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An important aspect of disease gene mapping is replication, that is, a putative finding in one group of individuals is confirmed in another set of individuals. As it can happen by chance that individuals share an estimated disease position, we developed a statistical approach to determine the p‐value for multiple individuals or families to share a possibly small number of candidate susceptibility variants. Here, we focus on candidate variants for dominant traits that have been obtained by our previously developed heterozygosity analysis, and we are testing the sharing of candidate variants obtained for different individuals. Our approach allows for multiple pathogenic variants in a gene to contribute to disease, and for estimated disease variant positions to be imprecise. Statistically, the method developed here falls into the category of equivalence testing, where the classical null and alternative hypotheses of homogeneity and heterogeneity are reversed. The null hypothesis situation is created by permuting genomic locations of variants for one individual after another. We applied our methodology to the ALSPAC data set of 1,927 whole‐genome sequenced individuals, where some individuals carry a pathogenic variant for the BRCA1 gene, but no two individuals carry the same variant. Our shared genomic segment analysis found significant evidence for BRCA1 pathogenic variants within ±5 kb of a given DNA variant.

Disrupted-in-schizophrenia 1 (DISC1) was reported to be associated with schizophrenia. In a previous study, we found significant association with schizophrenia patients with deficient sustained attention assessed by continuous performance test (CPT). This study aimed to identify risk polymorphisms in this specific neurocognitive subgroup and investigate the expression of different isoforms of DISC1. A total of 83 genetic variants were identified through direct sequencing in 50 controls and 100 schizophrenia patients. Fourteen variants were genotyped in 600 controls and 912 patients. Patients were subgrouped by familial loading (multiplex or simplex) and performance on CPT. The frequency of AA genotype of rs11122324 at the 3′-UTR of Es and Esv1 isoforms and of rs2793091 at intron 4 were significantly higher in multiplex schizophrenia patients than those in controls (corrected p < 0.05). In further subgrouping, the frequency of AA genotype of the two SNPs were significantly higher in multiplex schizophrenia patients with deficient sustained attention than those in controls (corrected p < 0.005). The mRNA expression levels of two extra-short isoforms (Es and Esv1) in the EBV-transformed lymphocytes of schizophrenia were significantly higher than those of controls. Luciferase reporter assays demonstrated that the A-allele of rs11122324 significantly upregulated DISC1 extra-short isoforms transcription compared with the G-allele. We found two SNPs (rs11122324 and rs2793091) of DISC1 may be specifically associated with multiplex schizophrenia patients with deficient sustained attention. The SNP rs11122324 may be a risk polymorphism, which may have functional influence on the transcription of Es and Esv1 through increasing their expression.

A region-specific method, NTR (non-threshold rare) variant detection method, was developed-it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

Background Schizophrenia is a highly heritable disease with a polygenic mode of inheritance. Many studies have contributed to our understanding of the genetic underpinnings of schizophrenia, but little is known about how interactions among genes affect the risk of schizophrenia. This study aimed to assess the associations and interactions among genes that confer vulnerability to schizophrenia and to examine the moderating effect of neuropsychological impairment. Methods We analyzed 99 SNPs from 10 candidate genes in 1,512 subject samples. The permutation-based single-locus, multi-locus association tests, and a gene-based multifactorial dimension reduction procedure were used to examine genetic associations and interactions to schizophrenia. Results We found that no single SNP was significantly associated with schizophrenia. However, a risk haplotype, namely A-T-C of the SNP triplet rsDAO7-rsDAO8-rsDAO13 of the DAO gene, was strongly associated with schizophrenia. Interaction analyses identified multiple between-gene and within-gene interactions. Between-gene interactions including DAO*DISC1, DAO*NRG1 and DAO*RASD2 and a within-gene interaction for CACNG2 were found among schizophrenia subjects with severe sustained attention deficits, suggesting a modifying effect of impaired neuropsychological functioning. Other interactions such as the within-gene interaction of DAO and the between-gene interaction of DAO and PTK2B were consistently identified regardless of stratification by neuropsychological dysfunction. Importantly, except for the within-gene interaction of CACNG2, all of the identified risk haplotypes and interactions involved SNPs from DAO. Conclusions These results suggest that DAO, which is involved in the N-methyl-d-aspartate receptor regulation, signaling and glutamate metabolism, is the master gene of the genetic associations and interactions underlying schizophrenia. Besides, the interaction between DAO and RASD2 has provided an insight in integrating the glutamate and dopamine hypotheses of schizophrenia.

