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Human QTL linkage mapping

Laura Almasy, John Blangero
2008 Genetica  
This was first proposed by Fulker et al for sibling pairs (11) and later expanded by Almasy and Blangero for larger pedigrees (12) .  ... 
doi:10.1007/s10709-008-9305-3 pmid:18668207 pmcid:PMC2761031 fatcat:e2zfpsfbvfakdhonvvjurtj3vu

Software for quantitative trait analysis

Laura Almasy, Diane M Warren
2005 Human Genomics  
This paper provides a brief overview of software currently available for the genetic analysis of quantitative traits in humans. Programs that implement variance components, Markov Chain Monte Carlo (MCMC), Haseman -Elston (H-E) and penetrance model-based linkage analyses are discussed, as are programs for measured genotype association analyses and quantitative trait transmission disequilibrium tests. The software compared includes LINKAGE, FASTLINK, PAP, SOLAR, SEGPATH, ACT, Mx, MERLIN,
more » ... ER, Loki, Mendel, SAGE, QTDT and FBAT. Where possible, the paper provides URLs for acquiring these programs through the internet, details of the platforms for which the software is available and the types of analyses performed.
doi:10.1186/1479-7364-2-3-191 pmid:16197737 pmcid:PMC3525122 fatcat:gut5tyezvzfdxledk7soggt2l4

Stability of Polygenic Scores Across Discovery Genome-Wide Association Studies [article]

Laura M. Schultz, Alison K. Merikangas, Kosha Ruparel, Sebastien Jacquemont, David C. Glahn, Raquel E. Gur, Ran Barzilay, Laura Almasy
2021 bioRxiv   pre-print
Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual patient level when phenotypes are not necessarily already known. Hence, we investigated the stability of PGS in European-American (EUR)- and African-American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort
more » ... and the Adolescent Brain Cognitive Development Study (ABCD) using different discovery GWAS for post-traumatic stress disorder (PTSD), type-2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations > 0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was higher for GWAS that explained more of the trait variance, with height PGS being more stable than PTSD or T2D PGS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the level of the individual patient, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.
doi:10.1101/2021.06.18.449060 fatcat:hwjzgqfr6fg57nie6igqqr7nku

Constrained multivariate association with longitudinal phenotypes

Phillip E. Melton, Juan M. Peralta, Laura Almasy
2016 BMC Proceedings  
The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of
more » ... a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (>0.001) for SBP variability and a known gene-centric kernel (MAP4)-based test under the GAW19 simulation model across 200 replicates. Results: When compared to results using two time points, the constrained method utilizing all 3 time points increased power to detect association. Averaging SBP was equally effective when the variant has a large effect on the phenotype, but less powerful for variants with lower effect sizes. However, averaging SBP was far more effective than either the constrained or unconstrained approaches when using a gene-centric kernel-based test. Conclusion: We determined that this constrained multivariate approach improves genetic signal over the bivariate method. However, this method is still only effective in those variants that explain a moderate to large proportion of the phenotypic variance but is not as effective for gene-centric tests.
doi:10.1186/s12919-016-0051-8 pmid:27980657 pmcid:PMC5133503 fatcat:4vgfkzbqefbk3k4yxt375usknu

Genetic risk, parental history, and suicide attempts in a diverse sample of US adolescents [article]

