PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population

Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.</p

Playing on common ground: Spaces of sport, education and corporate connectivity, contestation and creativity

In this article we examine connectivities within the ‘messy’ organizational commons of sport, education and corporate partnerships. As scholars forewarn, there are currently key stakeholders within the commons that that have set agendas, occupied ideological and physical terrain, and legitimized a presence and authority. The intertwining of organizations here is an evident function of an increased symbiosis between sport, education and governmental and non-governmental stakeholders to carve out significant sector spaces, and exert authority and power over the creation, implementation and ownership ‘collaborative’ and intersectional work. Drawing on spatial theorists, Henri Lefebvre and Yi Fu Tuan, and examples from FIFA and the IOC, we present a conceptual framework of global stakeholder relations. Focusing of processes of thought, production and action, we offer an intersectional critique of the nuances of Sport–Corporate–Education nexus and consider possibilities and potential for sport education spaces to be reconfigured ane

PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population

<div><p>Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (<u>P</u>edig<u>R</u>ee <u>IM</u>putation <u>AL</u>gorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.</p></div

Parental origin assignment process.

<p>For a given quasi-founder, we denote his/her haplotypes by A and B, and (by convention) the first is paternal and the second is maternal. At each SNV, we calculate a 2×2 matrix of kinships (Step 1) between each of the proband’s parents and each subject in the A and B IBD cliques. Using these, we generate a parental haplotype separation measure m (Step 2). If m≈1, A and B are already correctly ordered; if m≈-1, they should be swapped. If the majority of the SNVs agree on the same swapping (indicated by a sample separation M sufficiently close to 1 in Step 3), we assign paternal origin and reorder A and B accordingly (Step 4).</p

The imputation pipeline.

<p>Given a pedigree tree of 3,671 Hutterites (1), 1,415 individuals in the three most recent generations (within the red box) were genotyped with framework markers (2). The first part of the pipeline (steps 2–6) depends only on the framework marker data; the second part (steps 7–9) imputes the whole genome sequence variants. First, estimates of identity coefficients and the transition rate parameter λ [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004139#pcbi.1004139.ref024" target="_blank">24</a>] between each pair of the 1,415 individuals are calculated (3). The framework genotypes are then phased (4), IBD segments between haplotypes are identified using a HMM (5), and indexed into an efficient data structure consisting of IBD cliques (6). Haplotypes are assigned parental origins consistent across the pedigree using the cliques (7). Then, the whole genome sequences of 98 Hutterites (8) are cleaned using several filters, including a novel generalized Mendelian error check (9), and imputed to the remaining 1,317 Hutterites using IBD cliques (10). Call rates are boosted by imputing as many of remaining genotypes as possible using an LD-based imputation method, IMPUTE2 (11). To ensure that accuracy is not compromised, we calculate the concordance of the shared genotypes between the two methods and keep only variants that are highly concordant (12).</p

Partitioning an IBD-sharing graph into cliques.

<p>(1) IBD segments are indexed into a graph at each SNV. Nodes represent haplotypes (denoted A-H). Each pair of haplotypes that share an IBD segment at the SNV is connected with a link whose weight equals the HMM posterior probability. (2) Link weights are replaced by affinities. Links with small original weight or affinity are removed (3); all nodes within each of the resulting connected components are connected (4).</p