Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Variants associated with autoimmune Type 1 diabetes in Japanese children: implications for age-specific effects of cis-regulatory haplotypes at 17q12-q21

AIMS

The aim of this study was to clarify the significance of previously reported susceptibility variants in the development of autoimmune Type 1 diabetes in non-white children. Tested variants included rs2290400, which has been linked to Type 1 diabetes only in one study on white people. Haplotypes at 17q12-q21 encompassing rs2290400 are known to determine the susceptibility of early-onset asthma by affecting the expression of flanking genes.

METHODS

We genotyped 63 variants in 428 Japanese people with childhood-onset autoimmune Type 1 diabetes and 457 individuals without diabetes. Possible association between variants and age at diabetes onset was examined using age-specific quantitative trait locus analysis and ordered-subset regression analysis.

RESULTS

Ten variants, including rs2290400 in GSDMB, were more frequent among the people with Type 1 diabetes than those without diabetes. Of these, rs689 in INS and rs231775 in CTLA4 yielded particularly high odds ratios of 5.58 (corrected P value 0.001; 95% CI 2.15-14.47) and 1.64 (corrected P value 5.3 × 10^-5 ; 95% CI 1.34-2.01), respectively. Age-specific effects on diabetes susceptibility were suggested for rs2290400; heterozygosity of the risk alleles was associated with relatively early onset of diabetes, and the allele was linked to the phenotype exclusively in the subgroup of age at onset ≤ 5.0 years.

CONCLUSIONS

The results indicate that rs2290400 in GSDMB and polymorphisms in INS and CTLA4 are associated with the risk of Type 1 diabetes in Japanese children. Importantly, cis-regulatory haplotypes at 17q12-q21 encompassing rs2290400 probably determine the risk of autoimmune Type 1 diabetes predominantly in early childhood.

Family-based association test using both common and rare variants and accounting for directions of effects for sequencing data

Current family-based association tests for sequencing data were mainly developed for identifying rare variants associated with a complex disease. As the disease can be influenced by the joint effects of common and rare variants, common variants with modest effects may not be identified by the methods focusing on rare variants. Moreover, variants can have risk, neutral, or protective effects. Association tests that can effectively select groups of common and rare variants that are likely to be causal and consider the directions of effects have become important. We developed the Ordered Subset - Variable Threshold - Pedigree Disequilibrium Test (OVPDT), a combination of three algorithms, for association analysis in family sequencing data. The ordered subset algorithm is used to select a subset of common variants based on their relative risks, calculated using only parental mating types. The variable threshold algorithm is used to search for an optimal allele frequency threshold such that rare variants below the threshold are more likely to be causal. The PDT statistics from both rare and common variants selected by the two algorithms are combined as the OVPDT statistic. A permutation procedure is used in OVPDT to calculate the p-value. We used simulations to demonstrate that OVPDT has the correct type I error rates under different scenarios and compared the power of OVPDT with two other family-based association tests. The results suggested that OVPDT can have more power than the other tests if both common and rare variants have effects on the disease in a region.

Gene-smoking interactions in multiple Rho-GTPase pathway genes in an early-onset coronary artery disease cohort

We performed a gene-smoking interaction analysis using families from an early-onset coronary artery disease cohort (GENECARD). This analysis was focused on validating and expanding results from previous studies implicating single nucleotide polymorphisms (SNPs) on chromosome 3 in smoking-mediated coronary artery disease. We analyzed 430 SNPs on chromosome 3 and identified 16 SNPs that showed a gene-smoking interaction at P < 0.05 using association in the presence of linkage--ordered subset analysis, a method that uses permutations of the data to empirically estimate the strength of the association signal. Seven of the 16 SNPs were in the Rho-GTPase pathway indicating a 1.87-fold enrichment for this pathway. A meta-analysis of gene-smoking interactions in three independent studies revealed that rs9289231 in KALRN had a Fisher's combined P value of 0.0017 for the interaction with smoking. In a gene-based meta-analysis KALRN had a P value of 0.026. Finally, a pathway-based analysis of the association results using WebGestalt revealed several enriched pathways including the regulation of the actin cytoskeleton pathway as defined by the Kyoto Encyclopedia of Genes and Genomes.

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

As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.

TDT-HET: a new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data

BACKGROUND

Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (TDT) to one that accounts for locus heterogeneity.

RESULTS

Our simulations suggest that, for sufficiently large sample size (1000 trios) our method has good power to detect association even the proportion of unlinked trios is high (75%). While the median difference (TDT-HET empirical power - TDT empirical power) is approximately 0 for all MOI, there are parameter settings for which the power difference can be substantial. Our multi-locus simulations suggest that our method has good power to detect association as long as the markers are reasonably well-correlated and the genotype relative risk are larger. Results of both single-locus and multi-locus simulations suggest our method maintains the correct type I error rate.Finally, the TDT-HET statistic shows highly significant p-values for most of the idiopathic scoliosis candidate loci, and for some loci, the estimated proportion of unlinked trios approaches or exceeds 50%, suggesting the presence of locus heterogeneity.