Ran Barzilay, Elina Visoki, Laura M Schultz, Varun Warrier, Nikolaos P Daskalakis, Laura Almasy
2022 medRxiv   pre-print
AbstractBackgroundAdolescent suicide is a major health problem in the US marked by a recent increase in Black/African American youth suicide trends. While genetic factors partly account for familial transmission of suicidal behavior, it is not clear whether polygenic risk scores of suicide attempt have clinical utility in youth suicide risk classification.ObjectivesTo evaluate the contribution of a polygenic risk score for suicide attempt (PRS-SA) in explaining variance in suicide attempt by
more » ... ly adolescence.MethodsWe studied N=5,214 non-related Black and White youth from the Adolescent Brain Cognitive Development (ABCD) Study (ages 8.9-13.8 years) who were evaluated between 2016 and 2021. Regression models tested associations between PRS-SA and parental history of suicide attempt/death with youth-reported suicide attempt. Covariates included age, sex, and race.ResultsOver three waves of assessments, 182 youth (3.5%) reported a past suicide attempt, with Black youth reporting significantly more suicide attempts than their White counterparts (6.1% vs 2.8%, P<.001). PRS-SA was associated with suicide attempt (odds ratio [OR]=1.3, 95% confidence interval [CI] 1.1-1.5, P=.001). Inclusion of PRS-SA explained 2.7% of the variance in suicide attempts, significantly more than the base model including only age, sex and race, which explained 1.9% of the variance (P=.001). Parental history of suicide attempt/death was also associated with youth suicide attempt (OR=2.9, 95%CI 1.9-4.4, P<.001). Addition of PRS-SA to the model that included parental history significantly increased the variance explained from 3.3% to 4% (P=.002).ConclusionsFindings suggest that PRS-SA may be useful for suicide risk classification in diverse youth.Contribution to the Field StatementAdolescent suicidal behavior is a major health problem, with suicide being the 2nd leading cause of death in youth. Research that improves our understanding regarding drivers of suicide risk in youth can inform youth suicide prevention strategies. Family history of suicide is an established risk factor for youth suicidal behavior. Current methods in psychiatric genetics allow calculation of polygenic risk scores that represent genetic liability to specific conditions. It is not clear whether polygenic risk score of suicide attempt can assist in risk classification, beyond family history. In this work, we show that in a sample of 5,214 youth ages 9-13, of which 3.5% reported past suicide attempt, polygenic score of suicide attempt was associated with youth suicide attempt. This association additively explained variance over and above parental history of suicide attempt/death. Findings make a case for the potential utility of incorporating polygenic risk scores as part of suicide attempt risk classification in youth, and suggest that polygenic scores may reveal genetic liability that is not captured by family history of suicide.
doi:10.1101/2022.06.11.22276280 fatcat:svkjrqkl45habcug7grm7wfaqm

Bivariate association analysis of longitudinal phenotypes in families

Phillip E Melton, Laura A Almasy
2014 BMC Proceedings  
Statistical genetic methods incorporating temporal variation allow for greater understanding of genetic architecture and consistency of biological variation influencing development of complex diseases. This study proposes a bivariate association method jointly testing association of two quantitative phenotypic measures from different time points. Measured genotype association was analyzed for single-nucleotide polymorphisms (SNPs) for systolic blood pressure (SBP) from the first and third
more » ... using 200 simulated Genetic Analysis Workshop 18 (GAW18) replicates. Bivariate association, in which the effect of an SNP on the mean trait values of the two phenotypes is constrained to be equal for both measures and is included as a covariate in the analysis, was compared with a bivariate analysis in which the effect of an SNP was estimated separately for the two measures and univariate association analyses in 9 SNPs that explained greater than 0.001% SBP variance over all 200 GAW18 replicates.The SNP 3_48040283 was significantly associated with SBP in all 200 replicates with the constrained bivariate method providing increased signal over the unconstrained bivariate method. This method improved signal in all 9 SNPs with simulated effects on SBP for nominal significance (p-value <0.05). However, this appears to be determined by the effect size of the SNP on the phenotype. This bivariate association method applied to longitudinal data improves genetic signal for quantitative traits when the effect size of the variant is moderate to large.
doi:10.1186/1753-6561-8-s1-s90 pmid:25519346 pmcid:PMC4143799 fatcat:crpb6f7xjvc2xo3tbfcbj6x4ra

Pedigree and genotype errors in the Framingham Heart Study

Gerry Brush, Laura Almasy
2003 BMC Genetics  
The pedigree and genotype data from the Framingham Heart Study were examined for errors. Errors in 21 of 329 pedigrees were detected with the program PREST, and of these the errors in 16 pedigrees were resolved. Genotyping errors were then detected with SIMWALK2. Five Mendelian errors were found following the pedigree corrections. Double-recombinant errors were more common, with 142 being detected at mistyping probabilities of 0.25 or greater.
doi:10.1186/1471-2156-4-s1-s41 pmid:14975109 pmcid:PMC1866477 fatcat:cpffjzh22ncbxeyabdb4g2rska

Contributions of PTSD polygenic risk and environmental stress to suicidality in preadolescents [article]

