@article {8041, title = {Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel.}, journal = {Nat Commun}, volume = {6}, year = {2015}, month = {2015}, pages = {8111}, abstract = {

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1\% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants.

}, issn = {2041-1723}, doi = {10.1038/ncomms9111}, author = {Huang, Jie and Howie, Bryan and McCarthy, Shane and Memari, Yasin and Walter, Klaudia and Min, Josine L and Danecek, Petr and Malerba, Giovanni and Trabetti, Elisabetta and Zheng, Hou-Feng and Gambaro, Giovanni and Richards, J Brent and Durbin, Richard and Timpson, Nicholas J and Marchini, Jonathan and Soranzo, Nicole} } @article {7733, title = {Multicohort analysis of the maternal age effect on recombination.}, journal = {Nat Commun}, volume = {6}, year = {2015}, month = {2015}, pages = {7846}, abstract = {

Several studies have reported that the number of crossovers increases with maternal age in humans, but others have found the opposite. Resolving the true effect has implications for understanding the maternal age effect on aneuploidies. Here, we revisit this question in the largest sample to date using single nucleotide polymorphism (SNP)-chip data, comprising over 6,000 meioses from nine cohorts. We develop and fit a hierarchical model to allow for differences between cohorts and between mothers. We estimate that over 10 years, the expected number of maternal crossovers increases by 2.1\% (95\% credible interval (0.98\%, 3.3\%)). Our results are not consistent with the larger positive and negative effects previously reported in smaller cohorts. We see heterogeneity between cohorts that is likely due to chance effects in smaller samples, or possibly to confounders, emphasizing that care should be taken when interpreting results from any specific cohort about the effect of maternal age on recombination.

}, issn = {2041-1723}, doi = {10.1038/ncomms8846}, author = {Martin, Hilary C and Christ, Ryan and Hussin, Julie G and O{\textquoteright}Connell, Jared and Gordon, Scott and Mbarek, Hamdi and Hottenga, Jouke-Jan and McAloney, Kerrie and Willemsen, Gonnecke and Gasparini, Paolo and Pirastu, Nicola and Montgomery, Grant W and Navarro, Pau and Soranzo, Nicole and Toniolo, Daniela and Vitart, Veronique and Wilson, James F and Marchini, Jonathan and Boomsma, Dorret I and Martin, Nicholas G and Donnelly, Peter} } @article {3520, title = {A general approach for haplotype phasing across the full spectrum of relatedness.}, journal = {PLoS Genet}, volume = {10}, year = {2014}, month = {2014 Apr}, pages = {e1004234}, abstract = {

Many existing cohorts contain a range of relatedness between genotyped individuals, either by design or by chance. Haplotype estimation in such cohorts is a central step in many downstream analyses. Using genotypes from six cohorts from isolated populations and two cohorts from non-isolated populations, we have investigated the performance of different phasing methods designed for nominally {\textquoteright}unrelated{\textquoteright} individuals. We find that SHAPEIT2 produces much lower switch error rates in all cohorts compared to other methods, including those designed specifically for isolated populations. In particular, when large amounts of IBD sharing is present, SHAPEIT2 infers close to perfect haplotypes. Based on these results we have developed a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals. First SHAPEIT2 is run ignoring all explicit family information. We then apply a novel HMM method (duoHMM) to combine the SHAPEIT2 haplotypes with any family information to infer the inheritance pattern of each meiosis at all sites across each chromosome. This allows the correction of switch errors, detection of recombination events and genotyping errors. We show that the method detects numbers of recombination events that align very well with expectations based on genetic maps, and that it infers far fewer spurious recombination events than Merlin. The method can also detect genotyping errors and infer recombination events in otherwise uninformative families, such as trios and duos. The detected recombination events can be used in association scans for recombination phenotypes. The method provides a simple and unified approach to haplotype estimation, that will be of interest to researchers in the fields of human, animal and plant genetics.

}, keywords = {Chromosome Mapping, Cohort Effect, Family, Genotype, Haplotypes, Humans, Models, Genetic, Pedigree, Phenotype, Recombination, Genetic}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1004234}, author = {O{\textquoteright}Connell, Jared and Gurdasani, Deepti and Delaneau, Olivier and Pirastu, Nicola and Ulivi, Sheila and Cocca, Massimiliano and Traglia, Michela and Huang, Jie and Huffman, Jennifer E and Rudan, Igor and McQuillan, Ruth and Fraser, Ross M and Campbell, Harry and Polasek, Ozren and Asiki, Gershim and Ekoru, Kenneth and Hayward, Caroline and Wright, Alan F and Vitart, Veronique and Navarro, Pau and Zagury, Jean-Francois and Wilson, James F and Toniolo, Daniela and Gasparini, Paolo and Soranzo, Nicole and Sandhu, Manjinder S and Marchini, Jonathan} }