Nicole M. Roslin

Usher syndrome, the most common form of inherited deaf-blindness, is unlike many other forms of syndromic hereditary hearing loss in that the extra aural clinical manifestations are also detrimental to communication. Usher syndrome patients with early onset deafness also experience vision loss due to progressive retinitis pigmentosa that can lead to legal blindness in their third or fourth decade. Using a multi-omic approach, we identified three novel pathogenic variants in two Usher syndrome genes (USH2A and ADGRV1) in cases initially referred for isolated vision or hearing loss. In a multiplex hearing loss family, two affected sisters, the product of a second cousin union, are homozygous for a novel nonsense pathogenic variant in ADGRV1 (c.17062C > T, p.Arg5688*), predicted to create a premature stop codon near the N-terminus of ADGRV1. Ophthalmological examination of the sisters confirmed typical retinitis pigmentosa and prompted a corrected Usher syndrome diagnosis. In an unrelated clinical case, a child with hearing loss tested positive for two novel USH2A splicing variants (c.5777-1G > A, p. Glu1926_Ala1952del and c.10388-2A > G, p.Asp3463Alafs*6) and RNA studies confirmed that both pathogenic variants cause splicing errors. Interestingly, these same USH2A variants are also identified in another family with vision loss where subsequent clinical follow-up confirmed pre-existing hearing loss since early childhood, eventually resulting in a reassigned diagnosis of Usher syndrome. These findings provide empirical evidence to increase Usher syndrome surveillance of at-risk children. Given that novel antisense oligonucleotide therapies have been shown to rescue retinal degeneration caused by USH2A splicing pathogenic variants, these solved USH2A patients may now be eligible to be enrolled in therapeutic trials.

Citation: For any use of the 1000 Genomes Project data, please use the citation as noted here: http://www.1000genomes.org/faq/how-do-i-cite-1000-genomes-project . To cite this report or the lists described here, please use the following: Roslin NM, Li W, Paterson AD, Strug LJ. Quality control analysis of the 1000 Genomes Project Omni2.5 genotypes (Abstract/Program #576/F). Presented at the 66 ^th Annual Meeting of The American Society of Human Genetics, October 18-22, 2016, Vancouver, Canada. Data Summary: Chips : IlluminaHumanOmni2.5-4v1_B and Illumina HumanOmni25M-8v1-1_B Initial number of SNPs : 2 458 861 Initial number of samples : 2318 Number of SNPs passing QC : 1 989 184 (80.9%) Number of samples passing QC : 2318 (100%) Number of quasi-unrelated samples with consistent ethnicity and well inferred sex : 1736 The 1000 Genomes Project genotype 2318 individuals (48.1% male) from 19 populations in 5 continental groups on the Illumina Omni2.5 platform. The data are publicly available, and will prove a valuable resource to obtain ethnic-specific allele frequencies, as well as exploring population histories through principal components analysis (PCA), estimation of inbreeding coefficients, and admixture analysis. As in any study, the data should be cleaned prior to analysis, to remove individuals or markers of questionable quality. Furthermore, a thorough understanding of the relationships between individuals must be established. Here we report our findings after comprehensive examination of the data for quality control. The basic quality of the genotypes was assessed using standard procedures. KING version 1.4 was used to confirm the relationships in the provided pedigrees, and also to detect undeclared relationships. PCA was used to examine the similarities and differences between individuals among and between population groups. In general, the data was found to be of high quality. No samples were removed due to low call rate (rd degree relatives was extracted and used in PCA. These individuals clustered in a pattern that is consistent with other published reports of global populations. We identified 4 individuals whose genotypes clustered more closely with a different geographic region than the one in the provided data. Although the genotype data is of high quality, errors exist in the publicly available dataset that require attention prior to using the genotypes. PLINK-format files including SNPs with good quality metrics and revised pedigree structures is available at http://tcag.ca . Files with distantly related or unrelated individuals, with sex inference consistent with provided gender, and with PCA consistent with continental group are also available.

