The Contribution of Known Familial Cardiovascular Disease Genes to Sudden Cardiac Death in Patients Undergoing Hemodialysis

<b><i>Introduction:</i></b> Patients with chronic kidney disease experience high rates of cardiovascular mortality and morbidity. When kidney disease progresses to the need for dialysis, sudden cardiac death (SCD) accounts for 25–35% of all cardiovascular deaths. The objective was to determine if rare genetic variants known to be associated with cardiovascular death in the general population are associated with SCD in patients undergoing hemodialysis. <b><i>Methods:</i></b> We performed a case-control study comparing 126 (37 African American [AfAn] and 89 European ancestry [EA]) SCD subjects and 107 controls (34 AfAn and 73 EA), matched for age, sex, self-reported race, dialysis duration (&#x3c;2, 2–5 and &#x3e;5 years), and the presence or absence of diabetes mellitus. To target the coding regions of genes previously reported to be associated with 15 inherited cardiac conditions (ICCs), we used the TruSight Cardio Kit (Illumina, San Diego, CA, USA) to capture the genetic regions of interest. In all, the kit targets 572-kb regions that include the protein-coding regions and 40-bp 5′ and 3′ end-flanking regions of 174 genes associated with the 15 ICCs. Using the sequence data, burden tests were conducted to identify genes with an increased number of variants among SCD cases compared to matched controls. <b><i>Results:</i></b> Eleven genes were associated with SCD, but after correction for multiple testing, none of the 174 genes were identified as having more variants in the SCD cases than the matched controls, including previously identified genes. Secondary burden tests grouping variants based on diseases and gene function did not produce statistically significant associations. <b><i>Discussion/Conclusions:</i></b> We found no associations between genes known to be associated with ICCs and SCD in our sample of patients undergoing hemodialysis. This suggests that genetic causes are unlikely to be a major pathogenic factor in SCD in hemodialysis patients, although our sample size limits definitive conclusions.

Whole-exome sequencing in 16,511 individuals reveals a role of the HTRA1 protease and its substrate EGFL8 in brain white matter hyperintensities

White matter hyperintensities (WMH) are among the most common radiological abnormalities in the ageing population and an established risk factor for stroke and dementia. While common variant association studies have revealed multiple genetic loci with an influence on WMH volume, the contribution of rare variants to WMH burden in the general population remains largely unexplored. We conducted a comprehensive analysis of WMH burden in the UK Biobank using publicly available whole-exome sequencing data (N=16,511) and found a splice-site variant in GBE1, encoding 1,4-alpha-glucan branching enzyme 1, to be associated with lower white matter burden on an exome-wide level (c.691+2T>C, beta=-0.74, se=0.13, p=9.7E-9). Applying whole-exome gene-based burden tests, we found damaging missense and loss-of-function variants in HTRA1 to associate with increased WMH volume (p=5.5E-6, FDR=0.04). HTRA1 encodes a secreted serine protease implicated in familial forms of small vessel disease. Domain-specific burden tests revealed that the association with WMH volume was restricted to rare variants in the protease domain (amino acids 204-364; beta=0.79, se=0.14, p=9.4E-8). The frequency of such variants in the UK Biobank population was 1 in 450. WMH volume was brought forward by approximately 11 years in carriers of a rare protease domain variant. A comparison with the effect size of established risk factors for WMH burden revealed that the presence of a rare variant in the HTRA1 protease domain corresponded to a larger effect than meeting the criteria for hypertension (beta=0.26, se=0.02, p=2.9E-59) or being in the upper 99.8% percentile of the distribution of a polygenic risk score based on common genetic variants (beta=0.44, se=0.14, p=0.002). In biochemical experiments, most (6/9) of the identified protease domain variants resulted in a markedly reduced protease activity. We further found EGFL8, which showed suggestive evidence for association with WMH volume (p=1.5E-4, FDR=0.22) in gene burden tests, to be a direct substrate of HTRA1 and to be preferentially expressed in cerebral arterioles and arteries. In a phenome-wide association study (PheWAS) mapping ICD-10 diagnoses to 741 standardized Phecodes, rare variants in the HTRA1 protease domain were associated with multiple neurological and non-neurological conditions including migraine with aura (OR=12.24, 95%CI [2.54-35.25], p=8.3E-5). Collectively, these findings highlight an important role of rare genetic variation and of the HTRA1 protease in determining WMH burden in the general population.

The Post-Human Lyric: Diffractive Vision and the Ethics of Mattering in Adam Dickinson’s Anatomic

The aim of my inquiry is to discuss Adam Dickinson’s revisionist approach to the lyric autobiography as shown in his most recent volume Anatomic (2018a). Informed by an eco-critical sensibility, the biotechnological gaze, and post-humanist notions of subjectivity, this highly experimental conceptual project reveals porous boundaries of the autobiographical self caught up in the entanglement of the mind and matter. Based on burden tests of the poet’s own bodily fluids, Anatomic offers a philosophical speculation on the nature of the human, asking us to go beyond anthropocentric positioning of the subject and to consider ethical alongside onto-epistemological implications of this new direction. The methodology employed in my analyses of Dickinson’s poems derives from the influential notions of agential realism, diffractive vision, and intra-action formulated by Karan Barad – a trained quantum physicist and feminist philosopher working in the field of science and technology. Barad’s theories fuel New Materialist paradigms of thought as they propose the inherent indeterminacy of matter as well as question the established views of identity and the social. The particular focus of my interrogations will be the relationship between diffractive perception and the medical gaze used by the Canadian conceptualist to see himself non-anthropologically and thus to destabilize the perimeters of the autobiographical self.

