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Common SNPs in nicotinic acetylcholine receptor genes (CHRN genes) have been associated with drug behaviors and personality traits, but the influence of rare genetic variants is not well characterized. The goal of this project was to identify novel rare variants in CHRN genes in the Center for Antisocial Drug Dependence (CADD) and Genetics of Antisocial Drug Dependence (GADD) samples and to determine if low frequency variants are associated with antisocial drug dependence. Two samples of 114 and 200 individuals were selected using a case/control design including the tails of the phenotypic distribution of antisocial drug dependence. The capture, sequencing, and analysis of all variants in 16 CHRN genes (CHRNA1-7, 9, 10, CHRNB1-4, CHRND, CHRNG, CHRNE) were performed independently for each subject in each sample. Sequencing reads were aligned to the human reference sequence using BWA prior to variant calling with the Genome Analysis ToolKit (GATK). Low frequency variants (minor allele frequency

• For the first time, identify the association between rs539668656 and PD risk. • NUS1 rare variants, or rs539668656 was associated with PD phenotype. • Rs969919569, c.432T>G and rs369403261 were predicted to be deleterious. NUS1 has been recently identified as a candidate gene for Parkinson's disease (PD). Few studies have examined the association of NUS1 variants with PD susceptibility and phenotypes. In the first cohort, whole-exome sequencing was performed to identify variants in NUS1 exon-coding and exon-intron regions in 1542 cases and 1625 controls. 13 variants were totally detected, of which 10 rare variants and 3 low-frequency variants. Burden analysis showed that rare NUS1 variants significantly enriched in PD (p =0.016). We also performed a meta-analysis based on previous and our studies to correlate NUS1 mutations with PD susceptibility. Integrating our previous cohort (3210 cases and 2807 controls) and the first cohort identified the significant association of rs539668656 with PD risk (odds ratio (OR) = 2.82, p = 0.016). The genotype-phenotype association analysis showed that patients carrying rare variants, or rs539668656 were significantly associated with earlier onset age, depression, emotional impairment and severe disease condition. Our results support the role of NUS1 rare variants and rs539668656 towards PD susceptibility and phenotype. [ABSTRACT FROM AUTHOR]

Abstract Background Duplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost—sequencing the same molecule multiple times increases dynamic range but significantly diminishes coverage, making whole genome duplex sequencing prohibitively expensive. Furthermore, every duplex experiment produces a substantial proportion of singleton reads that cannot be used in the analysis and are thrown away. Results In this paper we demonstrate that a significant fraction of these reads contains PCR or sequencing errors within duplex tags. Correction of such errors allows “reuniting” these reads with their respective families increasing the output of the method and making it more cost effective. Conclusions We combine an error correction strategy with a number of algorithmic improvements in a new version of the duplex analysis software, Du Novo 2.0. It is written in Python, C, AWK, and Bash. It is open source and readily available through Galaxy, Bioconda, and Github: https://github.com/galaxyproject/dunovo.

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference size >50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population.

Background: Duplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost—sequencing the same molecule multiple times increases dynamic range but significantly diminishes coverage, making whole genome duplex sequencing prohibitively expensive. Furthermore, every duplex experiment produces a substantial proportion of singleton reads that cannot be used in the analysis and are thrown away. Results: In this paper we demonstrate that a significant fraction of these reads contains PCR or sequencing errors within duplex tags. Correction of such errors allows "reuniting" these reads with their respective families increasing the output of the method and making it more cost effective. Conclusions: We combine an error correction strategy with a number of algorithmic improvements in a new version of the duplex analysis software, Du Novo 2.0. It is written in Python, C, AWK, and Bash. It is open source and readily available through Galaxy, Bioconda, and Github: https://github.com/galaxyproject/dunovo. [ABSTRACT FROM AUTHOR]

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference size >50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population. [ABSTRACT FROM AUTHOR]

Low-frequency HIV variants possessing resistance mutations against non‑nucleoside reverse transcriptase inhibitors (NNRTI), especially at HIV reverse transcriptase (RT) amino acid (aa) positions K103 and Y181, have been shown to adversely affect treatment response. Therapeutic failure correlates with both the mutant viral variant frequency and the mutational load. We determined the prevalence of NNRTI resistance mutations at several RT aa positions in viruses from 204 antiretroviral (ARV)-naïve HIV-infected individuals using deep sequencing, and examined the relationship between mutant variant frequency and mutational load for those variants. Deep sequencing to ≥0.4% levels found variants with major NNRTI-resistance mutations having a Stanford-HIVdb algorithm value ≥30 for efavirenz and/or nevirapine in 52/204 (25.5%) ARV-naïve HIV-infected persons. Eighteen different major NNRTI mutations were identified at 11 different positions, with the majority of variants being at frequency >1%. The frequency of these variants correlated strongly with the mutational load, but this correlation weakened at low frequencies. Deep sequencing detected additional major NNRTI-resistant viral variants in treatment-naïve HIV-infected individuals. Our study suggests the significance of screening for mutations at all RT aa positions (in addition to K103 and Y181) to estimate the true burden of pre-treatment NNRTI-resistance. An important finding was that variants at low frequency had a wide range of mutational loads (>100-fold) suggesting that frequency alone may underestimate the impact of specific NNRTI-resistant variants. We recommend further evaluation of all low-frequency NNRTI-drug resistant variants with special attention given to the impact of mutational loads of these variants on treatment outcomes.

Low-frequency HIV variants possessing resistance mutations against non-nucleoside reverse transcriptase inhibitors (NNRTI), especially at HIV reverse transcriptase (RT) amino acid (aa) positions K103 and Y181, have been shown to adversely affect treatment response. Therapeutic failure correlates with both the mutant viral variant frequency and the mutational load. We determined the prevalence of NNRTI resistance mutations at several RT aa positions in viruses from 204 antiretroviral (ARV)-naïve HIV-infected individuals using deep sequencing, and examined the relationship between mutant variant frequency and mutational load for those variants. Deep sequencing to ≥0.4% levels found variants with major NNRTI-resistance mutations having a Stanford-HIVdb algorithm value ≥30 for efavirenz and/or nevirapine in 52/204 (25.5%) ARV-naïve HIV-infected persons. Eighteen different major NNRTI mutations were identified at 11 different positions, with the majority of variants being at frequency >1%. The frequency of these variants correlated strongly with the mutational load, but this correlation weakened at low frequencies. Deep sequencing detected additional major NNRTI-resistant viral variants in treatment-naïve HIV-infected individuals. Our study suggests the significance of screening for mutations at all RT aa positions (in addition to K103 and Y181) to estimate the true burden of pre-treatment NNRTI-resistance. An important finding was that variants at low frequency had a wide range of mutational loads (>100-fold) suggesting that frequency alone may underestimate the impact of specific NNRTI-resistant variants. We recommend further evaluation of all low-frequency NNRTI-drug resistant variants with special attention given to the impact of mutational loads of these variants on treatment outcomes. [ABSTRACT FROM AUTHOR]

Abstract: Allele-specific PCR based on subtype consensus sequences is a powerful technique for detecting low frequency drug resistant mutants in HIV-1 infected patients. However, this approach can be limited by genetic variation in the region complementary to the primers, leading to variability in allele detection. The goals of this study were to quantify this effect and then to improve assay performance. [Copyright &y& Elsevier]

While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A  range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.