Human Heredity最新文献

Parent-of-origin effects, which describe an occurrence where the expression of a gene depends on its parental origin, are an important phenomenon in epigenetics. Statistical methods for detecting parent-of-origin effects on autosomes have been investigated for 20 years, but the development of statistical methods for detecting parent-of-origin effects on the X chromosome is relatively new. In the literature, a class of Q-XPAT-type tests are the only tests for the parent-of-origin effects for quantitative traits on the X chromosome. In this paper, we propose two simple and powerful classes of tests to detect parent-of-origin effects for quantitative trait values on the X chromosome. The proposed tests can accommodate complete and incomplete nuclear families with any number of daughters. The simulation study shows that our proposed tests produce empirical type I error rates that are close to their respective nominal levels, as well as powers that are larger than those of the Q-XPAT-type tests. The proposed tests are applied to a real data set on Turner's syndrome, and the proposed tests give a more significant finding than the Q-C-XPAT test.

Objectives: To estimate the prevalence and mutation types of G6PD deficiency and evaluate the relationship between G6PD genotypes and erythrocyte phenotypes in the Dai and Jingpo ethnic groups in the Dehong prefecture of the Yunnan province, China.

Methods: G6PD deficiency was screened in Dai (1,530 individuals) and Jingpo (372 individuals) populations using a modified G6PD/6PGD ratio assay. Red blood cell traits were analyzed using the Sysmex XE2100 fully automated blood analyzer. PCR-direct sequencing for G6PD genotyping analysis was performed, and then the linkage disequilibrium blocks of the target SNPs were constructed with Haploview 4.2 software.

Results: The prevalence of G6PD deficiency was higher in the Dai ethnic group (8.63%) than in the Jingpo ethnic group (5.91%). The major mutations in descending order were rs137852314 G>A, rs72554664 G>A, rs72554665 G>T, and rs137852341 G>T. Hemoglobin concentration was significantly lower in the rs137852314 G>A group than in the normal group (p = 0.021). Mean corpuscular volume and mean corpuscular hemoglobin were substantially higher in the rs137852341 G>T group compared to the normal group (p = 0.049, p = 0.042). A linkage disequilibrium block of 13 SNPs was constructed for the G6PD deficiency group from the Dai sample.

Conclusions: The Dai and Jingpo ethnic groups have distinctive incidence rates and gene frequencies of G6PD deficiency, and the genotypes of G6PD deficiency are associated with erythrocyte phenotypes.

Background: Intellectual disability (ID) has been defined as a considerably reduced ability to understand new or complex information and to learn new skills. It is associated with life-long intellectual and adaptive functioning impairments that have a profound impact on individuals, families, and society. It affects about 3% of the general population. ID often comes out with other mental conditions like attention deficit, hyperactivity, and autism spectrum disorders (ASD), and it can be part of a malformation syndrome that affects other organs. It may be syndromic (S-ID) or non-syndromic (NS-ID).

Objective: The aims of this study were to identify the profile of intellectually disable patients being referred for cytogenetic analysis in Morocco, to determine the prevalence of chromosomal abnormalities in a Moroccan group, and to compare the results with those of analogous studies from other countries.

Participants: We included data from Moroccan patients with NS-ID and others with S-ID (mostly Down syndrome cases) who have been referred between 1996 and 2016. 1,626 patients were involved in this study, 1,200 were referred with a clinical diagnosis of Down syndrome, 37 were clinically diagnosed for ASD with ID, and 389 were suspected of NS-ID.

Results: We identified 1,200 cases of Down syndrome. In 1,096 analyses (91.3%), a cytogenetic variant of trisomy 21 was identified: standard trisomy 21 in 1,037 cases (94.6%), a translocation in 34 cases (3.10%), and mosaicism in 25 cases (2.3%). The cytogenetic analysis among ASD with ID cases did not reveal any specific chromosomal abnormalities. The present study also shows that chromosomal abnormalities were present in 6.43% of the patients with NS-ID (25 abnormal karyotypes out of 389 NS-ID cases). Autosomal structural abnormalities were the largest proportion of chromosomal aberrations.

Conclusion: The high rate of chromosomal abnormalities found in the Moroccan patients studied demonstrates the capital importance of cytogenetic evaluation in patients who show ID or any clinical development abnormality.

Background: tRNAscan-SE is the leading tool for transfer RNA (tRNA) annotation, which has been widely used in the field. However, tRNAscan-SE can return a significant number of false positives when applied to large sequences. Recently, conventional machine learning methods have been proposed to address this issue, but their efficiency can be still limited due to their dependency on handcrafted features. With the growing availability of large-scale genomic data-sets, deep learning methods, especially convolutional neural networks, have demonstrated excellent power in characterizing sequence patterns in genomic sequences. Thus, we hypothesize that deep learning may bring further improvement for tRNA prediction.

Methods: We proposed a new computational approach based on deep neural networks to predict tRNA gene sequences. We designed and investigated various deep neural network architectures. We used the tRNA sequences as positive samples, and the false-positive tRNA sequences predicted by tRNAscan-SE in coding sequences as negative samples, to train and evaluate the proposed models by comparison with the conventional machine learning methods and popular tRNA prediction tools.

Results: Using the one-hot encoding method, our proposed models can extract features without involving extensive manual feature engineering. Our proposed best model outperformed the existing methods under different performance metrics.

Conclusion: The proposed deep learning methods can substantially reduce the false positive output by the state-of-the-art tool tRNAscan-SE. Coupled with tRNAscan-SE, it can serve as a useful complementary tool for tRNA annotation. The application to tRNA prediction demonstrates the superiority of deep learning in automatic feature generation for characterizing sequence patterns.

Background: Advances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation.

Results: In this study, we compared the performance of NXT following Fluidigm's protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93-100% per cell).

Conclusions: Our results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm's protocol using NXT.

Background: Family-based design is one of the most popular designs in genetic studies. Transmission disequilibrium test (TDT) for family trio design is optimal only under the additive trait model and may lose power under the other trait models. The TDT-type tests are powerful only when the underlying trait model is correctly specified. Usually, the true trait model is unknown, and the selection of the TDT-type test is problematic. Several methods, which are robust against the mis-specification of the trait model, have been proposed. In this paper, we propose a new efficiency robust procedure for family trio design, namely, the weighted TDT (WTDT) test.

Methods: We combine information of the largest two TDT-type tests by using weights related to the three TDT-type tests and take the weighted sum as the test statistic.

Results: Simulation results demonstrate that WTDT has power close to, but much more robust than, the optimal TDT-type test based on a single trait model. WTDT also outperforms other efficiency robust methods in terms of power. Applications to real and simulated data from Genetic Analysis Workshop (GAW15) illustrate the practical application of the WTDT method.

Conclusion: WTDT is not only efficiency robust to model mis-specifications but also efficiency robust against mis-specifications of risk allele.

Objective: Stratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies, and the models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies; however, the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates.

Methods: We present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales.

Results: These equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors.

Conclusion: This straightforward method allows the conversion of model parameters between the logit and liability scales and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.

Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs.

Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach.

Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action.

Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

Background and aims: Many methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities.

Methods: Through coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees: a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties.

Results: In our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher exact test.

Conclusions: Our results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.