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A few good molecular ecologists: six months and 116 posts later
My usual Wednesday spot on The Molecular Ecologist is primetime real estate: a lot of journal table-of-contents get sent out on Tuesday/Wednesday and whole slew of people are in the office looking at computer screens.
This usually produces a nice readership on Wednesdays, expect for when I drew the unlucky straw for Christmas Eve. On December 24th, I posted a review of the first three months of posts from the five newest contributors to the blog. Since no one probably saw that post and we have just arrived at the end of our six month agreement as contributors, I’ve updated all the data to reflect our full tenure. For good measure, I’m publishing it on a Thursday to steal some of Arun’s fame.
Hopefully this can be used to catch some things you might have missed, see the past and future direction the content, and recognize the unique contributions from each contributor.
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Posted in blogging, Molecular Ecology views, Uncategorized
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Quantifying risks of consanguineous mating in humans
The efficacy of selection in purging a deleterious mutation from a randomly mating population depends on numerous factors, including dominance effects of alleles – see my previous posts. Simplistically, most new mutations are expected to be heterozygotic, and be purged less effectively if they are recessive, than dominant alleles in homozygotes. In other words, recessive mutations segregate at higher frequencies (in heterozygotes), than dominant mutant homozygotes, especially so for Mendelian traits.
In inbreeding populations, thus, there’s a greater reduction of population fitness due to the accumulation of deleterious alleles in homozygotes, a phenomenon commonly termed “inbreeding depression”. Modern humans are a classic example of repeated non-random, consanguineous mating, resulting in localized population structure. Understanding the effects of non-random mating on the accumulation of recessive deleterious mutations is of great interest to disease genetics, anthropology, and the social sciences at large.
A Hutterite family – Image courtesy: National Geographic Channel – http://channel.nationalgeographic.com/american-colony-meet-the-hutterites/galleries/barnburners/at/king-colony-family-portrait-54169/
In a recent publication, Gao et al. (2015) attempt to quantify the fitness effects of deleterious (particularly autosomal, recessive lethal – i.e. inviable homozygote) mutations, using a founder population of Hutterites with well documented pedigrees, since 1950. The Hutterites are a small community of South Dakotans founded by 64 ancestors in the 18th-19th centuries, that migrated over to the United States and established 3 communal farms, and have since married consanguineously.
In short, Gao et al. (2015) (a) simulated Mendelian inheritance at 14 autosomal recessive disease genes along small and large pedigrees of the Hutterite population, (b) quantified loss or manifestation of mutant alleles (that were present in the founder population), and (c) use a Bayesian approach for point estimates (and intervals) of the number of autosomal recessive lethal mutations in a haploid human genome. Their study finds that 57% of recessive mutations from the founding population were lost prior to 1950 (after which extensive disease and pedigree records exist), 19% of the surviving alleles were present in homozygotes, and that 8.1% of the founder alleles will be expected to manifest in the present generation. This is also equivalent to 0.29 recessive deleterious mutant alleles on an average per haploid human genome. More importantly,
(this study) suggests that the risk of autosomal recessive disorders that manifest after birth should be increased by 0.29/16 = 1.8% in offspring of first-cousin couples (assuming no difference in environmental factors).
Here’s some recent press coverage on this study: http://www.sciencedaily.com/releases/2015/04/150408100522.htm
http://www.nature.com/news/genomes-carry-a-heavy-burden-1.17304
Reference:
Gao, Z., Waggoner, D., Stephens, M., Ober, C. & Przeworski, M. Genetics 199, 1243–1254(2015). DOI: http://dx.doi.org/10.1534/genetics.114.173351
A mammoth bottleneck prior to extinction
Woolly mammoth
Here’s to back-to-back posts on extinct mammalian genomes!
Woolly mammoth genomes are all the rage. How do I know? Just check out the new book, pre-print, and paper that were recently published.
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Posted in conservation, genomics, next generation sequencing, Paleogenomics, population genetics
Tagged ancient DNA, mammoths, PSMC
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Extinct and extant Equus genomes reveal speciation with gene flow despite chromosome number variation
Painting of a quagga stallion by Nicolas Marechal, 1793. Photo from Wikipedia.
In their recent PNAS paper*, Hákon et al. generate full genome sequence data for each living species of asses and zebras, thus completing the set of genomes available for all extant species in the genus Equus (genomes for the donkey and horse have been published previously- see references below). The authors also collected full genome data for the quagga (Equus quagga quagga), a subspecies of the plains zebra that lived in South Africa until being driven to extinction in the 19th century. The last captive individual died in an Amsterdam zoo in 1883. In 1984, the quagga was the first extinct species to have its DNA sequenced. Hákon et al. used the Equus genome sequences to search for loci under selection, reconstruct demographic history, and measure gene flow among diverging lineages. Continue reading
Posted in genomics, speciation
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Clonal conundrum, part deux
© smh.com.au
In the second installment of the clonal conundrum, one hallmark of clonality is one that surprisingly hasn’t been validated that many times using species that have both sexually and asexually reproducing populations. Theoretically, clonal reproduction should generate massive …
Heterozygote excess
Mutations accumulate in clonal lineages as the two alleles at each locus irreversibly diverge within individuals (Balloux et al. 2003). This should generate largely negative Fis values. As clonality increases, Fis decreases:
Figure 1 from Balloux et al. (2003): Fis as a function of the rate of clonal reproduction. The line represents analytical results and the solid circles simulation results.
Balloux et al. (2003) suggest that strict clonality can, thus, be easily detected in populations due to the excess of heterozygotes. But, the occurrence of sex, even infrequently, what they term cryptic sex, might also be detected. Fis values will be variable among loci:
Figure 2 from Balloux et al. (2003). Standard errors of Fis as a function of the rate of clonal reproduction.
