Correction: Gustafson et al., Whole Genome Sequencing Revealed Mutations in Two Independent Genes as the Underlying Cause of Retinal Degeneration in an Ashkenazi Jewish Pedigree. Genes 2017, 8, 210.

Following publication of our article [1], we identified discrepancies between the pedigree shown in Figure 1 and the rest of the text.[...]

Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression.

While genetic variation at chromatin loops is relevant for human disease, the relationships between contact propensity (the probability that loci at loops physically interact), genetics, and gene regulation are unclear. We quantitatively interrogate these relationships by comparing Hi-C and molecular phenotype data across cell types and haplotypes. While chromatin loops consistently form across different cell types, they have subtle quantitative differences in contact frequency that are associated with larger changes in gene expression and H3K27ac. For the vast majority of loci with quantitative differences in contact frequency across haplotypes, the changes in magnitude are smaller than those across cell types; however, the proportional relationships between contact propensity, gene expression, and H3K27ac are consistent. These findings suggest that subtle changes in contact propensity have a biologically meaningful role in gene regulation and could be a mechanism by which regulatory genetic variants in loop anchors mediate effects on expression

Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease.

The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes

iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types.

Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically derived and characterized iPSC lines from 222 ethnically diverse individuals that allows for both familial and association-based genetic studies. iPSCORE lines are pluripotent with high genomic integrity (no or low numbers of somatic copy-number variants) as determined using high-throughput RNA-sequencing and genotyping arrays, respectively. Using iPSCs from a family of individuals, we show that iPSC-derived cardiomyocytes demonstrate gene expression patterns that cluster by genetic background, and can be used to examine variants associated with physiological and disease phenotypes. The iPSCORE collection contains representative individuals for risk and non-risk alleles for 95% of SNPs associated with human phenotypes through genome-wide association studies. Our study demonstrates the utility of iPSCORE for examining how genetic variants influence molecular and physiological traits in iPSCs and derived cell lines

Identification and Functional Impact of Structural Variants and Short Tandem Repeats in the Human Genome

Over the last decade, a substantial amount of work in genetics has been done with the goal of understanding how genetic variation affects human traits and diseases, primarily via genome-wide association studies (GWAS). Until recently, these studies have focused on associations with single nucleotide variants (SNVs), largely because they have traditionally been easier to genotype. However, the genome contains diverse classes of non-SNV variation such as short tandem repeats (STRs) and structural variants (SVs) that have been shown in some cases to affect human traits. The increasing availability of deep whole genome sequencing (WGS) data, has now enabled algorithms to robustly detect high resolution structural variants and STRs, and the potential for deeper understanding of these variants. Here I present two studies that focus on characterizing the extent and functional impact of SVs and STRs in the human genome. First, I present a study in which I built a comprehensive high quality map of SVs and STRs using over 700 deeply sequenced genomes. I also describe a novel method of filtering variants using reproducibility of genotypes within genetically duplicate sample pairs, and use this information to make insights into the quality of diverse classes of variants called using different methods. I then utilize this high quality map of genetic variation to assess the impact of different classes of variation on gene expression, and show that the functional properties of unique classes of genetic variation is associated with their likelihood to affect genes and linkage to complex traits in humans

Identification and Functional Impact of Structural Variants and Short Tandem Repeats in the Human Genome

Over the last decade, a substantial amount of work in genetics has been done with the goal of understanding how genetic variation affects human traits and diseases, primarily via genome-wide association studies (GWAS). Until recently, these studies have focused on associations with single nucleotide variants (SNVs), largely because they have traditionally been easier to genotype. However, the genome contains diverse classes of non-SNV variation such as short tandem repeats (STRs) and structural variants (SVs) that have been shown in some cases to affect human traits. The increasing availability of deep whole genome sequencing (WGS) data, has now enabled algorithms to robustly detect high resolution structural variants and STRs, and the potential for deeper understanding of these variants. Here I present two studies that focus on characterizing the extent and functional impact of SVs and STRs in the human genome. First, I present a study in which I built a comprehensive high quality map of SVs and STRs using over 700 deeply sequenced genomes. I also describe a novel method of filtering variants using reproducibility of genotypes within genetically duplicate sample pairs, and use this information to make insights into the quality of diverse classes of variants called using different methods. I then utilize this high quality map of genetic variation to assess the impact of different classes of variation on gene expression, and show that the functional properties of unique classes of genetic variation is associated with their likelihood to affect genes and linkage to complex traits in humans

Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats.

Structural variants (SVs) and short tandem repeats (STRs) are important sources of genetic diversity but are not routinely analyzed in genetic studies because they are difficult to accurately identify and genotype. Because SVs and STRs range in size and type, it is necessary to apply multiple algorithms that incorporate different types of evidence from sequencing data and employ complex filtering strategies to discover a comprehensive set of high-quality and reproducible variants. Here we assemble a set of 719 deep whole genome sequencing (WGS) samples (mean 42×) from 477 distinct individuals which we use to discover and genotype a wide spectrum of SV and STR variants using five algorithms. We use 177 unique pairs of genetic replicates to identify factors that affect variant call reproducibility and develop a systematic filtering strategy to create of one of the most complete and well characterized maps of SVs and STRs to date