We aim to identify rare variants that have large effects on trait variance using a cost-efficient strategy. We use an oligogenic segregation analysis as a prioritizing tool for whole-exome sequencing studies to identify families more likely to harbor rare variants, by estimating the mean number of quantitative trait loci (QTLs) in each family. We hypothesize that families with additional QTLs, relative to the other families, are more likely to segregate functional rare variants. We test the association of rare variants with the traits only in regions where at least modest evidence of linkage with the trait is observed, thereby reducing the number of tests performed. We found that family 7 harbored an estimated two, one, and zero additional QTLs for traits Q1, Q2, and Q4, respectively. Two rare variants (C4S4935 and C6S2981) segregating in family 7 were associated with Q1 and explained a substantial proportion of the observed linkage signal. These rare variants have 31 and 22 carriers, respectively, in the 128-member family and entered through a single but different founder. For Q2, we found one rare variant unique to family 7 that showed small effect and weak evidence of association; this was a false positive. These results are a proof of principle that prioritizing the sequencing of carefully selected extended families is a simple and cost-efficient design strategy for sequencing studies aiming at identifying functional rare variants.

Multivariate linear growth curves were used to model high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), and systolic blood pressure (SBP) measured during four exams from 1659 independent individuals from the Framingham Heart Study. The slopes and intercepts from each of two phenotype models were tested for association with 348,053 autosomal single-nucleotide polymorphisms from the Affymetrix Gene Chip 500 k set. Three regions were associated with LDL intercept, TG slope, and SBP intercept (p < 1.44 × 10^-7). We observed results consistent with previously reported associations between rs599839, on chromosome 1p13, and LDL. We note that the association is significant with LDL intercept but not slope. Markers on chromosome 17q25 were associated with TG slope, and a single-nucleotide polymorphism on chromosome 7p11 was associated with SBP intercept. Growth curve models can be used to gain more insight on the relationships between SNPs and traits than traditional association analysis when longitudinal data has been collected. The power to detect association with changes over time may be limited if the subjects are not followed over a long enough time period.

We propose the use of latent growth curve model to assess the influence of genetic, environmental, demographic, and lifestyle factors on multiple phenotypes related to coronary heart disease. We model four quantitative traits (systolic blood pressure, high-density lipoprotein, low-density lipoprotein, and triglycerides) simultaneously in a multivariate framework that allows us to study their change over time, assess individual variation, and investigate cross-phenotype relationships. Environmental, demographic, and lifestyle covariates are included at different levels of the model as time-varying or time-invariant, as appropriate. To investigate the change over time attributed to genetic factors, we use candidate markers that have previously been shown to be associated with the quantitative traits. We illustrate our approach using independent observations from the offspring cohort of the Framingham Heart Study data.

Introduction: Systemic lupus erythematosus is a genetically complex disease. Currently, the precise allelic polymorphisms associated with this condition remain largely unidentified. In part this reflects the fact that multiple genes, each having a relatively minor effect, act in concert to produce disease. Given this complexity, analysis of subclinical phenotypes may aid in the identification of susceptibility alleles. Here, we used flow cytometry to investigate whether some of the immune abnormalities that are seen in the peripheral blood lymphocyte population of lupus patients are seen in their first-degree relatives. Methods: Peripheral blood mononuclear cells were isolated from the subjects, stained with fluorochrome-conjugated monoclonal antibodies to identify various cellular subsets, and analyzed by flow cytometry. Results: We found reduced proportions of natural killer (NK)T cells among 367 first-degree relatives of lupus patients as compared with 102 control individuals. There were also slightly increased proportions of memory B and T cells, suggesting increased chronic low-grade activation of the immune system in first-degree relatives. However, only the deficiency of NKT cells was associated with a positive anti-nuclear antibody test and clinical autoimmune disease in family members. There was a significant association between mean parental, sibling, and proband values for the proportion of NKT cells, suggesting that this is a heritable trait. Conclusions: The findings suggest that analysis of cellular phenotypes may enhance the ability to detect subclinical lupus and that genetically determined altered immunoregulation by NKT cells predisposes first-degree relatives of lupus patients to the development of autoimmunity.