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Procrustes Analyses in R
Procrustes transformations (i.e. a form of multidimensional scaling that allows the comparison of two data sets) have been used extensively in recent literature to assess the similarity of geographical and genetic distributions of species, following the lead of Wang et al. (2010). See Jeremy’s post describing the method and its application to genomic data. I’ve scoured the internet but can’t seem to find a way to make these plots using R (or any software for that matter). Here’s a simple tutorial on how to do this in R using principal components (PC1, and PC2) already computed from a PCA on SNP data (using your favorite tool – eg. EIGENSOFT, or even in R, and geographical coordinates for each individual. This tutorial uses the package “MCMCpack” to compute Procrustes transformations. As an example, I am using a chunk of the European data-set published in Novembre et al. (2008), which I downloaded from here. Note that this file already contains geographical information (latitude and longitude), and PC’s 1 and 2 computed using EIGENSOFT.
Procrustes analyses of genetic and geographic coordinates in Europe, sensu Wang et al. (2010).
install.packages(“MCMCpack”)
library(MCMCpack)
library(maps)
nov<-read.table("PCA.txt",header=TRUE,sep="\t")
X<-as.matrix(cbind(nov$longitude,nov$latitude))
Xstar<-as.matrix(cbind(nov$PC1,nov$PC2))
p<-procrustes(Xstar,X,translation=TRUE,dilation=TRUE)
map(database="world",regions=c('belgium','netherlands',
'austria','denmark','portugal','italy','spain','uk','germany',
'france','sweden','norway','finland','luxemburg',
'greece','monaco','ireland'),xlim=c(-10,20),ylim=c(35,60))
map.axes()
text(p$X.new,col=c(nov$alabels),labels=nov$alabels,cex=0.45)
text(nov$longitude,nov$latitude,col=c(nov$alabels),
labels=nov$alabels)
And voila! My best reproduction of Figure 1 of Wang et al. (2010) – please note that I only used a portion of the data-set in this tutorial. Feel free to play around with other data-sets, and let me know how it goes!
Posted in genomics, howto, population genetics, R, software
Tagged data visualization, genomics, population genetics, population structure
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Interview: The landscape of Ian Wang's reading list
Relationships between pairwise genetic distance (dots) and both geographic and environmental distances. Linked from supplemental material of Wang and Bradburd (2014)
To follow up on some recent posts on The Molecular Ecologist about landscape genetics and isolation by environment, I brought in an expert.
Dr. Ian Wang is an assistant professor in the Department of Environment Science, Policy, and Management at UC Berkeley and has spent the bulk of his career asking questions and developing techniques that further the general field of landscape genetics. Dr. Wang was a visiting seminar speaker here at Ohio State a couple of weeks ago, and he graciously volunteered to answer some questions based on our conversations during his visit:
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Adaptive divergence in the monkey flower
The yellow monkey flower, Mimulus guttatus. Photo from wikimedia.org
Theory suggests adaptive divergence can proceed in the face of gene flow when adaptive alleles occur in areas of the genome, such as chromosomal inversions, that are protected from recombination, which can break up beneficial allele pairings. In their recent Evolution paper, Twyford and Friedman determine phylogeographic structure and the role of an inversion in the adaptive divergence of life history strategies in the yellow monkey flower, Mimulus guttatus, across northwest North America.
Mimulus gattatus plants employ perennial or annual life history strategies. Perennial plants tend to occur in wetter sites, invest heavily in vegetative growth, and flower later in the season while annual plants occur in drier, drought prone areas and reproduce early in the season. The two ecotypes differ in flowering and senescence time, flower size, and potential to spread clonally, but have overlapping ranges and are fully interfertile. Previous studies found adaptive traits that differ between the perennial and adaptive ecotypes map to a chromosomal inversion that contains hundreds of genes. Continue reading
Posted in adaptation, evolution, genomics, plants
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Clonal conundrum, part un
Molecular ecologists are faced with a clonal conundrum when we wish to investigate the evolutionary ecology of clonal organisms. An attack of the clones is not something that should frighten one away …
Attack or herding of the clones? © wirdou.com
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The brave new world of environmental genomics
A new special issue of Heredity reflects on the recent advances in environmental genomics (see other posts about eDNA here and here) and highlights the ways NGS can aid in characterizing complex biological systems.
© usatoday.com
The cryptic, as well as the rare but active fraction of biodiversity are now accessible to study in all environments. (Joly and Faure 2015)
One of the highlights was the review of E&R (evolve and resequence) by Schlotterer et al. (2015). The authors discuss the use whole genome sequencing of pools of individuals (Pool-Seq).
[The selection for a well-defined trait in a controlled experiment], assures that both the phenotypic and the underlying genomic response are triggered either directly or indirectly by the selection regime applied during the experiment.
The cool thing about this approach is what can be done beyond the characterization of different allele frequencies between two selection regimes (i.e., selected and control populations). It is possible to sample evolving populations at different time points and, thus,
to study the trajectories of the selected alleles and thus elucidate their evolutionary dynamics.
Schlotterer et al. continue with the potential challenges E&R studies face, including experimental design and validation of candidate loci, but conclude that the reliability of E&R interpretation will continue to improve. In time, the method will be extended to a broader range of taxa and species.
References
Joly D and Faure D (2015) Next-generation sequencing propels environmental genomics to the front line of research. Heredity 114 429-430. doi:10.1038/hdy.2015.23
Schlötterer C, Kofler R, Versace E, Tobler R and Franssen SU (2015) Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation. Heredity 114, 431-440. doi:10.1038/hdy.2014.86
Posted in bioinformatics, Coevolution, DNA barcoding, evolution, genomics
Tagged E&R, Evolution, genomics, NGS
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