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1.  Comparison of statistical approaches to rare variant analysis for quantitative traits 
BMC Proceedings  2011;5(Suppl 9):S113.
With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants.
doi:10.1186/1753-6561-5-S9-S113
PMCID: PMC3287837  PMID: 22373209
2.  Using linkage analysis of large pedigrees to guide association analyses 
BMC Proceedings  2011;5(Suppl 9):S79.
To date, genome-wide association studies have yielded discoveries of common variants that partly explain familial aggregation of diseases and traits. Researchers are now turning their attention to less common variants because the price of sequencing has dropped drastically. However, because sequencing of the whole genome in large samples is costly, great care must be taken to prioritize which samples and which genomic regions are selected for sequencing. We are interested in identifying genomic regions for deep sequencing using large multiplex families collected as part of earlier linkage studies. We incorporate linkage analysis into our search for Q1-associated alleles. Overall, we found that power was low for both whole-exome and linkage-guided sequencing analysis. By restricting sequencing to regions with high LOD peaks, we found fewer associated single-nucleotide polymorphisms than by using whole-exome sequencing. However, incorporating linkage analysis enabled us to detect more than half of the associated susceptibility loci (52%) that would have been identified by whole-exome sequencing while examining only 2.5% of the exome. This result suggests that incorporating linkage results from large multiplex families might greatly increase the efficiency of sequencing to detect trait-associated alleles in complex disease.
doi:10.1186/1753-6561-5-S9-S79
PMCID: PMC3287919  PMID: 22373287
3.  Incorporating biological knowledge in the search for gene × gene interaction in genome-wide association studies 
BMC Proceedings  2009;3(Suppl 7):S81.
We sought to find significant gene × gene interaction in a genome-wide association analysis of rheumatoid arthritis (RA) by performing pair-wise tests of interaction among collections of single-nucleotide polymorphisms (SNPs) obtained by one of two methods. The first method involved screening the results of the genome-wide association analysis for main effects p-values < 1 × 10-4. The second method used biological databases such as the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to define gene collections that each contained one of four genes with known associations with RA: PTPN22, STAT4, TRAF1, and C5. We used a permutation approach to determine whether any of these SNP sets had empirical enrichment of significant interaction effects. We found that the SNP set obtained by the first method was significantly enriched with significant interaction effects (empirical p = 0.003). Additionally, we found that the "protein complex assembly" collection of genes from the Gene Ontology collection containing the TRAF1 gene was significantly enriched with interaction effects with p-values < 1 × 10-8 (empirical p = 0.012).
PMCID: PMC2795984  PMID: 20018077
4.  Genome-wide association and linkage analysis of quantitative traits: comparison of likelihood-ratio test and conditional score statistic 
BMC Proceedings  2009;3(Suppl 7):S100.
Over the past decade, genetic analysis has shifted from linkage studies, which identify broad regions containing putative trait loci, to genome-wide association studies, which detect the association of a marker with a specific phenotype. Because linkage and association analysis provide complementary information, developing a method to combine these analyses may increase the power to detect a true association. In this paper we compare a linkage score and association score test as well as a newly proposed combination of these two scores with traditional linkage and association methods.
PMCID: PMC2795871  PMID: 20017964
5.  Handling linkage disequilibrium in linkage analysis using dense single-nucleotide polymorphisms 
BMC Proceedings  2007;1(Suppl 1):S161.
The presence of linkage disequilibrium violates the underlying assumption of linkage equilibrium in most traditional multipoint linkage approaches. Studies have shown that such violation leads to bias in qualitative trait linkage analysis when parental genotypes are unavailable. Appropriate handling of marker linkage disequilibrium can avoid such false positive evidence. Using the rheumatoid arthritis simulated data from Genetic Analysis Workshop 15, we examined and compared the following three approaches to handle linkage disequilibrium among dense markers in both qualitative and quantitative trait linkage analyses: a simple algorithm; SNPLINK, methods for marker selection; and MERLIN-LD, a method for modeling linkage disequilibrium by creating marker clusters. In analysis ignoring linkage disequilibrium between markers, we observed LOD score inflation only in the affected sib-pair linkage analysis without parental genotypes; no such inflation was present in the quantitative trait locus linkage analysis with severity as our phenotype with or without parental genotypes. Using methods to model or adjust for linkage disequilibrium, we found a substantial reduction of inflation of LOD score in affected sib-pair linkage analysis. Greater LOD score reduction was observed by decreasing the amount of tolerable linkage disequilibrium among markers selected or marker clusters using MERLIN-LD; the latter approach showed most reduction. SNPLINK performed better with selected markers based on the D' measure of linkage disequilibrium as opposed to the r2 measure and outperformed the simple algorithm. Our findings reiterate the necessity of properly handling dense markers in linkage analysis, especially when parental genotypes are unavailable.
PMCID: PMC2367569  PMID: 18466507
7.  Joint modeling of linkage and association using affected sib-pair data 
BMC Proceedings  2007;1(Suppl 1):S38.
There has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT.
PMCID: PMC2367481  PMID: 18466536

Results 1-7 (7)