Health financing in Africa: overview of a dialogue among high level policy makers

Sambo, Luís Gomes; Kirigia, Joses Muthuri; Ki-Zerbo, Georges

doi:10.1186/1753-6561-5-s5-s2

Cited by 91 publications

(113 citation statements)

References 3 publications

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“…The effect size for each SNP is provided by Almasy et al [6]. For each gene, we define its total effect size as:…”

Section: Resultsmentioning

confidence: 99%

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Liu

Huang

et al. 2011

BMC Proc

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Both common variants and rare variants are involved in the etiology of most complex diseases in humans. Developments in sequencing technology have led to the identification of a high density of rare variant single-nucleotide polymorphisms (SNPs) on the genome, each of which affects only at most 1% of the population. Genotypes derived from these SNPs allow one to study the involvement of rare variants in common human disorders. Here, we propose an association screening approach that treats genes as units of analysis. SNPs within a gene are used to create partitions of individuals, and inverse-probability weighting is used to overweight genotypic differences observed on rare variants. Association between a phenotype trait and the constructed partition is then evaluated. We consider three association tests (one-way ANOVA, chi-square test, and the partition retention method) and compare these strategies using the simulated data from the Genetic Analysis Workshop 17. Several genes that contain causal SNPs were identified by the proposed method as top genes.

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“…The effect size for each SNP is provided by Almasy et al [6]. For each gene, we define its total effect size as:…”

Section: Resultsmentioning

confidence: 99%

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Liu

Huang

et al. 2011

BMC Proc

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“…We found that the convergence of the REML failed because no SNP contributed any phenotypic variation in the simulated model [3]. …”

Section: Resultsmentioning

confidence: 99%

Estimating heritability using family and unrelated individuals data

et al. 2011

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For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval [0, 1]. The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data.

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“…The difficulty may come from the challenging underlying simulation model, which involved many rare causal SNPs [6]. BNSL relies on joint and conditional probability tables, for which only limited information gain may be available from rare causal SNPs that affect a common trait.…”

Section: Discussionmentioning

confidence: 99%

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

et al. 2011

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Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.

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Health financing in Africa: overview of a dialogue among high level policy makers

Cited by 91 publications

References 3 publications

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Estimating heritability using family and unrelated individuals data

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

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