Nikolaos P Daskalakis, Laura M Schultz, Elina Visoki, Tyler M Moore, Stirling T Argabright, Nathaniel H Harnett, Grace E DiDomenico, Varun Warrier, Laura Almasy, Ran Barzilay
2021 medRxiv   pre-print
Suicidal ideation and attempts (i.e., suicidality) are complex behaviors driven by environmental stress, genetic susceptibility, and their interaction. Preadolescent suicidality is a major health problem with rising rates, yet its underlying biology is understudied. Here we studied effects of genetic stress susceptibility, estimated by polygenic risk score (PRS) for post-traumatic-stress-disorder (PTSD), on preadolescent suicidality in participants from the Adolescent Brain Cognitive
more » ... (ABCD) Study. We further evaluated PTSD-PRS effects on suicidality in the presence of environmental stressors that are established suicide risk factors. Analyses included both European and African ancestry participants using PRS calculated based on summary statistics from ancestry-specific genome-wide association studies. In European ancestry participants (N=4,619, n=378 suicidal), PTSD-PRS was associated with preadolescent suicidality (odds ratio [OR]=1.12, 95%CI 1-1.25, p=0.038). Results in African ancestry participants (N=1,334, n=130 suicidal) showed a similar direction but were not statistically significant (OR=1.21, 95%CI 0.93-1.57, p=0.153). Sensitivity analyses using non-psychiatric polygenic score for height and using cross-ancestry PTSD-PRS did not reveal any association with suicidality, supporting the specificity of the association of ancestry-specific PTSD-PRS with suicidality. Environmental stressors were robustly associated with suicidality across ancestries with moderate effect size for negative life events and family conflict (OR 1.27-1.6); and with large effect size (OR ~ 4) for sexual-orientation discrimination. When combined with environmental factors, PTSD-PRS showed marginal additive effects in explaining variability in suicidality, with no evidence for G X E interaction. Results support use of cross-phenotype PRS, specifically stress-susceptibility, as a robust genetic marker for suicidality risk early in the lifespan.
doi:10.1101/2021.05.30.21258082 fatcat:i7wffck7wbhllcerbyj2zlinke

Genetic signal maximization using environmental regression

Phillip E Melton, Jack W Kent, Thomas D Dyer, Laura Almasy, John Blangero
2011 BMC Proceedings  
Joint analyses of correlated phenotypes in genetic epidemiology studies are common. However, these analyses primarily focus on genetic correlation between traits and do not take into account environmental correlation. We describe a method that optimizes the genetic signal by accounting for stochastic environmental noise through joint analysis of a discrete trait and a correlated quantitative marker. We conducted bivariate analyses where heritability and the environmental correlation between the
more » ... discrete and quantitative traits were calculated using Genetic Analysis Workshop 17 (GAW17) family data. The resulting inverse value of the environmental correlation between these traits was then used to determine a new b coefficient for each quantitative trait and was constrained in a univariate model. We conducted genetic association tests on 7,087 nonsynonymous SNPs in three GAW17 family replicates for Affected status with the b coefficient fixed for three quantitative phenotypes and compared these to an association model where the b coefficient was allowed to vary. Bivariate environmental correlations were 0.64 (± 0.09) for Q1, 0.798 (± 0.076) for Q2, and −0.169 (± 0.18) for Q4. Heritability of Affected status improved in each univariate model where a constrained b coefficient was used to account for stochastic environmental effects. No genome-wide significant associations were identified for either method but we demonstrated that constraining b for covariates slightly improved the genetic signal for Affected status. This environmental regression approach allows for increased heritability when the b coefficient for a highly correlated quantitative covariate is constrained and increases the genetic signal for the discrete trait.
doi:10.1186/1753-6561-5-s9-s72 pmid:22373104 pmcid:PMC3287912 fatcat:qiv4hoqkhbghbgksqhrnmaqzw4

Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees

Laura Almasy, John Blangero
1998 American Journal of Human Genetics  
et al. 1997c) , as well as multivariate and oligogenic analyses (Schork 1993; Almasy et al. 1997c; Blangero and Almasy 1997; Williams et al. 1997) .  ...  Study and event-related brain potentials in the Collaborative Study on the Genetics of Alcoholism (Almasy et al. 1997a; Porjesz et al. 1997; Begleiter et al., in press).  ... 
doi:10.1086/301844 pmid:9545414 pmcid:PMC1377101 fatcat:yp4nrykxdrhhlhv3uspomezkdq

