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2022 ◽  
Vol 293 ◽  
pp. 110683
Hongtao Wang ◽  
Chunhui Song ◽  
Sen Fang ◽  
Zhengyang Wang ◽  
Shangwei Song ◽  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Jiazhong Guo ◽  
Rui Jiang ◽  
Ayi Mao ◽  
George E. Liu ◽  
Siyuan Zhan ◽  

Abstract Background There is a long-term interest in investigating the genetic basis of the horned/polled phenotype in domestic goats. Here, we report a genome-wide association study (GWAS) to detect the genetic loci affecting the polled phenotype in goats. Results We obtained a total of 13,980,209 biallelic SNPs, using the genotyping-by-sequencing data from 45 Jintang Black (JT) goats, which included 32 female and nine male goats, and four individuals with the polled intersex syndrome (PIS). Using a mixed-model based GWAS, we identified two association signals, which were located at 150,334,857–150,817,260 bp (P = 5.15 × 10− 119) and 128,286,704–131,306,537 bp (P = 2.74 × 10− 15) on chromosome 1. The genotype distributions of the 14 most significantly associated SNPs were completely correlated with horn status in goats, based on the whole-genome sequencing (WGS) data from JT and two other Chinese horned breeds. However, variant annotation suggested that none of the detected SNPs within the associated regions were plausible causal mutations. Via additional read-depth analyses and visual inspections of WGS data, we found a 10.1-kb deletion (CHI1:g. 129424781_129434939del) and a 480-kb duplication (CHI1:150,334,286–150,818,098 bp) encompassing two genes KCNJ15 and ERG in the associated regions of polled and PIS-affected goats. Notably, the 10.1-kb deletion also served as the insertion site for the 480-kb duplication, as validated by PCR and Sanger sequencing. Our WGS genotyping showed that all horned goats were homozygous for the reference alleles without either the structural variants (SVs), whereas the PIS-affected goats were homozygous for both the SVs. We also demonstrated that horned, polled, and PIS-affected individuals among 333 goats from JT and three other Chinese horned breeds can be accurately classified via PCR amplification and agarose gel electrophoresis of two fragments in both SVs. Conclusion Our results revealed that two genomic regions on chromosome 1 are major loci affecting the polled phenotypes in goats. We provided a diagnostic PCR to accurately classify horned, polled, and PIS-affected goats, which will enable a reliable genetic test for the early-in-life prediction of horn status in goats.

2021 ◽  
Vol 53 (1) ◽  
Ruifei Yang ◽  
Zhenqiang Xu ◽  
Qi Wang ◽  
Di Zhu ◽  
Cheng Bian ◽  

Abstract Background Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. Results We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. Conclusions A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

2021 ◽  
Vol 6 ◽  
pp. 290
Alexander T. Williams ◽  
Nick Shrine ◽  
Hardeep Naghra-van Gijzel ◽  
Joanna C. Betts ◽  
Edith M. Hessel ◽  

Background: Globally, respiratory infections contribute to significant morbidity and mortality. However, genetic determinants of respiratory infections are understudied and remain poorly understood. Methods: We conducted a genome-wide association study in 19,459 hospitalised respiratory infection cases and 101,438 controls from UK Biobank. We followed-up well-imputed top signals from the UK Biobank discovery analysis in 50,912 respiratory infection cases and 150,442 controls from 11 cohorts. We aggregated effect estimates across studies using inverse variance-weighted meta-analyses. Additionally, we investigated the function of the top signals in order to gain understanding of the underlying biological mechanisms. Results: In the discovery analysis, we report 56 signals at P<5×10-6, one of which was genome-wide significant (P<5×10-8). The genome-wide significant signal was in an intron of PBX3, a gene that encodes pre-B-cell leukaemia transcription factor 3, a homeodomain-containing transcription factor. Further, the genome-wide significant signal was found to colocalise with gene-specific expression quantitative trait loci (eQTLs) affecting expression of PBX3 in lung tissue, where the respiratory infection risk alleles were associated with decreased PBX3 expression in lung tissue, highlighting a possible biological mechanism. Of the 56 signals, 40 were well-imputed in UK Biobank and were investigated in the 11 follow-up cohorts. None of the 40 signals replicated, with effect estimates attenuated. Conclusions: Our discovery analysis implicated PBX3 as a candidate causal gene and suggests a possible role of transcription factor binding activity in respiratory infection susceptibility. However, the PBX3 signal, and the other well-imputed signals, did not replicate when aggregating effect estimates across 11 independent cohorts. Significant phenotypic heterogeneity and differences in study ascertainment may have contributed to this lack of statistical replication. Overall, our study highlighted putative associations and possible biological mechanisms that may provide insight into respiratory infection susceptibility.

2021 ◽  
pp. jech-2020-216000
Molly Scannell Bryan ◽  
Temidayo Ogundiran ◽  
Oladosu Ojengbede ◽  
Wei Zheng ◽  
William Blot ◽  

IntroductionMany diseases of adulthood are associated with a woman’s age at menarche. Genetic variation affects age at menarche, but it remains unclear whether in women of African ancestry the timing of menarche is regulated by genetic variants that were identified in predominantly European and East Asian populations.MethodsWe explored the genetic architecture of age at menarche in 3145 women of African ancestry who live in the USA, Barbados and Nigeria. We undertook a genome-wide association study, and evaluated the performance of previously identified variants.ResultsOne variant was associated with age at menarche, a deletion at chromosome 2 (chr2:207216165) (p=1.14×10−8). 349 genotyped variants overlapped with these identified in populations of non-African ancestry; these replicated weakly, with 51.9% having concordant directions of effect. However, collectively, a polygenic score constructed of those previous variants was suggestively associated with age at menarche (beta=0.288 years; p=0.041). Further, this association was strong in women enrolled in the USA and Barbados (beta=0.445 years, p=0.008), but not in Nigerian women (beta=0.052 years; p=0.83).DiscussionThis study suggests that in women of African ancestry the genetic drivers of age at menarche may differ from those identified in populations of non-African ancestry, and that these differences are more pronounced in women living in Nigeria, although some associated trait loci may be shared across populations. This highlights the need for well-powered ancestry-specific genetic studies to fully characterise the genetic influences of age at menarche.