Angiotensin-converting enzyme (ACE) has been implicated in multiple biological system, particularly cardiovascular diseases. However, findings associating ACE insertion/deletion polymorphism with hypertension or other related traits are inconsistent. Therefore, in a two-stage approach, we aimed to fine-map ACE in order to narrow-down the function-specific locations. We genotyped 31 single nucleotide polymorphisms (SNPs) of ACE from 1168 individuals from 305 young-onset (age ≤40) hypertension pedigrees, and found four linkage disequilibrium (LD) blocks. A tag-SNP, rs1800764 on LD block 2, upstream of and near the ACE promoter, was significantly associated with young-onset hypertension (p = 0.04). Tag-SNPs on all LD blocks were significantly associated with ACE activity (p-value: 10(-16) to

Complex diseases are typically caused by combinations of molecular disturbances that vary widely among different patients. Endophenotypes, a combination of genetic factors associated with a disease, offer a simplified approach to dissect complex trait by reducing genetic heterogeneity. Because molecular dissimilarities often exist between patients with indistinguishable disease symptoms, these unique molecular features may reflect pathogenic heterogeneity. To detect molecular dissimilarities among patients and reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytical procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI), a measure of the similarity of two clusterings of a data set. To evaluate this procedure, we compared it with a commonly used method, principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression dataset and genotype information was conducted to examine the performance of our procedure and PCA-K. The results showed that NMF mostly outperformed PCA-K. Additionally, we applied our endophenotype-identification analytical procedure to a publicly available dataset containing data derived from patients with late-onset Alzheimer's disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes (MS) for patients in the LOAD dataset: MS1 (n1=80), MS2 (n2=73), and MS3 (n3=23). ARI was then used to determine the most representative transcripts for each metagene; 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts were identified as the endophenotypes. Our results showed that 14, 38, 0, and 28 candidate susceptibility genes listed in AlzGene database were found by all patients, MS1, MS2, and MS3, respectively. Moreover, we found that MS2 might be a normal-like subtype. Our proposed procedure provides an alternative approach to investigate the pathogenic mechanism of disease and better understand the relationship between phenotype and genotype.

BACKGROUND: Graves' disease (GD) is the leading cause of hyperthyroidism and thyroid eye disease inherited as a complex trait. Although geoepidemiology studies showed relatively higher prevalence of GD in Asians than in Caucasians, previous genetic studies were contradictory concerning whether and/or which human leukocyte antigen (HLA) alleles are associated with GD in Asians. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a case-control association study (499 unrelated GD cases and 504 controls) and a replication in an independent family sample (419 GD individuals and their 282 relatives in 165 families). To minimize genetic and phenotypic heterogeneity, we included only ethnic Chinese Han population in Taiwan and excluded subjects with hypothyroidism. We performed direct and comprehensive genotyping of six classical HLA loci (HLA-A, -B, -C, -DPB1, -DQB1 and -DRB1) to 4-digit resolution. Combining the data of two sample populations, we found that B*46:01 (odds ratio under dominant model [OR] = 1.33, Bonferroni corrected combined P [P(Bc)] = 1.17 x 10⁻²), DPB1*05:01 (OR = 2.34, P(Bc) = 2.58 x 10⁻¹⁰), DQB1*03:02 (OR = 0.62, P(Bc) = 1.97 x 10⁻²), DRB1*15:01 (OR = 1.68, P(Bc) = 1.22 x 10⁻²) and DRB1*16:02 (OR = 2.63, P(Bc) = 1.46 x 10⁻⁵) were associated with GD. HLA-DPB1*05:01 is the major gene of GD in our population and singly accounts for 48.4% of population-attributable risk. CONCLUSIONS/SIGNIFICANCE: These GD-associated alleles we identified in ethnic Chinese Hans, and those identified in other Asian studies, are totally distinct from the known associated alleles in Caucasians. Identification of population-specific association alleles is the critical first step for individualized medicine. Furthermore, comparison between different susceptibility/protective alleles across populations could facilitate generation of novel hypothesis about GD pathophysiology and indicate a new direction for future investigation.

Background Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive. Results In this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals. Conclusions This study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.