CONCLUSIONS

We have developed an extension of the TDT statistic (TDT-HET) that allows for locus heterogeneity among coded trios. Benefits of our method include: estimates of parameters in the presence of heterogeneity, and reasonable power even when the proportion of linked trios is small. Also, we have extended multi-locus methods to TDT-HET and have demonstrated that the empirical power may be high to detect linkage. Last, given that we obtain PPBs, we conjecture that the TDT-HET may be a useful method for correctly identifying linked trios. We anticipate that researchers will find this property increasingly useful as they apply next-generation sequencing data in family based studies.

Single-marker family-based association analysis conditional on parental information

Family-based designs have been commonly used in association studies. Different family structures such as extended pedigrees and nuclear families, including parent-offspring triads and families with multiple affected siblings (multiplex families), can be ascertained for family-based association analysis. Flexible association tests that can accommodate different family structures have been proposed. The pedigree disequilibrium test (PDT) (Am J Hum Genet 67:146-154, 2000) can use full genotype information from general (possibly extended) pedigrees with one or multiple affected siblings but requires parental genotypes or genotypes of unaffected siblings. On the other hand, the association in the presence of linkage (APL) test (Am J Hum Genet 73:1016-1026, 2003) is restricted to nuclear families with one or more affected siblings but can infer missing parental genotypes properly by accounting for identity-by-descent (IBD) parameters. Both the PDT and APL are powerful association tests in the presence of linkage and can be used as complementary tools for association analysis. This chapter introduces these two tests and compares their properties. Recommendations and notes for performing the tests in practice are provided.

Dissecting the genetic heterogeneity of myopia susceptibility in an Ashkenazi Jewish population using ordered subset analysis

PURPOSE

Despite many years of research, most of the genetic factors contributing to myopia development remain unknown. Genetic studies have pointed to a strong inherited component, but although many candidate regions have been implicated, few genes have been positively identified.

METHODS

We have previously reported 2 genomewide linkage scans in a population of 63 highly aggregated Ashkenazi Jewish families that identified a locus on chromosome 22. Here we used ordered subset analysis (OSA), conditioned on non-parametric linkage to chromosome 22 to detect other chromosomal regions which had evidence of linkage to myopia in subsets of the families, but not the overall sample.

RESULTS

Strong evidence of linkage to a 19-cM linkage interval with a peak OSA nonparametric allele-sharing logarithm-of-odds (LOD) score of 3.14 on 20p12-q11.1 (ΔLOD=2.39, empirical p=0.029) was identified in a subset of 20 families that also exhibited strong evidence of linkage to chromosome 22. One other locus also presented with suggestive LOD scores >2.0 on chromosome 11p14-q14 and one locus on chromosome 6q22-q24 had an OSA LOD score=1.76 (ΔLOD=1.65, empirical p=0.02).

CONCLUSIONS

The chromosome 6 and 20 loci are entirely novel and appear linked in a subset of families whose myopia is known to be linked to chromosome 22. The chromosome 11 locus overlaps with the known Myopia-7 (MYP7, OMIM 609256) locus. Using ordered subset analysis allows us to find additional loci linked to myopia in subsets of families, and underlines the complex genetic heterogeneity of myopia even in highly aggregated families and genetically isolated populations such as the Ashkenazi Jews.

Ordered subset analysis for case-control studies

Genetic heterogeneity, which may manifest on a population level as different frequencies of a specific disease susceptibility allele in different subsets of patients, is a common problem for candidate gene and genome-wide association studies of complex human diseases. The ordered subset analysis (OSA) was originally developed as a method to reduce genetic heterogeneity in the context of family-based linkage studies. Here, we have extended a previously proposed method (OSACC) for applying the OSA methodology to case-control datasets. We have evaluated the type I error and power of different OSACC permutation tests with an extensive simulation study. Case-control datasets were generated under two different models by which continuous clinical or environmental covariates may influence the relationship between susceptibility genotypes and disease risk. Our results demonstrate that OSACC is more powerful under some disease models than the commonly used trend test and a previously proposed joint test of main genetic and gene-environment interaction effects. An additional unique benefit of OSACC is its ability to identify a more informative subset of cases that may be subjected to more detailed molecular analysis, such as DNA sequencing of selected genomic regions to detect functional variants in linkage disequilibrium with the associated polymorphism. The OSACC-identified covariate threshold may also improve the power of an additional dataset to replicate previously reported associations that may only be detectable in a fraction of the original and replication datasets. In summary, we have demonstrated that OSACC is a useful method for improving SNP association signals in genetically heterogeneous datasets.