Genome-wide discovery of maternal effect variants

Jack W Kent, Charles P Peterson, Thomas D Dyer, Laura Almasy, John Blangero
2009 BMC Proceedings  
Many phenotypes may be influenced by the prenatal environment of the mother and/or maternal care, and these maternal effects may have a heritable component. We have implemented in the computer program SOLAR a variance components-based method for detecting indirect effects of maternal genotype on offspring phenotype. Of six phenotypes measured in three generations of the Framingham Heart Study, height showed the strongest evidence (P = 0.02) of maternal effect. We conducted a genome-wide
more » ... ion analysis for height, testing both the direct effect of the focal individual's genotype and the indirect effect of the maternal genotype. Offspring height showed suggestive evidence of association with maternal genotype for two single-nucleotide polymorphisms in the trafficking protein particle complex 9 gene TRAPPC9 (NIBP), which plays a role in neuronal NF-B signalling. This work establishes a methodological framework for identifying genetic variants that may influence the contribution of the maternal environment to offspring phenotypes.
doi:10.1186/1753-6561-3-s7-s19 pmid:20018008 pmcid:PMC2795915 fatcat:dqsjxx4b7fbpbo5a4tlkfktd5i

Bivariate quantitative trait linkage analysis: Pleiotropy versus co-incident linkages

Laura Almasy, Thomas D. Dyer, John Blangero
1997 Genetic Epidemiology  
Power to detect linkage and localization of a major gene were compared in univariate and bivariate variance components linkage analysis of three related quantitative traits in general pedigrees. Although both methods demonstrated adequate power to detect loci of moderate effect, bivariate analysis improved both power and localization for correlated quantitative traits mapping to the same chromosomal region, regardless of whether co-localization was the result of pleiotropy. Additionally, a test
more » ... of pleiotropy versus co-incident linkage was shown to have adequate power and a low error rate.
doi:10.1002/(sici)1098-2272(1997)14:6<953::aid-gepi65>3.0.co;2-k pmid:9433606 fatcat:xu4lafo5yjgcrjivtkq5augjze

The role of phenotype in gene discovery in the whole genome sequencing era

Laura Almasy
2012 Human Genetics  
., Almasy et al. 1997 ) and association (e.g., Saint-Pierre et al. 2011) .  ... 
doi:10.1007/s00439-012-1191-1 pmid:22722752 pmcid:PMC3525519 fatcat:jr4docpnw5ajdiuooipoub6twm

A Kernel of Truth [chapter]

John Blangero, Vincent P. Diego, Thomas D. Dyer, Marcio Almeida, Juan Peralta, Jack W. Kent, Jeff T. Williams, Laura Almasy, Harald H.H. Göring
2013 Advances in Genetics  
Standard polygenic model We start with a standard description of the linear model for a phenotype vector under a VC model, which is a standard modeling approach for human family data (Almasy and Blangero  ...  , 1998; Blangero et al., 2001; Lange, 2002; Almasy and Blangero, 2010) : (1) where y, the phenotype vector of interest, X, a design matrix of covariate effects, and β, a vector of regression coefficients  ... 
doi:10.1016/b978-0-12-407677-8.00001-4 pmid:23419715 pmcid:PMC4019427 fatcat:5dxvlxjex5em5ppxxacxbnmsr4

A variance component method for integrated pathway analysis of gene expression data

Ellen E. Quillen, John Blangero, Laura Almasy
2016 BMC Proceedings  
The application of pathway and gene-set based analyses to high-throughput data is increasingly common and represents an effort to understand underlying biology where single-gene or single-marker analyses have failed. Many such analyses rely on the a priori identification of genes associated with the trait of interest. In contrast, this variance-component-based approach creates a similarity matrix of individuals based on the expression of genes in each pathway. Methods: We compared 16 methods of
more » ... calculating similarity for positive control matrices based on probes for the genes used to model the simulated Genetic Analysis Workshop phenotypes. Results: A simple correlation matrix outperforms the other methods by identifying pathways associated with the simulated phenotypes at nearly twice the rate expected based on the associations of the component transcripts and an approximate false-positive rate of 0.05. Conclusions: This method has a number of additional advantages compared to single-transcript and pathway overrepresentation analyses, including the ability to estimate the proportion of variation explained by each pathway and the logistical advantage of only calculating the distance matrices once for each messenger RNA data set regardless of the number of phenotypes. Additionally, it offers a significant reduction in the multiple testing burden over individual consideration of each probe.
doi:10.1186/s12919-016-0053-6 pmid:27980659 pmcid:PMC5133490 fatcat:psgqkmnvanczpeiqakymnafwcq
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