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Helix tree

Manufactured by Golden Helix
Sourced in United States

Helix Tree is a software application developed by Golden Helix that enables the visualization and analysis of phylogenetic trees. The core function of Helix Tree is to provide a user-friendly interface for constructing, manipulating, and interpreting phylogenetic relationships among genetic sequences or organisms.

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7 protocols using helix tree

1

Genetic Analysis of Suicide Risk in Schizophrenia

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Adherence to Hardy-Weinberg equilibrium was determined using the chi-square test in Haploview version 4 (Barrett et al. 2005 (link)). Analyses of SCZ cases with history of suicide attempt versus SCZ cases without were done using Fisher’s Exact Tests both in terms of allele frequencies and genotype frequencies. Haplotype analyses were done using UNPHASED version 3.1.5 (Dudbridge 2008 (link)) for the analysis of history of suicide attempt. Haplotypes with frequencies of less than 5% were excluded from the analyses. For multiple-testing correction, our study of nineteen single-nucleotide polymorphisms was equivalent to testing twelve independent markers (Nyholt et al. 2004 (link); Li and Ji 2005 (link)). Thus, the significance threshold was adjusted to 0.0042. Gene-gene interaction analyses were performed using HELIXTREE (GoldenHelix; e.g., (Zai et al. 2008 (link))), and the significant analyses (after Bonferroni correction) were validated with the R package Model-Based Multifactor Dimensionality Reduction version 2.6 (MB-MDR) (Calle et al. 2010 (link)) as well as SPSS version 15 (IBM). The significance of the model was determined by running 1000 permutations. The meta-analysis, which incorporated replication samples, was carried out in STATA version 8.
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2

Genetic Variant Analysis Protocol

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The Hardy-Weinberg equilibrium for the population distribution of the variant alleles was determined according to the approach described by Guo and Thompson [23] (link). Allelic chi-squares were examined for each SNP and the correlation/trend test was performed. Odds ratios (ORs) were calculated and P values were decided with statistical significance defined by P≤0.05 using HelixTree™ software (Golden Helix Inc, Bozeman, MT). We considered the differences significant with respect to mortality at a full scan permutation of the correlation/trend test P-value of <0.05. Fold change, or ΔRQ, was calculated to determine the magnitude of difference in gene expression and a Mann-Whitney U test was performed to demonstrate the reproducibility of the changes observed in the IRGM gene after LPS challenge. We compared the variables in different groups using the unpaired Student’s t-test and Mann-Whitney U test, depending on the type of variable. Statistical significance was defined as P<0.05. Statistical analyses were performed with the GraphPad Prism 5 software package for Windows (GraphPad Software, CA, USA).
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3

Viral Load Measurement Protocols for HIV, HCV, and HBV

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Plasma HIV RNA levels were measured using the NASBA/NuciSens HIV RNA assay (BioMerieux, Durham, NC, USA), in laboratories certified by the NIH National Institute of Allergy and Infectious Diseases Virology Quality Assurance Certification Program. HCV and HBV serological markers were performed using standard commercial assays and included hepatitis C antibody by EIA 3.0 (Ortho-Clinical Diagnostics, Raritan, NJ, USA) and hepatitis B surface antigen (HBsAg) (Abbott Laboratories, Abbott Park, IL, USA). HCV RNA levels were measured by the COBAS Amplicor Monitor 2.0 assay (Roche Diagnostics, Branchburg, NJ, USA) with a linear range of 600–700 000 IU/mL, or COBAS TaqMan (Roche Diagnostics), with a linear range of 10–2.0 × 108 IU/mL. Genotyping for rs368234815 (ss469415590) was performed at the Laboratory of Translational Genomics National Cancer Institute with custom TaqMan allelic discrimination genotyping assays, as previously described [14 (link)]. IFNL3/IFNL4 SNPs were genotyped as part of the WIHS genomewide association study using the IlluminaOmni2.5-quad beadchip (Illumina Inc, San Diego, CA, USA). Ancestry informative marker SNPs were selected using Helix Tree (Golden Helix, Bozeman, MT, USA).
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4

