A Likelihood Ratio Test for Genome‐Wide Association under Genetic Heterogeneity

Most existing association tests for genome‐wide association studies (GWASs) fail to account for genetic heterogeneity. Zhou and Pan proposed a binomial‐mixture‐model‐based association test to account for the possible genetic heterogeneity in case‐control studies. The idea is elegant, however, the proposed test requires an expectation‐maximization (EM)‐type iterative algorithm to identify the penalised maximum likelihood estimates and a permutation method to assess p‐values. The intensive computational burden induced by the EM‐algorithm and the permutation becomes prohibitive for direct applications to GWASs. This paper develops a likelihood ratio test (LRT) for GWASs under genetic heterogeneity based on a more general alternative mixture model. In particular, a closed‐form formula for the LRT statistic is derived to avoid the EM‐type iterative numerical evaluation. Moreover, an explicit asymptotic null distribution is also obtained, which avoids using the permutation to obtain p‐values. Thus, the proposed LRT is easy to implement for GWASs. Furthermore, numerical studies demonstrate that the LRT has power advantages over the commonly used Armitage trend test and other existing association tests under genetic heterogeneity. A breast cancer GWAS dataset is used to illustrate the newly proposed LRT.     

Fine Mapping Functional Sites or Regions from Case-Control Data Using Haplotypes of Multiple Linked SNPs

Previously, we reported an algorithm for scanning a large number of tightly linked single nucleotide polymorphisms (SNPs) for LD mapping of functional sites or regions from a family‐based association design. In the present study, we extend our method to a case‐control design. We first use the expectation maximization (EM) algorithm to estimate haplotype frequencies of multiple linked SNPs, and follow this by constructing a contingency table statistic S for LD analysis, based on the estimated haplotype frequencies. An empirical p‐value is obtained based on the null distribution of the maximum of S (S *) from a large number (e.g., 1,000 or more) of randomized permutations. The proposed algorithm has been implemented in a computer program in which window searching for functional SNP sites can cover any number of loci without limitation, except that of computer storage. Unlike other programs for a case‐control design that always conduct tests at a fix window width, in our program after setting a maximum size of haplotype window width, for a given maximum window width all possible widths of haplotypes are utilized to find the maximum statistic S * for each locus under investigation. The sensitivity of the proposed algorithm has been examined with simulated and real genotyping datasets. Association analyses indicate that our program is powerful enough to detect most, if not all, functional SNPs simulated in the original model or identified in the original report. Moreover, the program is very flexible and can be used in either regional or genome‐wide scanning for association analysis with SNP markers.     

Tests of Association for Quantitative Traits in Nuclear Families Using Principal Components to Correct for Population Stratification

Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to population stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for population stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within the variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of population stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.     

Identification of Association of Common AGGF1 Variants with Susceptibility for Klippel-Trenaunay Syndrome Using the Structure Association Program

Klippel-Trenaunay syndrome (KTS) is a severe congenital disorder characterized by capillary malformations, venous malformations or varicose veins, and hypertrophy of the affected tissues. The angiogenic factor gene AGGF1 was previously identified as a candidate susceptibility gene for KTS, but further genetic studies are needed to firmly establish the genetic relationship between AGGF1 and KTS. We analyzed HapMap data and identified two tagSNPs, rs13155212 and rs7704267 that capture information for all common variants in AGGF1 . The two SNPs were genotyped in 173 Caucasian KTS patients and 477 Caucasian non-KTS controls, and both significantly associated with susceptibility for KTS ( P = 0.004 and 0.013, respectively). Permutation testing also showed a significant empirical P value for the association (empirical P = 0.006 and 0.015, respectively). To control for potential confounding due to population stratification, the population structure for both cases and controls was characterized by genotyping of 38 ancestry-informative markers (AIMs) and the STRUCTURE program. The association between the AGGF1 SNPs and KTS remained significant after multivariate analysis by incorporating the inferred cluster scores as a covariate or after removal of outlier individuals identified by STRUCTURE. These results suggest that common AGGF1 variants confer risk of KTS.     

Designing a multistage,SNP-based,genome screen for common diseases

A genome-wide linkage equilibrium mapping is an emerging strategy to identify risk-modifying genes for common diseases, despite unsettled controversies upon many aspects, including its premises, designs, marker choices and cost benefits. One large-scale attempt in Japan aims to identify disease-associated single nucleotide polymorphisms (SNPs) for five diseases among the Japanese population: Alzheimers disease, gastric cancer, diabetes, hypertension and asthma. Following an initial screening of c.a. 100,000 SNPs on 940 subjects (five diseases × 188 patients) to select about 2,000 SNPs, we compared which subsequent screening design is more appropriate, and an additional one or two screens to further narrow down any disease-associated SNPs within a fixed total volume of 15,040,000 typings (2,000 SNPs × five diseases × 1,504 subjects, comprising 752 cases and 752 controls). We employed a Monte Carlo simulation to evaluate the probability of identifying truly disease-associated SNPs. The results suggest the single additional stage design (i.e., total two-stage design including the initial screening of 100,000 SNPs) was more practicable for the simple reason that the gain in probability is considered insufficient relative to an associated increase in study complexity in the three-stage design.     

