Dataset Information

Comparative analysis of haplotype association mapping algorithms.

ABSTRACT: BACKGROUND: Finding the genetic causes of quantitative traits is a complex and difficult task. Classical methods for mapping quantitative trail loci (QTL) in miceuse an F2 cross between two strains with substantially different phenotype and an interval mapping method to compute confidence intervals at each position in the genome. This process requires significant resources for breeding and genotyping, and the data generated are usually only applicable to one phenotype of interest. Recently, we reported the application of a haplotype association mapping method which utilizes dense genotyping data across a diverse panel of inbred mouse strains and a marker association algorithm that is independent of any specific phenotype. As the availability of genotyping data grows in size and density, analysis of these haplotype association mapping methods should be of increasing value to the statistical genetics community. RESULTS: We describe a detailed comparative analysis of variations on our marker association method. In particular, we describe the use of inferred haplotypes from adjacent SNPs, parametric and nonparametric statistics, and control of multiple testing error. These results show that nonparametric methods are slightly better in the test cases we study, although the choice of test statistic may often be dependent on the specific phenotype and haplotype structure being studied. The use of multi-SNP windows to infer local haplotype structure is critical to the use of a diverse panel of inbred strains for QTL mapping. Finally, because the marginal effect of any single gene in a complex disease is often relatively small, these methods require the use of sensitive methods for controlling family-wise error. We also report our initial application of this method to phenotypes cataloged in the Mouse Phenome Database. CONCLUSION: The use of inbred strains of mice for QTL mapping has many advantages over traditional methods. However, there are also limitations in comparison to the traditional linkage analysis from F2 and RI lines. Application of these methods requires careful consideration of algorithmic choices based on both theoretical and practical factors. Our findings suggest general guidelines, though a complete evaluation of these methods can only be performed as more genetic data in complex diseases becomes available.

SUBMITTER: McClurg P

PROVIDER: S-EPMC1409800 | biostudies-other | 2006

REPOSITORIES: biostudies-other

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Comparative analysis of haplotype association mapping algorithms.

McClurg Phillip P Pletcher Mathew T MT Wiltshire Tim T Su Andrew I AI

BMC bioinformatics 20060209

<h4>Background</h4>Finding the genetic causes of quantitative traits is a complex and difficult task. Classical methods for mapping quantitative trail loci (QTL) in miceuse an F2 cross between two strains with substantially different phenotype and an interval mapping method to compute confidence intervals at each position in the genome. This process requires significant resources for breeding and genotyping, and the data generated are usually only applicable to one phenotype of interest. Recentl ...[more]

PMID: 16466585

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Project description:The structure of casein genes has been fully understood in llamas, whereas in other camelids, this information is still incomplete. In fact, structure and polymorphisms have been identified in three (CSN1S1, ?s1-CN; CSN2, ?-CN; CSN3, ?-CN) out of four casein genes, whereas controversial information is available for the CSN1S2 (?s2-CN) in terms of structure and genetic diversity. Data from the genome analysis, whose assembly is available for feral camel, Bactrian, dromedary, and alpaca, can contribute to a better knowledge. However, a majority of the scaffolds available in GenBank are still unplaced, and the comparative annotation is often inaccurate or lacking.Therefore, the aims of this study are 1) to perform a comparative genome analysis and synthesize the literature data on camelids casein cluster; 2) to analyze the casein variability in two dromedary populations (Sudanese and Nigerian) using polymorphisms at CSN1S1 (c.150G > T), CSN2 (g.2126A > G), and CSN3 (g.1029T > C); and 3) to physically map the casein cluster in alpaca. Exon structures, gene and intergenic distances, large insertion/deletion events, SNPs, and microsatellites were annotated. In all camelids, the CSN1S2 consists of 17 exons, confirming the structure of llama CSN1S2 gene. The comparative analysis of the complete casein cluster (?190kb) shows 12,818 polymorphisms. The most polymorphic gene is the CSN1S1 (99 SNPs in Bactrian vs. 248 in dromedary vs. 626 in alpaca). The less polymorphic is the CSN3 in the Bactrian (22 SNPs) and alpaca (301 SNPs), whereas it is the CSN1S2 in dromedary (79 SNPs). In the two investigated dromedary populations, the allele frequencies for the three markers are slightly different: the allele C at CSN1S1 is very rare in Nigerian (0.054) and Sudanese dromedaries (0.094), whereas the frequency of the allele G at CSN2 is almost inverted. Haplotype analysis evidenced GAC as the most frequent (0.288) and TGC as the rarest (0.005). The analysis of R-banding metaphases hybridized with specific probes mapped the casein genes on chromosome 2q21 in alpaca. These data deepen the information on the structure of the casein cluster in camelids and add knowledge on the cytogenetic map and haplotype variability.

Project description:BackgroundThe genetic etiology of complex diseases in human has been commonly viewed as a complex process involving both genetic and environmental factors functioning in a complicated manner. Quite often the interactions among genetic variants play major roles in determining the susceptibility of an individual to a particular disease. Statistical methods for modeling interactions underlying complex diseases between single genetic variants (e.g. single nucleotide polymorphisms or SNPs) have been extensively studied. Recently, haplotype-based analysis has gained its popularity among genetic association studies. When multiple sequence or haplotype interactions are involved in determining an individual's susceptibility to a disease, it presents daunting challenges in statistical modeling and testing of the interaction effects, largely due to the complicated higher order epistatic complexity.ResultsIn this article, we propose a new strategy in modeling haplotype-haplotype interactions under the penalized logistic regression framework with adaptive L1-penalty. We consider interactions of sequence variants between haplotype blocks. The adaptive L1-penalty allows simultaneous effect estimation and variable selection in a single model. We propose a new parameter estimation method which estimates and selects parameters by the modified Gauss-Seidel method nested within the EM algorithm. Simulation studies show that it has low false positive rate and reasonable power in detecting haplotype interactions. The method is applied to test haplotype interactions involved in mother and offspring genome in a small for gestational age (SGA) neonates data set, and significant interactions between different genomes are detected.ConclusionsAs demonstrated by the simulation studies and real data analysis, the approach developed provides an efficient tool for the modeling and testing of haplotype interactions. The implementation of the method in R codes can be freely downloaded from http://www.stt.msu.edu/~cui/software.html.

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Comparative analysis of haplotype association mapping algorithms.

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Comparative analysis of haplotype association mapping algorithms.

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