The impact of complex informative missingness on the validity of the transmission/disequilibrium test (TDT)

The transmission/disequilibrium test was introduced to test for linkage and association between a marker and a putative disease locus using case-parent triads. Several extensions have been proposed to accommodate incomplete triads. Some strategies assumed that parental genotypes were missing completely at random and some methods allowed informative missingness for parental genotypes. However, the above tests assumed that offspring genotypes were missing completely at random and concluded that the transmission/disequilibrium test remained a valid test by excluding incomplete triads from the analysis. In this article, the conditional distribution of ascertained triads allowing informative missingness for offspring genotypes, as well as their parental genotypes, was derived and several tests under such scenarios were evaluated. In simulations, independent triads from the Genetic Analysis Workshop 15 simulated data (Problem 3) was ascertained. When offspring genotypes were missing informatively, simulation results revealed inflated type I error and/or reduced power for the transmission/disequilibrium test excluding incomplete triads

PedGenie: an analysis approach for genetic association testing in extended pedigrees and genealogies of arbitrary size

BACKGROUND: We present a general approach to perform association analyses in pedigrees of arbitrary size and structure, which also allows for a mixture of pedigree members and independent individuals to be analyzed together, to test genetic markers and qualitative or quantitative traits. Our software, PedGenie, uses Monte Carlo significance testing to provide a valid test for related individuals that can be applied to any test statistic, including transmission disequilibrium statistics. Single locus at a time, composite genotype tests, and haplotype analyses may all be performed. We illustrate the validity and functionality of PedGenie using simulated and real data sets. For the real data set, we evaluated the role of two tagging-single nucleotide polymorphisms (tSNPs) in the DNA repair gene, NBS1, and their association with female breast cancer in 462 cases and 572 controls selected to be BRCA1/2 mutation negative from 139 high-risk Utah breast cancer families. RESULTS: The results from PedGenie were shown to be valid both for accurate p-value calculations and consideration of pedigree structure in the simulated data set. A nominally significant association with breast cancer was observed with the NBS1 tSNP rs709816 for carriage of the rare allele (OR = 1.61, 95% CI = 1.10–2.35, p = 0.019). CONCLUSION: PedGenie is a flexible and valid statistical tool that is intuitively simple to understand, makes efficient use of all the data available from pedigrees without requiring trimming, and is flexible to the types of tests to which it can be applied. Further, our analyses of real data indicate NBS1 may play a role in the genetic etiology of heritable breast cancer

PedGenie: meta genetic association testing in mixed family and case-control designs

<p>Abstract</p> <p>Background-</p> <p>PedGenie software, introduced in 2006, includes genetic association testing of cases and controls that may be independent or related (nuclear families or extended pedigrees) or mixtures thereof using Monte Carlo significance testing. Our aim is to demonstrate that PedGenie, a unique and flexible analysis tool freely available in Genie 2.4 software, is significantly enhanced by incorporating meta statistics for detecting genetic association with disease using data across multiple study groups.</p> <p>Methods-</p> <p>Meta statistics (chi-squared tests, odds ratios, and confidence intervals) were calculated using formal Cochran-Mantel-Haenszel techniques. Simulated data from unrelated individuals and individuals in families were used to illustrate meta tests and their empirically-derived p-values and confidence intervals are accurate, precise, and for independent designs match those provided by standard statistical software.</p> <p>Results-</p> <p>PedGenie yields accurate Monte Carlo p-values for meta analysis of data across multiple studies, based on validation testing using pedigree, nuclear family, and case-control data simulated under both the null and alternative hypotheses of a genotype-phenotype association.</p> <p>Conclusion-</p> <p>PedGenie allows valid combined analysis of data from mixtures of pedigree-based and case-control resources. Added meta capabilities provide new avenues for association analysis, including pedigree resources from large consortia and multi-center studies.</p

Haplotype association analyses in resources of mixed structure using Monte Carlo testing

<p>Abstract</p> <p>Background</p> <p>Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.</p> <p>Results</p> <p>Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size.</p> <p>Conclusions</p> <p>We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.</p

A genome-wide linkage and association scan reveals novel loci for autism

Member of the Autism Genome Project Consortium: Astrid M. VicenteAlthough autism is a highly heritable neurodevelopmental disorder, attempts to identify specific susceptibility genes have thus far met with limited success. Genome-wide association studies using half a million or more markers, particularly those with very large sample sizes achieved through meta-analysis, have shown great success in mapping genes for other complex genetic traits. Consequently, we initiated a linkage and association mapping study using half a million genome-wide single nucleotide polymorphisms (SNPs) in a common set of 1,031 multiplex autism families (1,553 affected offspring). We identified regions of suggestive and significant linkage on chromosomes 6q27 and 20p13, respectively. Initial analysis did not yield genome-wide significant associations; however, genotyping of top hits in additional families revealed an SNP on chromosome 5p15 (between SEMA5A and TAS2R1) that was significantly associated with autism (P = 2 x 10(-7)). We also demonstrated that expression of SEMA5A is reduced in brains from autistic patients, further implicating SEMA5A as an autism susceptibility gene. The linkage regions reported here provide targets for rare variation screening whereas the discovery of a single novel association demonstrates the action of common variants

