Reconsidering Association Testing Methods Using Single-Variant Test Statistics as Alternatives to Pooling Tests for Sequence Data with Rare Variants

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants

Associations between neurohormonal and inflammatory activation and heart failure in children

Adult heart failure (HF) has been shown to be associated with neuroendocrine and inflammatory activation. We hypothesize that neuroendocrine and inflammatory activation also associate with symptom severity and echocardiographic measurements in pediatric HF. Nineteen children with HF were divided into 3 symptom severity groups. Measurements were made of left ventricular (LV) ejection fraction, LV shortening fraction (LVSF), LV shortening fraction Z score (LVSFz), and LV end-systolic (LVSDz) and diastolic diameter Z scores. Blood levels of N-terminal prohormone brain natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hsCRP), tumor necrosis factor alpha, and soluble tumor necrosis factor receptor II were measured. NT-proBNP and hsCRP were significantly elevated with more severe symptoms (P < or = .003) and discriminated between clinical severity groups (volume under the receiver operating characteristic surface = 0.58 and 0.62, P = .007 and P = .002, respectively). NT-proBNP was negatively associated with LV ejection fraction, LVSF, and LVSFz (P < or = .05) and positively associated with LVSDz (P < .001). High-sensitivity C-reactive protein was negatively associated with LVSF (P = .02) and positively associated with NT-proBNP (P = .03). Tumor necrosis factor alpha was negatively associated with LVSF and LVSFz (P < or = .03) and positively associated with LVSDz and NT-proBNP (P < or = .02). Soluble tumor necrosis factor receptor II was negatively associated with LVSFz (P = .03). Neuroendocrine and inflammatory activation are associated with more severe symptoms and worse cardiac characteristics in pediatric HF. Blood levels of these biomarkers could be used to better assess the severity of HF in children

MAF and within-gene pairwise LD distributions in actual sequence data.

<p>Distributions of MAFs (Panel A) and within-gene pairwise LD (Panel B) for biallelic variants in six candidate genes for dilated cardiomyopathy. Pairwise LD was measured by the correlation coefficient (<i>r</i>) between major/minor alleles for variants within the same gene. These distributions were estimated from 184 Coriell samples of European descent. The vertical dashed line in Panel B indicates <i>r</i> = 0.</p

Simulated power comparison for a mixture of rare and common risk variants.

<p>Monte Carlo estimates of rejection rates for each association testing procedure based on 1,000 samples from a disease model with 50 total risk variants, which represent ∼5% of all variants in the locus in the average population, randomly allocated between rare variants (MAF<0.01; OR = 2), low-frequency variants (0.01≤MAF<0.05; OR = 1.5), and common variants (0.05≤MAF<0.10; OR = 1.2). Estimates are reported by call rate, nominal <i>α</i> level, and sample size (<i>N</i>). Error bars represent exact binomial 95% confidence intervals <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030238#pone.0030238-Leemis1" target="_blank">[39]</a> for the rejection rate. The CMC could not be performed at a call rate of 95% because no individual had complete genotype data in any sample; at a call rate of 99.5%, CMC results with <i>F</i> ddf>4 were available in 15, 564, and 989 samples for <i>N</i> = 500, 1,000, and 2,000, respectively.</p

Toward Genetics-Driven Early Intervention in Dilated Cardiomyopathy: Design and Implementation of the DCM Precision Medicine Study

The cause of idiopathic dilated cardiomyopathy (DCM) is unknown by definition, but its familial subtype is considered to have a genetic component. We hypothesize that most idiopathic DCM, whether familial or nonfamilial, has a genetic basis, in which case a genetics-driven approach to identifying at-risk family members for clinical screening and early intervention could reduce morbidity and mortality. On the basis of this hypothesis, we have launched the National Heart, Lung, and Blood Institute- and National Human Genome Research Institute-funded DCM Precision Medicine Study, which aims to enroll 1300 individuals (600 non-Hispanic African ancestry, 600 non-Hispanic European ancestry, and 100 Hispanic) who meet rigorous clinical criteria for idiopathic DCM along with 2600 of their relatives. Enrolled relatives will undergo clinical cardiovascular screening to identify asymptomatic disease, and all individuals with idiopathic DCM will undergo exome sequencing to identify relevant variants in genes previously implicated in DCM. Results will be returned by genetic counselors 12 to 14 months after enrollment. The data obtained will be used to describe the prevalence of familial DCM among idiopathic DCM cases and the genetic architecture of idiopathic DCM in multiple ethnicity-ancestry groups. We will also conduct a randomized controlled trial to test the effectiveness of , an intervention to aid family communication, for improving uptake of preventive screening and surveillance in at-risk first-degree relatives. We anticipate that this study will demonstrate that idiopathic DCM has a genetic basis and guide best practices for a genetics-driven approach to early intervention in at-risk relatives. URL: http://www.clinicaltrials.gov. Unique identifier: NCT03037632

