A genomic region containing RNF212 is associated with sexually-dimorphic recombination rate variation in wild Soay sheep (Ovis aries).
Susan E Johnston, Jon Slate, Josephine M Pemberton
Meiotic recombination breaks down linkage disequilibrium and forms new haplotypes, meaning that it is an important driver of diversity in eukaryotic genomes. Understanding the causes of variation in recombination rate is not only important in interpreting and predicting evolutionary phenomena, but also for understanding the potential of a population to respond to selection. Yet, there remains little data on if, how and why recombination rate varies in natural populations. Here, we used extensive pedigree and high-density SNP information in a wild population of Soay sheep (Ovis aries) to determine individual crossovers in 3330 gametes from 813 individuals. Using these data, we investigated the recombination landscape and the genetic architecture of individual autosomal recombination rate. The population was strongly heterochiasmic (male to female linkage map ratio = 1.31), driven by significantly elevated levels of male recombination in sub-telomeric regions. Autosomal recombination rate was heritable in both sexes (h2 = 0.16 & 0.12 in females and males, respectively), but with different genetic architectures. In females, 46.7% of heritable variation was explained by a sub-telomeric region on chromosome 6; a genome-wide association study showed the strongest associations at RNF212, with further associations observed at a nearby ~374kb region of complete linkage disequilibrium containing three additional candidate loci, CPLX1, GAK and PCGF3. This region did not affect male recombination rate. A second region on chromosome 7 containing REC8 and RNF212B explained 26.2% of heritable variation in recombination rate in both sexes, with further single locus associations identified on chromosome 3. Our findings provide a key empirical example of the genetic architecture of recombination rate in a wild mammal population with male-biased crossover frequency.
GWAS identifies a single selective sweep for age of maturation in wild and cultivated Atlantic salmon males.
Fernando Ayllon, Erik Kjærner-Semb, Tomasz Furmanek, Vidar Wennevik, Monica Solberg, Harald Sægrov, Kurt Urdal, Geir Dahle, Geir Lasse Taranger, Kevin A Glover, Markus S Almén, Carl J Rubin, Rolf B Edvardsen, Anna Wargelius
Abstract Background Sea age at sexual maturation displays large plasticity for wild Atlantic salmon males and varies between 1-5 years. This flexibility can also be observed in domesticated salmon. Previous studies have uncovered a genetic predisposition for age at maturity with moderate heritability, thus suggesting a polygenic nature of this trait. The aim with this study was to identify genomic regions and associated SNPs and genes conferring age at maturity in salmon. Results We performed a GWAS using a pool sequencing approach (n=20 per river and trait) of salmon returning as sexually mature either after one sea winter (2009) or after three sea winters (2011) in six rivers in Norway. The study revealed one major selective sweep, which covered 76 significant SNP in a 230 kb region of Chr 25. A SNP assay of other year classes of wild salmon and from cultivated fish supported this finding. The assay in cultivated fish reduced the haplotype conferring the trait to a region which covered 4 SNPs of a 2386 bp region containing the vgll3 gene. 2 of these SNPs caused miss-sense mutations in vgll3. Conclusions This study presents a single selective region in the genome for age at maturation in male Atlantic salmon. The SNPs identified may be used as QTLs to prevent early maturity in aquaculture and in monitoring programs of wild salmon. Interestingly, the identified vgll3 gene has previously been linked to time of puberty in humans, suggesting a conserved mechanism for time of puberty in vertebrates.