Fine mapping of susceptibility loci to malaria clinical episodes in a family-based cohort from Senegal

O parasita da malária, P. falciparum, mata na ordem de um milhão de crianças Africanas em cada ano, e esta é uma pequena fracção do número de pessoas infectadas em todo o mundo. A evolução clínica de uma infecção por este parasita depende em certa medida, da constituição genética do indivíduo infectado. O papel dos factores genéticos que regulam a gravidade da infecção da malária tem sido repetidamente demonstrado em humanos e animais. Os estudos de associação são realizados com o objectivo de identificar os genes implicados na causalidade do resultado da infecção. Foi detectado anteriormente, linkage no cromossoma humano 5p15 ao número de ataques de Plasmodium falciparum (PFA) em Dielmo, uma aldeia senegalesa [48]. Posteriormente, e antes deste estudo, um levantamento usando um ensaio "GoldenGate" da Illumina, com cerca de 1.450 SNPs foi realizada na região de Linkage com o fenótipo PFA. A análise foi realizada com três programas estatísticos baseados na família: Merlin, QTDT e FBAT/PBAT. Estes programas identificaram três genes candidatos associados com o fenótipo PFA: três SNPs (rs4867417, rs7714218 e rs11959398), localizados no gene PDZD2, um SNP (rs11134099) no gene ADAMTS16, e outro (rs3777320) localizado no gene SEMA5A. O objectivo deste estudo foi investigar estas associações. Os SNPs das regiões destes genes candidatos foram escolhidos por sequenciação de exões situados na região candidata ou por análise bioinformática utilizando dados do HapMap da população Yoruba. O estudado para genotipagem foi através das análises de pré-design ou “Custom” dos SNPs (Applied Biosystems). Os dados foram incluídos num banco de dados e a verificação dos erros de transmissão mendeliana foi efectuada. As análises estatísticas foram realizadas utilizando dois programas de associação familiar, PBAT e QTDT. Foram utilizados diferentes modelos de transmissão de alelos e foi definido como limite de significância p-value = 10-3. As análises de SNPs dos genes PDZD2 e ADAMTS16 não confirmaram a associação, mas encontrou-se associação significativa com SNPs do gene SEMA5A. Um SNP (rs3777325) foi significativamente associado com o fenótipo PFA usando ambos os programas (p-value= - 6.49x10-4 usando o programa PBAT e p-value = 2.0x10-4 usando o programa QTDT). A análise de haplótipos de dois SNPs adjacentes (rs4541632 e rs1018956), também mostrou uma associação significativa do haplótipo GC (p-value= -6.82x10-5) utilizando o programa PBAT. Este estudo confirma que o locus de susceptibilidade para o fenótipo PFA está localizado no gene SEMA5A. Mais estudos serão necessários para replicar essa associação e identificar o polimorfismo causal.The malaria parasite, P. falciparum, kills on the order of a million African children each year, and this is a small fraction of the number of infected individuals world-wide. The clinical outcome of an infection by this parasite depends to some extent on the genetic makeup of the infected individual. The role of genetic factors that regulate the severity of malaria infection has been repeatedly demonstrated in humans and animals. Association studies are conducted with the aim of identifying the causal genes implicated in the outcome of infection. Linkage was previously detected on human chromosome 5p15 controlling the number of Plasmodium falciparum attacks (PFA) in Dielmo, a Senegalese village [48]. Subsequently, and prior to this present study, a fine mapping study using a "GoldenGate assay” from Illumina, with about 1450 SNPs was performed in this region of linkage with PFA phenotype. Analysis was performed with three statistical family-based programs: Merlin, QTDT, and FBAT/PBAT. These programs identified three candidate genes associated with PFA phenotype: three SNPs (rs4867417, rs7714218, and rs11959398) located in PDZD2, one SNP (rs11134099) in ADAMTS16, and one (rs3777320) in SEMA5A. The aim of this present study was to investigate these associations. Novel SNPs in the candidate regions of these genes were selected either by sequencing exons located in these candidate regions or by bioinformatics analysis using HapMap data from Yoruba population. SNPs were studied using either Pre-design or Custom SNP genotyping assay (Applied Biosystems). Data were included in an Access Database and checked for error of Mendelian transmission. Statistical analyses were performed using two family-based association programs, PBAT and QTDT. We used different models of allele transmission and defined p=10-3 as significance threshold. The analyses did not confirm the association with SNPs of PDZD2 or ADAMTS16, but did find significant association with SNPs of SEMA5A. One SNP (rs3777325) was significantly associated with PFA phenotype using both programs (p-value= -6.49x10-4 using the PBAT program and p-value=2.0x10-4 using the QTDT program). A haplotype analysis of two adjacent SNPs (rs4541632 and rs1018956) also showed a significant association of the haplotype GC (p-value= -6.82x10-5) using the PBAT program. This work confirms that a susceptibility locus to PFA phenotype is located inside SEMA5A. Further studies will be necessary to replicate this association and identify the causal polymorphism