Power of Variance Component Linkage Analysis—II. Discrete Traits

Williams, Jeff T.; Blangero, John

doi:10.1046/j.1529-8817.2004.00128.x

Cited by 25 publications

(21 citation statements)

References 67 publications

(62 reference statements)

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“…There is relatively little power to calculate heritability for diabetes affection status in this data set, attributable to the age-adjusted prevalence of about 9%. There is considerable loss of power observed for dichotomous versus continuous traits, and this power loss is a function of prevalence [35]. Therefore, the appropriate interpretation is that it appears heritable in this population, but the estimate is imprecise.…”

Section: Discussionmentioning

confidence: 99%

Association of Common Genetic Variants with Diabetes and Metabolic Syndrome Related Traits in the Arizona Insulin Resistance Registry: A Focus on Mexican American Families in the Southwest

et al. 2014

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Background/Aims: The increased occurrence of type 2 diabetes and its clinical correlates is a global public health issue, and there are continued efforts to find its genetic determinant across ethnically diverse populations. The aims of this study were to determine the heritability of diabetes and metabolic syndrome phenotypes in the Arizona Insulin Resistance (AIR) registry and to perform an association analysis of common single nucleotide polymorphisms (SNPs) identified by GWAS with these traits. All study participants were Mexican Americans from the AIR registry. Methods: Metabolic, anthropometric, demographic and medical history information was obtained on the 667 individuals enrolled in the registry. Results: The heritability estimates were moderate to high in magnitude and significant, indicating that the AIR registry is well suited for the identification of genetic factors contributing to diabetes and the metabolic syndrome. From the 30 GWAS genes selected (some genes were represented by multiple SNPs), 20 SNPs exhibited associations with one or more of the diabetes related traits with nominal significance (p ≤ 0.05). In addition, 25 SNPs were nominally significantly associated with one or more of the metabolic phenotypes tested (p ≤ 0.05). Most notably, 5 SNPs from 5 genes [body mass index (BMI), hip circumference: rs3751812/FTO; fasting plasma glucose, hemoglobin A1c: rs4607517/GCK; very-low-density lipoprotein: rs10830963/MTNR1B; BMI: rs13266634/SLC30A8, and total cholesterol, low-density lipoprotein: rs7578597/THADA] were significantly associated with obesity, glycemic, and lipid phenotypes when using the multiple testing significance threshold of 0.0015. Conclusion: These findings extend previous work on Mexican Americans to suggest that metabolic disease is strongly influenced by genetic background in this high-risk population.

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Section: Discussionmentioning

confidence: 99%

Association of Common Genetic Variants with Diabetes and Metabolic Syndrome Related Traits in the Arizona Insulin Resistance Registry: A Focus on Mexican American Families in the Southwest

et al. 2014

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“…In a variance component framework, discrete traits are typically analyzed by use of a liability threshold method [Williams and Blangero, 2004] where computationally intensive integrations of the underlying continuous traits need to be computed for the linkage test. A simulation study is not practical for the purpose of power analysis, because of computational requirements for the numerical integration procedure.…”

Section: Discussionmentioning

confidence: 99%

Estimating the power of variance component linkage analysis in large pedigrees

Chen

Abecasis

2006

Genetic Epidemiology

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Variance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2-5 generations and 2-8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 (1.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with >1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY.

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“…Two-point linkage analysis using 61 108 SNP markers was conducted in Sequential Oligogenic Linkage Analysis Routines (SOLAR) 14 using a probit liability model with ascertainment correction. 15 …”

Section: Methodsmentioning

confidence: 99%

“…Estimates of heritability were performed using a probit liability model with ascertainment correction. 15 Neither age nor sex was a significant correlate of hearing loss, and neither was included as a covariate in the analysis. Initially, each genotype dosage score was applied as a covariate to the liability model for hearing loss, and the likelihood of this model was compared with that of the null model of no association.…”

Section: Methodsmentioning

confidence: 99%

Association ofTMTC2With Human Nonsyndromic Sensorineural Hearing Loss

Runge-Samuelson

Indap

Zhou

et al. 2016

JAMA Otolaryngol Head Neck Surg

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IMPORTANCE Sensorineural hearing loss (SNHL) is commonly caused by conditions that affect cochlear structures or the auditory nerve, and the genes identified as causing SNHL to date only explain a fraction of the overall genetic risk for this debilitating disorder. It is likely that other genes and mutations also cause SNHL. OBJECTIVE To identify a candidate gene that causes bilateral, symmetric, progressive SNHL in a large multigeneration family of Northern European descent. DESIGN, SETTING, AND PARTICIPANTS In this prospective genotype and phenotype study performed from January 1, 2006, through April 1, 2016, a 6-generation family of Northern European descent with 19 individuals having reported early-onset hearing loss suggestive of an autosomal dominant inheritance were studied at a tertiary academic medical center. In addition, 179 unrelated adult individuals with SNHL and 186 adult individuals reporting nondeafness were examined. MAIN OUTCOMES AND MEASURES Sensorineural hearing loss. RESULTS Nine family members (5 women [55.6%]) provided clinical audiometric and medical records that documented hearing loss. The hearing loss is characterized as bilateral, symmetric, progressive SNHL that reached severe to profound loss in childhood. Audiometric configurations demonstrated a characteristic dip at 1000 to 2000 Hz. All affected family members wear hearing aids or have undergone cochlear implantation. Exome sequencing and linkage and association analyses identified a fully penetrant sequence variant (rs35725509) on chromosome 12q21 (logarithm of odds, 3.3) in the TMTC2 gene region that segregates with SNHL in this family. This gene explains the SNHL occurrence in this family. The variant is also associated with SNHL in a cohort of 363 unrelated individuals (179 patients with confirmed SNHL and 184 controls, P = 7 × 10−4). CONCLUSIONS AND RELEVANCE A previously uncharacterized gene, TMTC2, has been identified as a candidate for causing progressive SNHL in humans. This finding identifies a novel locus that causes autosomal dominant SNHL and therefore a more detailed understanding of the genetic basis of SNHL. Because TMTC2 has not been previously reported to regulate auditory function, the discovery reveals a potentially new, uncharacterized mechanism of hearing loss.

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Power of Variance Component Linkage Analysis—II. Discrete Traits

Cited by 25 publications

References 67 publications

Association of Common Genetic Variants with Diabetes and Metabolic Syndrome Related Traits in the Arizona Insulin Resistance Registry: A Focus on Mexican American Families in the Southwest

Association of Common Genetic Variants with Diabetes and Metabolic Syndrome Related Traits in the Arizona Insulin Resistance Registry: A Focus on Mexican American Families in the Southwest

Estimating the power of variance component linkage analysis in large pedigrees

Association ofTMTC2With Human Nonsyndromic Sensorineural Hearing Loss

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