Fast and efficient QTL mapper for thousands of molecular phenotypes
Halit Ongen, Alfonso Buil, Andrew Brown, Emmanouil Dermitzakis, Olivier Delaneau
Motivation: In order to discover quantitative trait loci (QTLs), multi-dimensional genomic data sets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. Results: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted p-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot data sets can be now easily analyzed an order of magnitude faster than previous approaches. Availability: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/
Impact of the X chromosome and sex on regulatory variation
Kimberly R Kukurba, Princy Parsana, Kevin S Smith, Zachary Zappala, David A Knowles, Marie-Julie Favé, Xin Li, Xiaowei Zhu, James B Potash, Myrna M Weissman, Jianxin Shi, Anshul Kundaje, Douglas F Levinson, Philip Awadalla, Sara Mostafavi, Alexis Battle, Stephen B Montgomery
The X chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. We have conducted an analysis of the impact of both sex and the X chromosome on patterns of gene expression identified through transcriptome sequencing of whole blood from 922 individuals. We identified that genes on the X chromosome are more likely to have sex-specific expression compared to the autosomal genes. Furthermore, we identified a depletion of regulatory variants on the X chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X chromosome. To resolve the molecular mechanisms underlying such effects, we generated and connected sex-specific chromatin accessibility to sex-specific expression and regulatory variation. As sex-specific regulatory variants can inform sex differences in genetic disease prevalence, we have integrated our data with genome-wide association study data for multiple immune traits and to identify traits with significant sex biases. Together, our study provides genome-wide insight into how the X chromosome and sex shape human gene regulation and disease.
Genome variation and meiotic recombination in Plasmodium falciparum: insights from deep sequencing of genetic crosses
Alistair Miles, Zamin Iqbal, Paul Vauterin, Richard Pearson, Susana Campino, Michel Theron, Kelda Gould, Daniel Mead, Eleanor Drury, John O’Brien, Valentin Ruano Rubio, Bronwyn MacInnis, Jonathan Mwangi, Upeka Samarakoon, Lisa Ranford-Cartwright, Michael Ferdig, Karen Hayton, Xinzhuan Su, Thomas Wellems, Julian Rayner, Gil McVean, Dominic Kwiatkowski
The malaria parasite Plasmodium falciparum has a great capacity for evolutionary adaptation to evade host immunity and develop drug resistance. Current understanding of parasite evolution is impeded by the fact that a large fraction of the genome is either highly repetitive or highly variable, and thus difficult to analyse using short read technologies. Here we describe a resource of deep sequencing data on parents and progeny from genetic crosses, which has enabled us to perform the first integrated analysis of SNP, INDEL and complex polymorphisms, using Mendelian error rates as an indicator of genotypic accuracy. These data reveal that INDELs are exceptionally abundant and the dominant mode of polymorphism within the core genome. We analyse patterns of meiotic recombination, including the relative contribution of crossover and non-crossover events, and we observe several instances of recombination that modify copy number variants associated with drug resistance. We describe a novel web application that allows these data to be explored in detail.
Isolation-By-Distance-and-Time in a stepping-stone model
Nicolas Duforet-Frebourg, Montgomery Slatkin
With the great advances in ancient DNA extraction, population genetics data are now made of geographically separated individuals from both present and ancient times. However, population genetics theory about the joint effect of space and time has not been thoroughly studied. Based on the classical stepping–stone model, we develop the theory of Isolation by Distance and Time. We derive the correlation of allele frequencies between demes in the case where ancient samples are present in the data, and investigate the impact of edge effects with forward-in-time simulations. We also derive results about coalescent times in circular/toroidal models. As one of the most common way to investigate population structure is to apply principal component analysis, we evaluate the impact of this theory on plots of principal components. Our results demonstrate that time between samples is a non-negligible factor that requires new attention in population genetics.
Integrative approaches for large-scale transcriptome-wide association studies
Alexander Gusev, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda WJ Penninx, Rick Jansen, Eco JC de Geus, Dorret I Boomsma, Fred A Wright, Patrick F Sullivan, Elina Nikkola, Marcus Alvarez, Mete Civelek, Aldonis J Lusis, Terho Lehtimaki, Emma Raitoharju, Mika Kahonen, Ilkka Seppala, Olli Raitakari, Johanna Kuusisto, Markku Laakso, Alkes L Price, Paivi Pajukanta, Bogdan Pasaniuc
Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS1-3. We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.