Grouping preprocess to accurately extend application of EM algorithm to haplotype inference

Haplotype inference is an indispensable technique in medical science, especially in genome-wide association studies. Although the conventional method of inference using the expectation-maximization (EM) algorithm by Excoffier and Slatkin is one standard approach, as its calculation cost is an exponential function of the maximum number of heterozygous loci, it has not been widely applied. We propose a method of haplotype inference that can empirically accommodate up to several tens of single nucleotide polymorphism loci in a single haplotype block while maintaining criteria that are exactly equivalent to those of the EM algorithm. The idea is to reduce the cost of calculating the EM algorithm by using a haplotype-grouping preprocess exploiting the symmetrical and inclusive relationships of haplotypes based on the Hardy–Weinberg equilibrium. Testing of the proposed method using real data sets revealed that it has a wider range of applications than the EM algorithm.     

Human leukocyte antigen alleles and susceptibility to psoriatic arthritis

Objective

Our purpose was to determine associations between HLA alleles and psoriatic arthritis (PsA).

Methods

678 PsA cases and 688 healthy controls were analyzed in a case–control design. The difference in the proportion of cases and controls with at least 1 copy of HLA alleles were tested for significance using χ² test and Fisher’s exact test. Association analyses of haplotypes inferred by the Expectation–Maximization algorithm were performed. In the family-based association study, data from 283 families were analyzed.

Results

Univariate analysis revealed that cases were more likely to be carriers of HLA-C^∗01, -C^∗02, -C^∗06, -C^∗12, -B^∗27, -B^∗38 and -B^∗57, whereas controls were more likely to be carriers of HLA-C^∗03, -C^∗07, -B^∗07, -B^∗51, -DRB1^∗15 and -DQB1^∗0602. In haplotype analyses, PsA cases were more likely to be carriers of the HLA haplotypes -C^∗01/-B^∗27, -C^∗02/-B^∗27, -C^∗12/-B^∗38, and -C^∗06/-B^∗57, while controls were more likely to be carriers of the haplotypes -C^∗07/-B^∗07 and -C^∗15/-B^∗51. In the family-based association analysis, the HLA alleles -A^∗02, -B^∗27 and -DRB1^∗07 were preferentially transmitted to cases, whereas the alleles -A^∗03, -A^∗28, -B^∗51, -DRB1^∗11 and -DQB1^∗0301 were under transmitted.

Conclusion

This large case–control and family based association study shows that HLA-C^∗12/B^∗38, HLA-B^∗27 and HLA-C^∗06/B^∗57 are haplotypes (alleles) robustly associated with PsA. However, since patients with PsA also have psoriasis it is difficult to determine whether the primary association is with arthritis or psoriasis.     

DNA methylation signatures at endoplasmic reticulum stress genes are associated with adiposity and insulin resistance

A sustained activation of the unfolded protein response and the subsequent endoplasmic reticulum (ER) stress has been involved in the onset and severity of several metabolic diseases. The aim of this study was to analyze the association of DNA methylation signatures at ER stress genes with adiposity traits and related metabolic disorders. An epigenomic analysis within the Methyl Epigenome Network Association (MENA) project was conducted in an adult population (n = 474). DNA methylation status in peripheral white blood cells was analyzed by a microarray approach. KEGG database was used to the characterization and discrimination of genes involved in the “protein processing in endoplasmic reticulum pathway”. Anthropometric measurements and plasma metabolic profiles were analyzed. A total of 15 CpG sites at genes participating in ER pathway were strongly correlated with BMI after adjusted linear regression analyses (p < 0.0001). These included cg08188400 (MAP2K7), cg20541779 (CASP12), cg24776411 (EIF2AK1), cg14190817 (HSPA5), cg21376454 (ERN1), cg06666486 (EIF2AK1), cg03211481 (DNAJC1), cg18357645 (OS9), cg05801879 (MBTPS1), cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg03384128 (EIF2AK4), cg02712587 (EIF2AK4), cg04972384 (SELS), cg02240686 (EIF2AK2). Noteworthy, most of them were implicated in ER stress (p = 2.9E ? 09). However, only methylation levels at cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg05801879 (MBTPS1), and cg03384128 (EIF2AK4) also correlated with total fat mass. Interestingly, significant associations between methylation patterns at cg20964082 (ERO1LB) and cg17300868 (NFE2L2) and insulin and HOMA-IR index were found, whereas cg05801879 (MBTPS1) and cg03384128 (EIF2AK4) were correlated with triglyceride levels. This study suggests associations of methylation signatures at ER stress genes with adiposity and insulin resistance, as revealed by discriminative pathway analyses.