Identification of key contributors in complex population structures.

Evaluating the genetic contribution of individuals to population structure is essential to select informative individuals for genome sequencing, genotype imputation and to ascertain complex population structures. Existing methods for the selection of informative individuals for genomic imputation solely focus on the identification of key ancestors, which can lead to a loss of phasing accuracy of the reference population. Currently many methods are independently applied to investigate complex population structures. Based on the Eigenvalue Decomposition (EVD) of a genomic relationship matrix we describe a novel approach to evaluate the genetic contribution of individuals to population structure. We combined the identification of key contributors with model-based clustering and population network visualization into an integrated three-step approach, which allows identification of high-resolution population structures and substructures around such key contributors. The approach was applied and validated in four disparate datasets including a simulated population (5,100 individuals and 10,000 SNPs), a highly structured experimental sheep population (1,421 individuals and 44,693 SNPs) and two large complex pedigree populations namely horse (1,077 individuals and 38,124 SNPs) and cattle (2,457 individuals and 45,765 SNPs). In the simulated and experimental sheep dataset, our method, which is unsupervised, successfully identified all known key contributors. Applying our three-step approach to the horse and cattle populations, we observed high-resolution population substructures including the absence of obvious important key contributors. Furthermore, we show that compared to commonly applied strategies to select informative individuals for genotype imputation including the computation of marginal gene contributions (Pedig) and the optimization of genetic relatedness (Rel), the selection of key contributors provided the highest phasing accuracies within the selected reference populations. The presented approach opens new perspectives in the characterization and informed management of populations in general, and in areas such as conservation genetics and selective animal breeding in particular, where assessing the genetic contribution of influential and admixed individuals is crucial for research and management applications. As such, this method provides a valuable complement to common applied tools to visualize complex population structures and to select individuals for re-sequencing.

Congenital hepatic fibrosis in the Franches-Montagnes horse is associated with the polycystic kidney and hepatic disease 1 (PKHD1) gene.

Congenital hepatic fibrosis has been described as a lethal disease with monogenic autosomal recessive inheritance in the Swiss Franches-Montagnes horse breed. We performed a genome-wide association study with 5 cases and 12 controls and detected an association on chromosome 20. Subsequent homozygosity mapping defined a critical interval of 952 kb harboring 10 annotated genes and loci including the polycystic kidney and hepatic disease 1 (autosomal recessive) gene (PKHD1). PKHD1 represents an excellent functional candidate as variants in this gene were identified in human patients with autosomal recessive polycystic kidney and hepatic disease (ARPKD) as well as several mouse and rat mutants. Whereas most pathogenic PKHD1 variants lead to polycystic defects in kidney and liver, a small subset of the human ARPKD patients have only liver symptoms, similar to our horses with congenital hepatic fibrosis. The PKHD1 gene is one of the largest genes in the genome with multiple alternative transcripts that have not yet been fully characterized. We sequenced the genomes of an affected foal and 46 control horses to establish a comprehensive list of variants in the critical interval. We identified two missense variants in the PKHD1 gene which were strongly, but not perfectly associated with congenital hepatic fibrosis. We speculate that reduced penetrance and/or potential epistatic interactions with hypothetical modifier genes may explain the imperfect association of the detected PKHD1 variants. Our data thus indicate that horses with congenital hepatic fibrosis represent an interesting large animal model for the liver-restricted subtype of human ARPKD.