Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data

Abstract

BackgroundIn the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.MethodsWe compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.ResultsDifferent combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.ConclusionsOur results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.

Family-based approaches: design, imputation, analysis, and beyond

“…Bhatnagar et al [ 30 ] compared the performance of methods that can be used to detect evidence for biased transmission and in doing so, also identified an additional potential QC step that may be useful in pedigree samples, as shown by application to observed vs. imputed sequence data. The papers by Sippy et al [ 31 ] and Saad et al [ 32 ] address approaches for selecting a subset of subjects for sequencing when there are constraints on the number of subjects that can be sequenced in a family study. Finally, the papers by Saad et al [ 32 ] and Lent et al [ 33 ] compared and evaluated approaches for combining both pedigree-agnostic and pedigree-based methods to improve genotype imputation from subjects with WGS data into those without.…”

“…The papers by Sippy et al [ 31 ] and Saad et al [ 32 ] address approaches for selecting a subset of subjects for sequencing when there are constraints on the number of subjects that can be sequenced in a family study. Finally, the papers by Saad et al [ 32 ] and Lent et al [ 33 ] compared and evaluated approaches for combining both pedigree-agnostic and pedigree-based methods to improve genotype imputation from subjects with WGS data into those without. Here, we will use “pedigree-agnostic” for genotype imputation methods that were designed for use on unrelated samples, but can be applied to samples from pedigrees, although without using the pedigree information.…”

“…Three other papers also applied a similar linear mixed model approach to the family data [ 31 , 34 , 35 ]. Finally, Saad et al [ 32 ] introduced a novel approach to combine transmission information obtained from pedigree data with identity-by-descent (IBD) inferred between individuals in different pedigrees to increase linkage evidence, thus, in some senses, combining both pedigree- and family-based approaches.…”

“…One paper [ 33 ] used only the WGS data, without additionally including trait phenotypes. Eight papers [ 30 – 32 , 34 , 35 , 38 , 39 , 41 ] used 1 or more blood pressure phenotypes, with all but 2 papers [ 30 , 31 ] evaluating all 200 phenotypic replicates. Seven of these 8 papers evaluated both the blood pressure data at either one particular visit, or collapsed the multiple temporal measurements into a single variable.…”

Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees

“…GIGI can handle large pedigrees efficiently, whereas Merlin cannot, thus requiring pedigree splitting or trimming. In previous studies, the performance of GIGI was compared to several population-based imputation methods (Saad et al 2016), while the performance of Merlin was separately evaluated on trimmed pedigrees (Lent et al 2016). The two programs were not compared directly on large pedigrees, although other studies have shown that both GIGI and Merlin perform well for rare variant imputation but not as well for common variants Saad and Wijsman 2014b).…”