Analytic power comparisons in a small sample (<i>N</i> = 500).

<p>Analytic locus-wide power at <i>α</i> = 0.05 of the BC-CA (lower bound), collapsing, and summing tests at a locus comprising one neutral and one risk variant as a function of the pairwise correlation coefficient between major/minor alleles (<i>r</i>). The variants had the same MAF = 0.005 (Panel A) or MAF = 0.01 (Panel B), and the relative risk was 3 (Panel A) or 2 (Panel B) for each additional minor allele at the risk variant. Both panels assume penetrance of 0.05 for the major allele homozygote at the risk variant and a balanced case-control sample with <i>N</i> = 500 total subjects.</p

Simulated power comparison for rare risk variants (MAF<0.005; OR = 3).

<p>Monte Carlo estimates of rejection rates for each association testing procedure based on 1,000 samples from a disease model with 50 rare risk variants (MAF<0.005; OR = 3), which represent ∼5% of all variants in the locus in the average population. Estimates are reported by call rate, nominal <i>α</i> level, and sample size (<i>N</i>). Error bars represent exact binomial 95% confidence intervals <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030238#pone.0030238-Leemis1" target="_blank">[39]</a> for the rejection rate. The CMC could not be performed at a call rate of 95% because no individual had complete genotype data in any sample; at a call rate of 99.5%, CMC results with <i>F</i> ddf>4 were available in 22, 596, and 991 samples for <i>N</i> = 500, 1,000, and 2,000, respectively.</p

Simulated power comparison for rare risk variants (MAF<0.01; OR = 2).

<p>Monte Carlo estimates of rejection rates for each association testing procedure based on 1,000 samples from a disease model with 50 rare risk variants (MAF<0.01; OR = 2), which represent ∼5% of all variants in the locus in the average population. Estimates are reported by call rate, nominal <i>α</i> level, and sample size (<i>N</i>). Error bars represent exact binomial 95% confidence intervals <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030238#pone.0030238-Leemis1" target="_blank">[39]</a> for the rejection rate. The CMC could not be performed at a call rate of 95% because no individual had complete genotype data in any sample; at a call rate of 99.5%, CMC results with <i>F</i> ddf>4 were available in 18, 573, and 986 samples for <i>N</i> = 500, 1,000, and 2,000, respectively.</p

Common susceptibility variants examined for association with dilated cardiomyopathy

BACKGROUND: Rare mutations in more than 20 genes have been suggested to cause dilated cardiomyopathy (DCM), but explain only a small percentage of cases, mainly in familial forms. We hypothesized that more common variants may also play a role in increasing genetic susceptibility to DCM, similar to that observed in other common complex disorders. METHODS AND RESULTS: To test this hypothesis, we performed case-control analyses on all DNA polymorphic variation identified in a resequencing study of six candidate DCM genes (CSRP3, LDB3, MYH7, SCN5A, TCAP, and TNNT2) conducted in 289 unrelated white probands with DCM of unknown cause and 188 unrelated white controls. In univariate analyses, we identified associated common variants at LDB3 site 10779, LDB3 site 57877, MYH7 sites 16384 and 17404, and TCAP sites 140 and 1735. Multivariate analyses to examine the joint effects of multiple gene variants confirmed univariate results for MYH7 and TCAP and identified a block of 9 variants in MYH7 that was strongly associated with DCM. CONCLUSIONS: Common variants in genes known to be causative of DCM may play a role in genetic susceptibility to DCM. Our results suggest that examination of common genetic variants may be warranted in future studies of DCM and other Mendelian-like disorders