Meta-Qtest: meta-analysis of quadratic test for rare variants

Background In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level. Results Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT. Conclusion As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did.Publication costs are funded by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) grant (HI16C2037). Also, this work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165, HI16C2048

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

Backgrounds Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. Results Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. Conclusion In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/.Publication costs are funded by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) grant (HI16C2037). Also, this work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165, HI16C2048)

Type 2 diabetes genetic association database manually curated for the study design and odds ratio

<p>Abstract</p> <p>Background</p> <p>The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.</p> <p>Description</p> <p>The list of publications used in this study was collected from the HuGE Navigator, which is an online database of published genome epidemiology literature. Because type 2 diabetes genetic association database (T2DGADB) aims to provide specialized information on the genetic risk factors involved in the development of type 2 diabetes, 701 of the 1,771 publications in the type 2 Diabetes case-control study for the development of the disease were extracted.</p> <p>Conclusions</p> <p>In the database, the association results were grouped as either positive or negative. The gene and SNP names were replaced with gene symbols and rsSNP numbers, the association p-values were determined manually, and the results are displayed by graphs and tables. In addition, the study design in publications, such as the population type and size are described. This database can be used for research purposes, such as an association and functional study of type 2 diabetes related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.</p

Implication of Genetic Variants Near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in Type 2 Diabetes and Obesity in 6,719 Asians

OBJECTIVE— Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans. However, the implications of these genes in Asians are unclear

Gene Flow between the Korean Peninsula and Its Neighboring Countries

SNP markers provide the primary data for population structure analysis. In this study, we employed whole-genome autosomal SNPs as a marker set (54,836 SNP markers) and tested their possible effects on genetic ancestry using 320 subjects covering 24 regional groups including Northern ( = 16) and Southern ( = 3) Asians, Amerindians ( = 1), and four HapMap populations (YRI, CEU, JPT, and CHB). Additionally, we evaluated the effectiveness and robustness of 50K autosomal SNPs with various clustering methods, along with their dependencies on recombination hotspots (RH), linkage disequilibrium (LD), missing calls and regional specific markers. The RH- and LD-free multi-dimensional scaling (MDS) method showed a broad picture of human migration from Africa to North-East Asia on our genome map, supporting results from previous haploid DNA studies. Of the Asian groups, the East Asian group showed greater differentiation than the Northern and Southern Asian groups with respect to Fst statistics. By extension, the analysis of monomorphic markers implied that nine out of ten historical regions in South Korea, and Tokyo in Japan, showed signs of genetic drift caused by the later settlement of East Asia (South Korea, Japan and China), while Gyeongju in South East Korea showed signs of the earliest settlement in East Asia. In the genome map, the gene flow to the Korean Peninsula from its neighboring countries indicated that some genetic signals from Northern populations such as the Siberians and Mongolians still remain in the South East and West regions, while few signals remain from the early Southern lineages

Single nucleotide polymorphisms in bone turnover-related genes in Koreans: ethnic differences in linkage disequilibrium and haplotype

<p>Abstract</p> <p>Background</p> <p>Osteoporosis is defined as the loss of bone mineral density that leads to bone fragility with aging. Population-based case-control studies have identified polymorphisms in many candidate genes that have been associated with bone mass maintenance or osteoporotic fracture. To investigate single nucleotide polymorphisms (SNPs) that are associated with osteoporosis, we examined the genetic variation among Koreans by analyzing 81 genes according to their function in bone formation and resorption during bone remodeling.</p> <p>Methods</p> <p>We resequenced all the exons, splice junctions and promoter regions of candidate osteoporosis genes using 24 unrelated Korean individuals. Using the common SNPs from our study and the HapMap database, a statistical analysis of deviation in heterozygosity depicted.</p> <p>Results</p> <p>We identified 942 variants, including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers. Of the SNPs, 557 (63%) had been previously identified and 331 (37%) were newly discovered in the Korean population. When compared SNPs in the Korean population with those in HapMap database, 1% (or less) of SNPs in the Japanese and Chinese subpopulations and 20% of those in Caucasian and African subpopulations were significantly differentiated from the Hardy-Weinberg expectations. In addition, an analysis of the genetic diversity showed that there were no significant differences among Korean, Han Chinese and Japanese populations, but African and Caucasian populations were significantly differentiated in selected genes. Nevertheless, in the detailed analysis of genetic properties, the LD and Haplotype block patterns among the five sub-populations were substantially different from one another.</p> <p>Conclusion</p> <p>Through the resequencing of 81 osteoporosis candidate genes, 118 unknown SNPs with a minor allele frequency (MAF) > 0.05 were discovered in the Korean population. In addition, using the common SNPs between our study and HapMap, an analysis of genetic diversity and deviation in heterozygosity was performed and the polymorphisms of the above genes among the five populations were substantially differentiated from one another. Further studies of osteoporosis could utilize the polymorphisms identified in our data since they may have important implications for the selection of highly informative SNPs for future association studies.</p

Identification of Close Relatives in the HUGO Pan-Asian SNP Database

The HUGO Pan-Asian SNP Consortium has recently released a genome-wide dataset, which consists of 1,719 DNA samples collected from 71 Asian populations. For studies of human population genetics such as genetic structure and migration history, this provided the most comprehensive large-scale survey of genetic variation to date in East and Southeast Asia. However, although considered in the analysis, close relatives were not clearly reported in the original paper. Here we performed a systematic analysis of genetic relationships among individuals from the Pan-Asian SNP (PASNP) database and identified 3 pairs of monozygotic twins or duplicate samples, 100 pairs of first-degree and 161 second-degree of relationships. Three standardized subsets with different levels of unrelated individuals were suggested here for future applications of the samples in most types of population-genetics studies (denoted by PASNP1716, PASNP1640 and PASNP1583 respectively) based on the relationships inferred in this study. In addition, we provided gender information for PASNP samples, which were not included in the original dataset, based on analysis of X chromosome data

Population Genetic Structure of Peninsular Malaysia Malay Sub-Ethnic Groups

Patterns of modern human population structure are helpful in understanding the history of human migration and admixture. We conducted a study on genetic structure of the Malay population in Malaysia, using 54,794 genome-wide single nucleotide polymorphism genotype data generated in four Malay sub-ethnic groups in peninsular Malaysia (Melayu Kelantan, Melayu Minang, Melayu Jawa and Melayu Bugis). To the best of our knowledge this is the first study conducted on these four Malay sub-ethnic groups and the analysis of genotype data of these four groups were compiled together with 11 other populations' genotype data from Indonesia, China, India, Africa and indigenous populations in Peninsular Malaysia obtained from the Pan-Asian SNP database. The phylogeny of populations showed that all of the four Malay sub-ethnic groups are separated into at least three different clusters. The Melayu Jawa, Melayu Bugis and Melayu Minang have a very close genetic relationship with Indonesian populations indicating a common ancestral history, while the Melayu Kelantan formed a distinct group on the tree indicating that they are genetically different from the other Malay sub-ethnic groups. We have detected genetic structuring among the Malay populations and this could possibly be accounted for by their different historical origins. Our results provide information of the genetic differentiation between these populations and a valuable insight into the origins of the Malay sub-ethnic groups in Peninsular Malaysia