An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests

Analysis of rare variants in family-based studies remains a challenge. To perform a region/set-based association analysis of rare variants in family-based studies, we propose a general methodological framework that integrates higher criticism, maximum, SKATs, and burden approaches into the family-based association testing (FBAT) framework. Using the haplotype algorithm for FBATs to compute the conditional genotype distribution under the null hypothesis of Mendelian transmissions, virtually any association test statistics can be implemented in our approach and simulation-based or exact p-values can be computed without the need for asymptotic settings. Using simulations, we compare the features of the proposed test statistics in our framework with the existing region-based methodology for family-based studies under various scenarios. The tests of our framework outperform the existing approaches. We provide general guidelines for which scenarios, e.g., sparseness of the signals or local LD structure, which test statistic will have distinct power advantages over the others. We also illustrate our approach in an application to a whole-genome sequencing dataset with 897 asthmatic trios.

Contributions of Rare Gene Variants to Familial and Sporadic FSGS

BackgroundOver the past two decades, the importance of genetic factors in the development of FSGS has become increasingly clear. However, despite many known monogenic causes of FSGS, single gene defects explain only 30% of cases.MethodsTo investigate mutations underlying FSGS, we sequenced 662 whole exomes from individuals with sporadic or familial FSGS. After quality control, we analyzed the exome data from 363 unrelated family units with sporadic or familial FSGS and compared this to data from 363 ancestry-matched controls. We used rare variant burden tests to evaluate known disease-associated genes and potential new genes.ResultsWe validated several FSGS-associated genes that show a marked enrichment of deleterious rare variants among the cases. However, for some genes previously reported as FSGS related, we identified rare variants at similar or higher frequencies in controls. After excluding such genes, 122 of 363 cases (33.6%) had rare variants in known disease-associated genes, but 30 of 363 controls (8.3%) also harbored rare variants that would be classified as “causal” if detected in cases; applying American College of Medical Genetics filtering guidelines (to reduce the rate of false-positive claims that a variant is disease related) yielded rates of 24.2% in cases and 5.5% in controls. Highly ranked new genes include SCAF1, SETD2, and LY9. Network analysis showed that top-ranked new genes were located closer than a random set of genes to known FSGS genes.ConclusionsAlthough our analysis validated many known FSGS-causing genes, we detected a nontrivial number of purported “disease-causing” variants in controls, implying that filtering is inadequate to allow clinical diagnosis and decision making. Genetic diagnosis in patients with FSGS is complicated by the nontrivial rate of variants in known FSGS genes among people without kidney disease.

DOT: Gene-set analysis by combining decorrelated association statistics

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer breast cancer risk.

Efficient genotype compression and analysis of large genetic variation datasets

The economy of human genome sequencing has catalyzed ambitious efforts to interrogate the genomes of large cohorts in search of new insight into the genetic basis of disease. This manuscript introduces Genotype Query Tools (GQT) as a new indexing strategy and toolset that addresses an analytical bottleneck by enabling interactive analyses based on genotypes, phenotypes and sample relationships. Speed improvements are achieved by operating directly on a compressed genotype index without decompression. GQT?s data compression ratios increase favorably with cohort size and relative analysis performance improves in kind. We demonstrate substantial performance improvements over state-of-theart tools using datasets from the 1000 Genomes Project (46 fold), the Exome Aggregation Consortium (443 fold), and simulated datasets of up to 100,000 genomes (218 fold). Furthermore, we show that this indexing strategy facilitates population and statistical genetics measures such as principal component analysis and burden tests. Based on its computational efficiency and by complementing existing toolsets, GQT provides a flexible framework for current and future analyses of massive genome datasets.

Detecting association of rare and common variants by adaptive combination of P-values

SummaryGenome-wide association studies (GWAS) can detect common variants associated with diseases. Next generation sequencing technology has made it possible to detect rare variants. Most of association tests, including burden tests and nonburden tests, mainly target rare variants by upweighting rare variant effects and downweighting common variant effects. But there is increasing evidence that complex diseases are caused by both common and rare variants. In this paper, we extend the ADA method (adaptive combination of P-values; Lin et al., 2014) for rare variants only and propose a RC-ADA method (common and rare variants by adaptive combination of P-values). Our proposed method combines the per-site P-values with the weights based on minor allele frequencies (MAFs). The RC-ADA is robust to directions of effects of causal variants and inclusion of a high proportion of neutral variants. The performance of the RC-ADA method is compared with several other association methods. Extensive simulation studies show that the RC-ADA method is more powerful than other association methods over a wide range of models.

Likelihood ratio and score burden tests for detecting disease-associated rare variants

This paper presents two simple rare variant (RV) burden tests based on the likelihood ratio test (LRT) and score statistics. LRT is one of the commonly used tests in practical data analysis, and we show here that there is no reason to ignore it in testing RV associations. With the Bartlett correction, we have numerically shown that the LRT-based test can have a reliable distribution. Our simulation study indicates that if the non-null variants are as common as the null variants, then the LRT and score statistics have comparable performance to the C-alpha test, and if the former is rarer than the null variants, then they outperform the C-alpha test.