The role of parametric linkage methods in complex trait analyses using microsatellites

Many investigators of complexly inherited familial traits bypass classical segregation analysis to perform model-free genome-wide linkage scans. Because model-based or parametric linkage analysis may be the most powerful means to localize genes when a model can be approximated, model-free statistics may result in a loss of power to detect linkage. We performed limited segregation analyses on the electrophysiological measurements that have been collected for the Collaborative Study on the Genetics of Alcoholism. The resulting models are used in whole-genome scans. Four genomic regions provided a model-based LOD > 2 and only 3 of these were detected (p < 0.05) by a model-free approach. We conclude that parametric methods, using even over-simplified models of complex phenotypes, may complement nonparametric methods and decrease false positives

An examination of the genotyping error detection function of SIMWALK2

This investigation was undertaken to assess the sensitivity and specificity of the genotyping error detection function of the computer program SIMWALK2. We chose to examine chromosome 22, which had 7 microsatellite markers, from a single simulated replicate (330 pedigrees with a pattern of missing genotype data similar to the Framingham families). We created genotype errors at five overall frequencies (0.0, 0.025, 0.050, 0.075, and 0.100) and applied SIMWALK2 to each of these five data sets, respectively assuming that the total error rate (specified in the program), was at each of these same five levels. In this data set, up to an assumed error rate of 10%, only 50% of the Mendelian-consistent mistypings were found under any level of true errors. And since as many as 70% of the errors detected were false-positives, blanking suspect genotypes (at any error probability) will result in a reduction of statistical power due to the concomitant blanking of correctly typed alleles. This work supports the conclusion that allowing for genotyping errors within likelihood calculations during statistical analysis may be preferable to choosing an arbitrary cut-off

Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts

The goal of this paper is to review recent research on copy number variations (CNVs) and their association with complex and rare diseases. In the latter part of this paper, we focus on how large biorepositories such as the electronic medical record and genomics (eMERGE) consortium may be best leveraged to systematically mine for potentially pathogenic CNVs, and we end with a discussion of how such variants might be reported back for inclusion in electronic medical records as part of medical history

Analysis of recently identified dyslipidemia alleles reveals two loci that contribute to risk for carotid artery disease

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) affecting high density lipoprotein (HDL) or low density lipoprotein (LDL) cholesterol levels; these SNPs may contribute to the genetic basis of vascular diseases.</p> <p>Results</p> <p>We assessed the impact of 34 SNPs at 23 loci on dyslipidemia, key lipid sub-phenotypes, and severe carotid artery disease (CAAD) in a case-control cohort. The effects of these SNPs on HDL and LDL were consistent with those previously reported, and we provide unbiased estimates of the percent variance in HDL (3.9%) and LDL (3.3%) explained by genetic risk scores. We assessed the effects of these SNPs on HDL subfractions, apolipoprotein A-1, LDL buoyancy, apolipoprotein B, and lipoprotein (a) and found that rs646776 predicts apolipoprotein B level while rs2075650 predicts LDL buoyancy. Finally, we tested the role of these SNPs in conferring risk for ultrasonographically documented CAAD stenosis status. We found that two loci, chromosome 1p13.3 near CELSR2 and PSRC1 which contains rs646776, and 19q13.2 near TOMM40 and APOE which contains rs2075650, harbor risk alleles for CAAD.</p> <p>Conclusion</p> <p>Our analysis of 34 SNPs contributing to dyslipidemia at 23 loci suggests that genetic variation in the 1p13.3 region may increase risk of CAAD by increasing LDL particle number, whereas variation in the 19q13.2 region may increase CAAD risk by promoting formation of smaller, denser LDL particles.</p

Multiple genome-wide analyses of smoking behavior in the Framingham Heart Study

BACKGROUND: Cigarette smoking behavior may have a genetic basis. We assessed evidence for quantitative trait loci (QTLs) affecting the maximum number of cigarettes smoked per day, a trait meant to quantify this behavior, using data collected over 40 years as part of the Framingham Heart Study's original and offspring cohorts. RESULTS: Heritability was estimated to be approximately 21% using variance components (VC) methods (SOLAR), while oligogenic linkage and segregation analysis based on Bayesian Markov chain Monte Carlo (MCMC) methods (LOKI) estimated a mean of two large QTLs contributing approximately 28% and 20%, respectively, to the trait's variance. Genome-wide parametric (FASTLINK) and VC linkage analyses (SOLAR) revealed several LOD scores greater than 1.0, with peak LOD scores using both methods on chromosomes 2, 17, and 20; multi-point MCMC methods followed up on these chromosomes. The most robust linkage results were for a QTL between 65 and 84 cM on chromosome 20 with signals from multiple sex- and age-adjusted analyses including two-point LOD scores of 1.30 (parametric) and 1.07 (heritability = 0.17, VC) at 70.51 cM, a multi-point LOD score of 1.50 (heritability = 0.20, VC) at 84 cM, and an intensity ratio of 12.0 (MCMC) at 65 cM. CONCLUSION: Familial aggregation of the maximum number of cigarettes smoked per day was consistent with a genetic component to this behavior, and oligogenic segregation analyses using MCMC suggested two important QTLs. Linkage signals on chromosome 20 between 65 and 84 cM were seen using multiple analytical methods. No linkage result, however, met genome-wide statistical significance criteria, and the true relationship between these regions and smoking behavior remains unclear

Patients' Choices for Return of Exome Sequencing Results to Relatives in the Event of Their Death

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116009/1/jlme12290.pd

Genetic variation in the SIM1 locus is associated with erectile dysfunction.

Erectile dysfunction affects millions of men worldwide. Twin studies support the role of genetic risk factors underlying erectile dysfunction, but no specific genetic variants have been identified. We conducted a large-scale genome-wide association study of erectile dysfunction in 36,649 men in the multiethnic Kaiser Permanente Northern California Genetic Epidemiology Research in Adult Health and Aging cohort. We also undertook replication analyses in 222,358 men from the UK Biobank. In the discovery cohort, we identified a single locus (rs17185536-T) on chromosome 6 near the single-minded family basic helix-loop-helix transcription factor 1 (SIM1) gene that was significantly associated with the risk of erectile dysfunction (odds ratio = 1.26, P = 3.4 × 10-25). The association replicated in the UK Biobank sample (odds ratio = 1.25, P = 6.8 × 10-14), and the effect is independent of known erectile dysfunction risk factors, including body mass index (BMI). The risk locus resides on the same topologically associating domain as SIM1 and interacts with the SIM1 promoter, and the rs17185536-T risk allele showed differential enhancer activity. SIM1 is part of the leptin-melanocortin system, which has an established role in body weight homeostasis and sexual function. Because the variants associated with erectile dysfunction are not associated with differences in BMI, our findings suggest a mechanism that is specific to sexual function

Is “incidental finding” the best term?: a study of patients’ preferences

There is debate within the genetics community about the optimal term to describe genetic variants unrelated to the test indication, but potentially important for health. Given the lack of consensus and the importance of adopting terminology that promotes effective clinical communication, we sought the opinion of clinical genetics patients