make really nice use of this slight variability in how much of the genome sibs share to learn about how much variation in height within a population is due to genetic variation. They use the fact that sibs who share slightly more of their genome (>0.5) should have more similar heights, than sibs who share less of their genomes (<0.5). This allows them to partition out how much of the resemblance between siblings is due to a shared environment, as opposed to shared genomes.

This is a really nice application variation in genomic sharing (although the paper is a little tough going in places). It also makes me wonder if sibs are actually unconsciously, weakly aware of these subtle genomic differences (through their similarity in a range of traits, including height etc). I could imagine doing a study where siblings (or others) are asked to assess how similar they are/feel, and then assessing whether this is weakly correlated with the fraction of the genome shared. I keep meaning to followup on this idea with some popgen theory to assess how this might play out in modifying kin-selection and altruism between sibs and other relatives. Anyone know if this has this been looked at before?

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A fond farewell to Yaniv!

The awesome Yaniv Brandvain has flown the Coop lab, and starts his evolutionary plant genomics lab at the University of Minnesota today. It’s been wonderful having Yaniv as a member of the Coop lab. Yaniv brought a wonderful sense of community to Davis, and the Coop lab and the Center for Population Biology benefited enormously from his intellectual generosity. We are sad to see him go, but we know the future holds great things for him and his lab.

You can get some sense of the diverse projects that Yaniv worked on in his time in Davis from his recent publications. We also have a more papers in the pipeline, so keep an eye out for those.

The Coop lab out for dinner:
photo-13
(Kristin, Alisa, Jeremy, Chenling, Yaniv at a dinner for Yaniv and Jeremy’s Quals exam. Gideon not present. )

As a leaving present we got Yaniv the Princeton guide to Evolution:

photo-14
It was signed by many folks at Davis, and had many messages of support from Yaniv’s many collaborators and authors of many of the chapters (who Yaniv knows). A small, but fitting, tribute to mark the evolution of a wonderful scientist.

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