An NT-3-releasing bioscaffold supports the formation of TrkC-modified neural stem cell-derived neural network tissue with efficacy in repairing spinal cord injury.

The mechanism underlying neurogenesis during embryonic spinal cord development involves a specific ligand/receptor interaction, which may be help guide neuroengineering to boost stem cell-based neural regeneration for the structural and functional repair of spinal cord injury. Herein, we hypothesized that supplying spinal cord defects with an exogenous neural network in the NT-3/fibroin-coated gelatin sponge (NF-GS) scaffold might improve tissue repair efficacy. To test this, we engineered tropomyosin receptor kinase C (TrkC)-modified neural stem cell (NSC)-derived neural network tissue with robust viability within an NF-GS scaffold. When NSCs were genetically modified to overexpress TrkC, the NT-3 receptor, a functional neuronal population dominated the neural network tissue. The pro-regenerative niche allowed the long-term survival and phenotypic maintenance of the donor neural network tissue for up to 8 weeks in the injured spinal cord. Additionally, host nerve fibers regenerated into the graft, making synaptic connections with the donor neurons. Accordingly, motor function recovery was significantly improved in rats with spinal cord injury (SCI) that received TrkC-modified NSC-derived neural network tissue transplantation. Together, the results suggested that transplantation of the neural network tissue formed in the 3D bioactive scaffold may represent a valuable approach to study and develop therapies for SCI.

Multivariate Gene-Based Association Test on Family Data in MGAS.

In analyses of unrelated individuals, the program multivariate gene-based association test by extended Simes (MGAS), which facilitates multivariate gene-based association testing, was shown to have correct Type I error rate and superior statistical power compared to other multivariate gene-based approaches. Here we show, through simulation, that MGAS can also be applied to data including genetically related subjects (e.g., family data), by using p value information obtained in Plink or in generalized estimating equations (with the 'exchangeable' working correlation matrix), both of which account for the family structure on a univariate single nucleotide polymorphism-based level by applying a sandwich correction of standard errors. We show that when applied to family-data, MGAS has correct Type I error rate, and given the details of the simulation setup, adequate power. Application of MGAS to seven eye measurement phenotypes showed statistically significant association with two genes that were not discovered in previous univariate analyses of a composite score. We conclude that MGAS is a useful and convenient tool for multivariate gene-based genome-wide association analysis in both unrelated and related individuals.

FAPI: Fast and accurate P-value Imputation for genome-wide association study.

Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi.

SNPTracker: A Swift Tool for Comprehensive Tracking and Unifying dbSNP rs IDs and Genomic Coordinates of Massive Sequence Variants.

The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

MOTIVATION: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. RESULTS: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. CONCLUSION: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. AVAILABILITY AND IMPLEMENTATION: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis.

The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases.

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M(e)) for the adjustment of multiple testing, but current methods of calculation for M(e) are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M(e). Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M(e), and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10(-7) as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10(-8) for current or merged commercial genotyping arrays, ~10(-8) for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10(-8) for the common SNPs only within genes.

GATES: a rapid and powerful gene-based association test using extended Simes procedure.

The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.

Identification of genes with allelic imbalance on 6p associated with nasopharyngeal carcinoma in southern Chinese.

Nasopharyngeal carcinoma (NPC) is a malignancy of epithelial origin. The etiology of NPC is complex and includes multiple genetic and environmental factors. We employed case-control analysis to study the association of chromosome 6p regions with NPC. In total, 360 subjects and 360 healthy controls were included, and 233 single nucleotide polymorphisms (SNPs) on 6p were examined. Significant single-marker associations were found for SNPs rs2267633 (p = 4.49 × 10(-5)), rs2076483 (most significant, p = 3.36 × 10(-5)), and rs29230 (p=1.43 × 10(-4)). The highly associated genes were the gamma-amino butyric acid B receptor 1 (GABBR1), human leukocyte antigen (HLA-A), and HLA complex group 9 (HCG9). Haplotypic associations were found for haplotypes AAA (located within GABBR1, p-value  = 6.46 × 10(-5)) and TT (located within HLA-A, p = 0.0014). Further investigation of the homozygous genotype frequencies between cases and controls suggested that micro-deletion regions occur in GABBR1 and neural precursor cell expressed developmentally down-regulated 9 (NEDD9). Quantitative real-time polymerase chain reaction (qPCR) using 11 pairs of NPC biopsy samples confirmed the significant decline in GABBR1 and NEDD9 mRNA expression in the cancer tissues compared to the adjacent non-tumor tissue (p<0.05). Our study demonstrates that multiple chromosome 6p susceptibility loci contribute to the risk of NPC, possibly though GABBR1 and NEDD9 loss of function.

Complete genome sequence of the bacterium Methylovorus sp. strain MP688, a high-level producer of pyrroloquinolone quinone.

Methylotrophic bacteria are widespread microbes which can use one carbon compound as their only carbon and energy sources. Here we report the finished, annotated genome sequence of the methylotrophic bacterium Methylovorus sp. strain MP688, which was isolated from soil for high-level production of pyrroloquinolone quinone (PQQ) in our lab.

A knowledge-based weighting framework to boost the power of genome-wide association studies.

BACKGROUND: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. CONCLUSIONS/SIGNIFICANCE: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases.

IGG3: a tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis.

SUMMARY: There is an urgent and increasing demand for integrating large genotype datasets across genome-wide association studies and HapMap project for whole-genome imputation and individual-level meta-analysis. A new algorithm was developed to efficiently merge raw genotypes across large datasets and implemented in the latest version of IGG, IGG3. In addition, IGG3 can integrate the latest phased and unphased HapMap genotypes and can flexibly generate complete sets of input files for six popular genotype imputation tools. We demonstrated the efficiency of IGG3 by simulation tests, which could rapidly merge genotypes in tens of thousands of large genotype chips (e.g. Affymetrix Genome-Wide Human SNP Array 6.0 and Illumina Human1m-duo) and in HapMap III project on an ordinary desktop computer. AVAILABILITY: (http://bioinfo.hku.hk/iggweb) (version 3.0).

Power of transmission/disequilibrium tests in admixed populations.

The power of transmission/disequilibrium tests (TDTs) for detecting disease susceptibility loci is expected to be influenced by population admixture through its impact on the degree of linkage disequilibrium (LD) between the genetic marker and the DSL. However, few studies have been done to systematically examine this behavior of the TDTs in admixed populations. In the present study, extensive computer simulations were conducted to explore how population admixture affects the power of TDTs. It was found that (1) in newly admixed populations, the LD due to admixture makes no contribution to the power of TDTs, and it is the averaged background LD in the parental populations that determines the power of TDTs; but (2) after random mating between the admixed populations, the LD due to admixture becomes effective in increasing or decreasing the power of the tests, and (3) incomplete random mating can prolong the time for the LD due to admixture to become effective. This study clarifies the potential influence of population admixture on the performance of TDTs.

The genetic, environmental and phenotypic correlations of bone phenotypes at the spine and hip in Chinese.

BACKGROUND: Bone mineral density (BMD), bone mineral content (BMC), and bone size have been widely studied individually as important risk factors for osteoporotic fracture, but little is known about the correlation and the degree of sharing genetic and environmental factors between the pairs of the three phenotypes. AIM: The study investigated genetic correlation (rhoG), environmental correlation (rhoE) and phenotypic correlation (rhoP) between BMD, BMC and bone size. SUBJECTS AND METHODS: Bivariate variance decomposition analyses were performed in 904 subjects from 287 Chinese nuclear families. RESULTS: Significant rhoE, rhoG and rhoP were detected between BMD, BMC and bone size, except for rhoE between BMD and bone size at the hip (rhoE = 0.121, p = 0.361). Common shared genetic factors explained 86.1% and 60% of BMD and BMC genetic variations at the spine and hip, respectively. However, the genetic and environmental correlations between BMD and bone size were limited. rhoE and rhoG at the spine were 0.392 and 0.381, and at the hip were 0.121 and -0.205, respectively. Only 14.5% and 4.2% of variations between BMD and bone size at the spine and hip may be due to the shared genetic factors. CONCLUSION: The obtained results suggested that bone size may be used as another surrogate phenotype independently of BMD for eventual elucidation of the pathogenesis of osteoporosis because of the limited correlations between BMD and bone size.

Genetic determination and correlation of body mass index and bone mineral density at the spine and hip in Chinese Han ethnicity.

The purpose of the present study was to evaluate the magnitude of genetic determination of spine and hip bone mineral density (BMD) and body mass index (BMI), and to explore the genetic, environmental, and phenotypic correlations among the above phenotypes in Chinese Han ethnicity. The sample was composed of at least 217 complete nuclear families in Chinese Han ethnicity. BMD at the spine and hip was measured using a dual-energy X-ray absorptiometry scanner. The heritability (h2) of BMI and BMD at the spine and hip, the genetic correlation (rhoG) and environmental correlation (rhoE) among the three phenotypes were evaluated via variance analysis, with age, sex, and age-by-sex interaction as covariates. The phenotypic correlation (rhoP) and the bivariate heritability rhoG2 were also calculated. The heritability for BMD and BMI was approximately 0.70 and approximately 0.50, respectively (p<0.0001). The common environment shared by household members (household effect) is significant for BMI variation (p=0.0004). Significant genetic, environmental, and phenotypic correlation was observed. The rhoG2 values were 0.13 for BMI/spine BMD, 0.18 for BMI/hip BMD, and 0.58 for the spine BMD/hip BMD. While BMD at the spine and hip have significant genetic determination, BMI is more likely to be affected by environmental factors than BMD. In addition, BMD at the spine and hip shares more genetic effect (pleiotropy) than BMI and BMD do in Chinese Han ethnicity, though the effects are significant for both.

Exclusion mapping of chromosomes 1, 4, 6 and 14 with bone mineral density in 79 Caucasian pedigrees.

Low bone mineral density (BMD) is a major determinant of osteoporosis and is under strong genetic control. A large number of linkage and association studies for BMD variation have been conducted, with the results being largely inconsistent. Linkage exclusion analysis is a useful tool for gene mapping but has never been used on BMD. In the present study, we conducted a linkage exclusion mapping for BMD variation on chromosomes 1, 4, 6 and 17 in 79 Caucasian pedigrees. For hip BMD variation, several genomic regions were excluded for effect sizes of 10% or greater, including regions of 61-77 cM at 1p35-p34, 167-196 cM at 1q21-q23 and 261-291 cM at 1q42-q44; 85-112 cM at 4q21-q25 and 146-150 cM at 4q31; and 77-85 cM at 6p12-q13. For spine BMD, we were able to exclude the regions of 168-189 cM at 1q21-q23, 92-94 cM at 4q21 and 106-107 cM at 4q24 and 56-103 cM at 17q12-q25, as having effect sizes of 10% or greater. These results suggest that a number of candidate genes located in the excluded regions, such as interleukin 6 receptor (IL6R) gene, type I collagen alpha 1 (COL1A1) gene and bone morphogenetic protein-3 (BMP3) gene are unlikely to have a substantial effect on BMD variation in this Caucasian population. Along with previous studies searching for genes underlying BMD variation, the current study has further delineated the genetic basis of BMD variation and provided valuable information for future genetic studies.

Alpha2-HS glycoprotein gene is associated with bone size at the hip in Chinese.

Bone size is an important risk factor, independent of bone mineral density (BMD), for osteoporotic fracture. Bone size has a high heritability. A better understanding of genetic factors regulating bone size will have important clinical implications. In this study, we explored the relationship between the alpha2-HS glycoprotein (AHSG) gene and bone size variation at the spine and hip in a Chinese population. The study sample comprised 1 260 subjects from 401 Chinese nuclear families (each including both parents and at least one female child). The Sac / polymorphism inside the exon 7 of the AHSG gene was genotyped and analyzed. This variant represents a nucleotide substitution of C to G at amino acid position 238 resulting in a translation polymorphism of threonine to serine and thus making a potential impact on gene function. We assessed population stratification but did not find significant evidence at any skeletal sites. We found significant association between the AHSG Sac / polymorphism and bone size at the intertrochanteric region (P = 0.019) and the total hip (P = 0.035). The polymorphisms explained 3.74% and 3.16% variations in bone size at the intertrochanteric region and total hip respectively. No significant evidence of linkage was detected, largely due to the limited number of sibpairs in this data set and less informative marker (AHSG Sac / polymorphism) (compared with microsatellite markers) for linkage analysis. Our results suggested that the AHSG gene may contribute to bone size variation at the hip in this Chinese population.