Genetic Factors Influencing Cholesterol Levels

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SNPs were genotyped using the Illumina HumanOmni2.5-quad beadchip (Illumina, San Diego) for all WIHS women enrolled in 1994–1995 and 2001–2002 who provided consent for genetic testing (n=3,353). Excluded from the dataset were SNPs with a genotype call rate of <95% and SNPs that failed our in-house quality control criteria. Specifically, SNPs with at least 2 discordant genotypes among greater than 20 duplicate samples were excluded.
SNPs within and flanking (i.e., approximately 20 kb surrounding) 19 candidate cholesterol genes (ABCA1, ABCG1, ABCA12, SCARB1, LDLR, SLC10A2, TTC39B, CYP39A1, CETP, PCSK9, MVK, RXRA, NR1H3, NCOR1, NCOR2, SREBF1, NR1H3, NR1H2 and RXRB) were used. Ancestry informative marker (AIM) SNPs from the Illumina beadchip were selected from across the genome using Helix Tree (Golden Helix, Bozeman, MT) to calculate principal components (PCs) of genomic race/ethnicity.
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5

Genetic Association Analysis with HWE

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Hardy–Weinberg equilibrium (HWE) was assessed using a chi-squared test. The HWE is a principle stating that the genetic variation in a population will remain constant from one generation to the next in the absence of disturbing factors. The HWE can be disturbed by several factors including mutations, natural selection, nonrandom mating, and genetic drift. A significant result (p<0.05) means that controls are not in HWE [13 (link)]. SNPAnalyzer-Pro (ISTECH, Goyang, Korea), HelixTree (Golden Helix, Bozeman, United States), and SNPStats (http://bioinfo.iconcologia.net/index.php?module=Snpstats) were used to analyze genetic data. Multiple logistic regression models (codominant 1, codominant 2, dominant, recessive, overdominant, and log-additive) were performed to determine the odds ratios (ORs), 95% confidence intervals (CIs) and p-values, (age and gender were controlled as co-variables). For the statistical power, the required case size in each SNP was calculated using the genetic power calculator (http://pngu.mgh.harvard.edu/~purcell/gpc/cc2.html). The statistical significance level was considered as a p-value below 0.05.
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6

Imputation of HLA Genes in WIHS Cohort

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SNPs were genotyped using the Illumina HumanOmni2.5-quad beadchip (Illumina, San Diego) for all WIHS women enrolled in the 1994-1995 and 2001-2002 recruitment waves who provided informed consent for genetic testing (n=3,353). Excluded from the analytic dataset were SNPs with a genotype call rate of <95% and SNPs that failed our in-house quality control criteria. Specifically, SNPs with at least 2 discordant genotypes among greater than 20 duplicate samples were excluded.
SNPs flanking (i.e., approximately 100kb surrounding) the classical HLA genes typed in WIHS were used for imputation (i.e., 379 SNPs spanning HLA-A (chr6: 29,810,001-30,013,000), 725 SNPs spanning HLA-B/C (chr6: 31,136,000-31,424,000), 848 SNPs spanning HLA-DQ/DR (chr6: 32,307,000-32,734,000)). Mapping positions were defined using NCBI build 37, hg19. Ancestry informative marker (AIM) SNPs (n=185, Supplementary Table 1) from the Illumina beadchip were selected from across the genome using Helix Tree (Golden Helix, Bozeman, MT) to calculate principal components (PCs) of ancestry.
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7

Genetic Association Analysis of SNPs

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For all SNPs, compliance with the Hardy-Weinberg equilibrium (HWE) was assessed using SNPstats software (http://bioinfo.iconcologia.net/index.php?module=Snpstats) in patients and controls, and adjusted for age and sex. We analyzed the genetic data, using Helixtree (Golden Helix Inc., Bozeman, MT, USA) and SNPAnalyzer (Istech Inc., Goyang, Korea). Multiple logistic regression models (codominant, dominant, and recessive) were applied to get odds ratio (OR), 95% confidence interval (CI) and P-value. Analysis of data was performed using SPSS ver. 18.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was set at P<0.05.
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