Background In many studies, researchers may recruit samples consisting of independent trios and unrelated individuals. However, most of the currently available haplotype inference methods do not cope well with these kinds of mixed data sets. Methods We propose a general and simple methodology using a mixture of weighted multinomial (MIXMUL) approach that combines separate haplotype information from unrelated individuals and independent trios for haplotype inference to the individual level. Results The new MIXMUL procedure improves over existing methods in that it can accurately estimate haplotype frequencies from mixed data sets and output probable haplotype pairs in optimized reconstruction outcomes for all subjects that have contributed to estimation. Simulation results showed that this new MIXMUL procedure competes well with the EM-based method, i.e. FAMHAP, under a few assumed scenarios. Conclusion The results showed that MIXMUL can provide accurate estimates similar to those haplotype frequencies obtained from FAMHAP and output the probable haplotype pairs in the most optimal reconstruction outcome for all subjects that have contributed to estimation. If available data consist of combinations of unrelated individuals and independent trios, the MIXMUL procedure can be used to estimate the haplotype frequencies accurately and output the most likely reconstructed haplotype pairs of each subject in the estimation.

Young-onset hypertension has a stronger genetic component than late-onset counterpart; thus, the identification of genes related to its susceptibility is a critical issue for the prevention and management of this disease. We carried out a two-stage association scan to map young-onset hypertension susceptibility genes. The first-stage analysis, a genome-wide association study, analyzed 175 matched case-control pairs; the second-stage analysis, a confirmatory association study, verified the results at the first stage based on a total of 1,008 patients and 1,008 controls. Single-locus association tests, multilocus association tests and pair-wise gene-gene interaction tests were performed to identify young-onset hypertension susceptibility genes. After considering stringent adjustments of multiple testing, gene annotation and single-nucleotide polymorphism (SNP) quality, four SNPs from two SNP triplets with strong association signals (-log(10)(p)>7) and 13 SNPs from 8 interactive SNP pairs with strong interactive signals (-log(10)(p)>8) were carefully re-examined. The confirmatory study verified the association for a SNP quartet 219 kb and 495 kb downstream of LOC344371 (a hypothetical gene) and RASGRP3 on chromosome 2p22.3, respectively. The latter has been implicated in the abnormal vascular responsiveness to endothelin-1 and angiotensin II in diabetic-hypertensive rats. Intrinsic synergy involving IMPG1 on chromosome 6q14.2-q15 was also verified. IMPG1 encodes interphotoreceptor matrix proteoglycan 1 which has cation binding capacity. The genes are novel hypertension targets identified in this first genome-wide hypertension association study of the Han Chinese population.

Background Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region. Results An advantage of the LSR method relative to procedures based on genotypic data is that only p-value data are needed and hence can be applied extensively to different study designs. In this study the proposed LSR method was compared with commonly used methods such as Bonferroni's method and FDR controlling method. We found that while all methods provide good control over false positive rate, LSR has much better power and false discovery rate. In the authentic analysis on psoriasis and asthma disease data, the LSR method successfully identified important candidate regions and replicated the results of previous association studies. Conclusion The proposed LSR method provides an efficient exploratory tool for the analysis of sequences of dense genetic markers. Our results show that the LSR method has better power and lower false discovery rate comparing with the locus-specific multiple tests.

Background Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, 'PDA' (Pooled DNA Analyzer), to analyze pooled DNA data. Results We develop the software, PDA, for the analysis of pooled-DNA data. PDA is originally implemented with the MATLAB® language, but it can also be executed on a Windows system without installing the MATLAB®. PDA provides estimates of the coefficient of preferential amplification and allele frequency. PDA considers an extended single-point association test, which can compare allele frequencies between two DNA pools constructed under different experimental conditions. Moreover, PDA also provides novel chromosome-wide multipoint association tests based on p-value combinations and a sliding-window concept. This new multipoint testing procedure overcomes a computational bottleneck of conventional haplotype-oriented multipoint methods in DNA pooling analyses and can handle data sets having a large pool size and/or large numbers of polymorphic markers. All of the PDA functions are illustrated in the four bona fide examples. Conclusion PDA is simple to operate and does not require that users have a strong statistical background. The software is available at http://www.ibms.sinica.edu.tw/%7Ecsjfann/first%20flow/pda.htm.

