Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands’

Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with ‘true’ genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed s

Population-specific genotype imputations using minimac or IMPUTE2

In order to meaningfully analyze common and rare genetic variants, results from genome-wide association studies (GWASs) of multiple cohorts need to be combined in a meta-analysis in order to obtain enough power. This requires all cohorts to have the same single-nucleotide polymorphisms (SNPs) in their GWASs. To this end, genotypes that have not been measured in a given cohort can be imputed on the basis of a set of reference haplotypes. This protocol provides guidelines for performing imputations

A framework for the detection of de novo mutations in family-based sequencing data

Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father's age at conception and the number of DNMs in female offspring's X chromosome, consistent with previous literature reports

A framework for the detection of de novo mutations in family-based sequencing data

Francioli LC, Cretu-Stancu M, Garimella KV, et al. A framework for the detection of de novo mutations in family-based sequencing data. European Journal of Human Genetics. 2016;25(2):227-233

The canine era : the rise of a biomedical model

Since the annotation of its genome a decade ago, the dog has proven to be an excellent model for the study of inherited diseases. A large variety of spontaneous simple and complex phenotypes occur in dogs, providing physiologically relevant models to corresponding human conditions. In addition, gene discovery is facilitated in clinically less heterogeneous purebred dogs with closed population structures because smaller study cohorts and fewer markers are often sufficient to expose causal variants. Here, we review the development of genomic resources from microsatellites to whole-genome sequencing and give examples of successful findings that have followed the technological progress. The increasing amount of whole-genome sequence data warrants better functional annotation of the canine genome to more effectively utilise this unique model to understand genetic contributions in morphological, behavioural and other complex traits.Peer reviewe

Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10−6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis.

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, identifying variants in CCDC33 associated with susceptibility. Pneumococcal genetic variation explains a large amount of invasive potential (70%), but has no effect on severity. Serotype alone is insufficient to explain invasiveness, suggesting other pneumococcal factors are involved in progression to invasive disease. We identify pneumococcal genes involved in invasiveness including pspC and zmpD, and perform a human-bacteria interaction analysis. These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines