No abstract

The development of high throughput SNP genotyping technologies has improved the genetic dissection of simple and complex traits in many species including cats. The properties of feline 62,897 SNPs Illumina Infinium iSelect DNA array are described using a dataset of over 2,000 feline samples, the most extensive to date, representing 41 cat breeds, a random bred population, and four wild felid species. Accuracy and efficiency of the array’s genotypes and its utility in performing population-based analyses were evaluated. Average marker distance across the array was 37,741 Kb, and across the dataset, only 1% (625) of the markers exhibited poor genotyping and only 0.35% (221) showed Mendelian errors. Marker polymorphism varied across cat breeds and the average minor allele frequency (MAF) of all markers across domestic cats was 0.21. Population structure analysis confirmed a Western to Eastern structural continuum of cat breeds. Genome-wide linkage disequilibrium ranged from 50–1,500 Kb for domestic cats and 750 Kb for European wildcats (Felis silvestris silvestris). Array use in trait association mapping was investigated under different modes of inheritance, selection and population sizes. The efficient array design and cat genotype dataset continues to advance the understanding of cat breeds and will support monogenic health studies across feline breeds and populations.

Cats are ubiquitous companion animals that have been keenly associated with humans for thousands of years and only recently have been intentionally bred for aesthetically appealing coat looks and body forms. The intense selection on single gene phenotypes and the various breeding histories of cat breeds have left different marks on the genomes. Using a previously published 63K Feline SNP array dataset of twenty-six cat breeds, this study utilized a genetic differentiation-based method (di) to empirically identify candidate regions under selection. Defined as three or more overlapping (500Kb) windows of high levels of population differentiation, we identified a total of 205 candidate regions under selection across cat breeds with an average of 6 candidate regions per breed and an average size of 1.5 Mb per candidate region. Using the combined size of candidate regions of each breed, we conservatively estimate that a minimum of ~ 0.1–0.7% of the autosomal genome is potentially under selection in cats. As positive controls and tests of our methodology, we explored the candidate regions of known breed-defining genes (e.g., FGF5 for longhaired breeds) and we were able to detect the genes within candidate regions, each in its corresponding breed. For breed specific exploration of candidate regions under selection, eleven representative candidate regions were found to encompass potential candidate genes for several phenotypes such as brachycephaly of Persian (DLX6, DLX5, DLX2), curled ears of American Curl (MCRIP2, PBX1), and body-form of Siamese and Oriental (ADGRD1), which encourages further molecular investigations. The current assessment of the candidate regions under selection is empiric and detailed analyses are needed to rigorously disentangle effects of demography and population structure from artificial selection.

Fully understanding the genetic factors involved in Autism Spectrum Disorder (ASD) requires whole-genome sequencing (WGS), which theoretically allows the detection of all types of genetic variants. With the aim of generating an unprecedented resource for resolving the genomic architecture underlying ASD, we analyzed genome sequences and phenotypic data from 5,100 individuals with ASD and 6,212 additional parents and siblings (total n=11,312) in the Autism Speaks MSSNG Project, as well as additional individuals from other WGS cohorts. WGS data and autism phenotyping were based on high-quality short-read sequencing (>30x coverage) and clinically accepted diagnostic measures for ASD, respectively. For initial discovery of ASD-associated genes, we used exonic sequence-level variants from MSSNG as well as whole-exome sequencing-based ASD data from SPARK and the Autism Sequencing Consortium (>18,000 trios plus additional cases and controls), identifying 135 ASD-associated protein-coding genes with false discovery rate <10%. Combined with ASD-associated genes curated from the literature, this list was used to guide the interpretation of all other variant types in WGS data from MSSNG and the Simons Simplex Collection (SSC; n=9,205). We identified ASD-associated rare variants in 789/5,100 individuals with ASD from MSSNG (15%) and 421/2,419 from SSC (17%). Considering the genomic architecture, 57% of ASD-associated rare variants were nuclear sequence-level variants, 41% were nuclear structural variants (SVs) (mainly copy number variants, but also including inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Several of the ASD-associated SVs would have been difficult to detect without WGS, including an inversion disrupting SCN2A and a nuclear mitochondrial insertion impacting SYNGAP1. Polygenic risk scores did not differ between children with ASD in multiplex families versus simplex, and rare, damaging recessive events were significantly depleted in multiplex families, collectively suggesting that rare, dominant variation plays a predominant role in multiplex ASD. Our study provides a guidebook for exploring genotype-phenotype correlations in the 15-20% of ASD families who carry ASD-associated rare variants, as well as an entry point to the larger and more diverse studies that will be required to dissect the etiology in the >80% of the ASD population that remains idiopathic. All data resulting from this study are available to the medical genomics research community in an open but protected manner.