Hypertension is one of the most important risk factors for cardiovascular diseases. Despite extensive research examining the causes of blood pressure variation, a significant proportion of blood pressure variation is yet to be explained. Studies of families and twins suggest that 20-40% of blood pressure variation can be attributed to genetic factors.1 Evidence shows that the genetic contribution is even greater for young onset hypertension.2 We feel that genetic approaches focusing on young onset hypertension will provide new insight into the pathogenesis of hypertension. In our previous report, the affected sib pairs (25 independent, affected sib pairs) method showed positive signs of linkage for markers of the atrial natriuretic peptide gene ( NPPA ) (D1S1612, p=0.0162), angiotensinogen gene ( AGT ) (D1S547, p=0.0263), lipoprotein lipase gene ( LPL ) (D8S1145, p=0.0284), and angiotensin converting enzyme gene (DCP1) (D17S2193, p=0.0256),3 indicating that multiple pathogenic pathways may be involved in the aetiology of young onset hypertension. Owing to this aetiological complexity, in the current study we focus on high resolution mapping of AGT (located on 1q42-43) and DCP1 (located on 17q23), genes of the renin angiotensin system (RAS). Renin catalyses the first step of the activation pathway of angiotensinogen to angiotensin I, which is then cleaved to angiotensin II by angiotensin I converting enzyme. This cascade can lead to aldosterone release, vasoconstriction, and increased blood pressure. Although the RAS has been extensively studied, it remains unclear how and to what extent RAS gene variants contribute to the blood pressure variations in various human populations. We have recruited 59 nuclear families (a total of 214 subjects) from a hypertension clinic at Taipei …

Chronic Helicobacter pylori-stimulated immune reactions determine the pathogenesis of gastric mucosa-associated lymphoid tissue (MALT) lymphoma. We aimed to explore the genetic predisposition to this lymphoma and its clinical implication. A total of 68 patients and 140 unrelated controls were genotyped for 84 single-nucleotide polymorphisms in genes encoding cytokines, chemokines and related receptors that play important roles in T cell-mediated gastrointestinal immunity. Five genotypes in IL-22, namely CC at rs1179246, CC at rs2227485, AA at rs4913428, AA at rs1026788 and TT at rs7314777, were associated with disease susceptibility. The former four genotypes resided in the same linkage disequilibrium block (r(2)=0.99) that conferred an approximately threefold higher risk. In vitro experiments demonstrated that co-culturing peripheral mononuclear cells or CD4(+) T cells with H. pylori stimulated the secretion of interleukin-22 (IL-22), and that IL-22 induced the expression of antimicrobial proteins, RegIIIα and lipocalin-2, in gastric epithelial cells. Furthermore, patients with gastric tissue expressing IL-22 were more likely to respond to H. pylori eradication (14/22 vs 4/19, P0.006). We conclude that susceptibility of gastric MALT lymphoma is influenced by genetic polymorphisms in IL-22, the product of which is involved in mucosal immunity against H. pylori and associated with tumor response to H. pylori eradication.

Endophenotypes such as behavior disorders have been increasingly adopted in genetic studies for complex traits. For efficient gene mapping, it is essential that an endophenotype is associated with the disease of interest and is inheritable or co-segregating within families. In this study, we proposed a strategy to construct endophenotypes to analyze the Genetic Analysis Workshop 14 simulated dataset. Initially, generalized estimating equation models were employed to identify phenotypes that were correlated to the disease (affected status) in combination with the family structures in data. Endophenotypes were then constructed with consideration of heterogeneity as functions of the identified phenotypes. Genome scans on the constructed endophenotypes were carried out using family-based association analysis. For comparison, genome scans were also performed with the original affected status. The family-based association analysis using the endophenotypes correctly identified the same susceptible gene in about 80 of the 100 replicates.

Background Copy number variations (CNVs) have recently been recognized as important structural variations in the human genome. CNVs can affect gene expression and thus may contribute to phenotypic differences. The copy number inferring tool (CNIT) is an effective hidden Markov model-based algorithm for estimating allele-specific copy number and predicting chromosomal alterations from single nucleotide polymorphism microarrays. The CNIT algorithm, which was constructed using data from 270 HapMap multi-ethnic individuals, was applied to identify CNVs from 300 unrelated Han Chinese individuals in Taiwan. Results Using stringent selection criteria, 230 regions with variable copy numbers were identified in the Han Chinese population; 133 (57.83%) had been reported previously, 64 displayed greater than 1% CNV allele frequency. The average size of the CNV regions was 322 kb (ranging from 1.48 kb to 5.68 Mb) and covered a total of 2.47% of the human genome. A total of 196 of the CNV regions were simple deletions and 27 were simple amplifications. There were 449 genes and 5 microRNAs within these CNV regions; some of these genes are known to be associated with diseases. Conclusion The identified CNVs are characteristic of the Han Chinese population and should be considered when genetic studies are conducted. The CNV distribution in the human genome is still poorly characterized, and there is much diversity among different ethnic populations.