2021 ◽  
Vol 12 ◽  
Sanqi An ◽  
Yueqi Li ◽  
Yao Lin ◽  
Jiemei Chu ◽  
Jinming Su ◽  

The coronavirus disease 2019 (COVID-19) pandemic has caused many deaths worldwide. To date, the mechanism of viral immune escape remains unclear, which is a great obstacle to developing effective clinical treatment. RNA processing mechanisms, including alternative polyadenylation (APA) and alternative splicing (AS), are crucial in the regulation of most human genes in many types of infectious diseases. Because the role of APA and AS in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains unknown, we performed de novo identification of dynamic APA sites using a public dataset of human peripheral blood mononuclear cell (PBMC) RNA-Seq data in COVID-19 patients. We found that genes with APA were enriched in innate immunity -related gene ontology categories such as neutrophil activation, regulation of the MAPK cascade and cytokine production, response to interferon-gamma and the innate immune response. We also reported genome-wide AS events and enriched viral transcription-related categories upon SARS-CoV-2 infection. Interestingly, we found that APA events may give better predictions than AS in COVID-19 patients, suggesting that APA could act as a potential therapeutic target and novel biomarker in those patients. Our study is the first to annotate genes with APA and AS in COVID-19 patients and highlights the roles of APA variation in SARS-CoV-2 infection.

Vitaly B. Novakov ◽  
Olga N. Novakova ◽  
Mikhail I. Churnosov

Introduction. Knee Osteoarthritis (OA) is a multifactorial disease resulting from the interaction of many environmental, epigenetic and genetic risk factors, and the latter account for 40% to 65%. Genetic bases of the knee OA based on genome-wide association search (GWAS) are being actively studied by many scientific teams around the world. At the same time, the results obtained are often contradictory and ambiguous, as for the conducted replicative studies of knee OA. This dictates the need for additional replicative studies in various populations, including populations of Russia, which are characterized by significant ethno-territorial variability, in order to identify specific GWAS-significant polymorphic markers of candidate genes associated with OA in these individual populations. The aim of the study was to analyze genome-wide studies of knee OA and to establish GWAS-significant polymorphic loci associated with OA. Materials and methods. The search for publications was carried out in the electronic databases PubMed, PubMedCentral, eLIBRARY, in the GWAS catalog for the period from 2008 to the present by the keywords: knee osteoarthritis, GWAS studies, candidate genes. Results. First, to date, 14 genome-wide studies of knee OA have been performed, as a result of which about 80 GWAS-significant polymorphic loci associated with the risk of knee OA have been identified. Secondly, all GWAS of the knee OA were carried out abroad on samples from various foreign populations, and the samples from the Russian Federation were not included in these studies. Third, only two GWAS-significant polymorphic loci for OA (rs143384 of the GDF5 gene and rs3771501 of the TGFA gene) were replicated at the genome-wide significance level (p5x10-08) in two different studies. Fourth, the data obtained indicate the presence of two regions of chromosomes (6p21.32 and 7q22.3), in which the largest number of GWAS-significant polymorphic loci for OA is located - 3SNPs in each (6p21.32 - rs10947262, rs7775228, rs9277552; 7q22.3 - rs4730250, rs10953541, rs3815148). Fifth, with an increase in the volume of the studied samples of patients and control in genome-wide studies of knee OA, the number of identified GWAS-significant polymorphisms also increases. Conclusion. The main genome-wide studies of knee OA were reviewed and GWAS-significant polymorphisms associated with OA were identified. The obtained materials on GWAS-significant loci can be used both in the selection of polymorphisms in replicative studies of OA in various populations of Russia, and for expanding the understanding of the molecular genetic mechanisms of the disease development.

2021 ◽  
Vol 22 (1) ◽  
Patrik Waldmann

Abstract Background The genetic basis of phenotypic traits is highly variable and usually divided into mono-, oligo- and polygenic inheritance classes. Relatively few traits are known to be monogenic or oligogeneic. The majority of traits are considered to have a polygenic background. To what extent there are mixtures between these classes is unknown. The rapid advancement of genomic techniques makes it possible to directly map large amounts of genomic markers (GWAS) and predict unknown phenotypes (GWP). Most of the multi-marker methods for GWAS and GWP falls into one of two regularization frameworks. The first framework is based on $$\ell _1$$ ℓ 1 -norm regularization (e.g. the LASSO) and is suitable for mono- and oligogenic traits, whereas the second framework regularize with the $$\ell _2$$ ℓ 2 -norm (e.g. ridge regression; RR) and thereby is favourable for polygenic traits. A general framework for mixed inheritance is lacking. Results We have developed a proximal operator algorithm based on the recent LAVA regularization method that jointly performs $$\ell _1$$ ℓ 1 - and $$\ell _2$$ ℓ 2 -norm regularization. The algorithm is built on the alternating direction method of multipliers and proximal translation mapping (LAVA ADMM). When evaluated on the simulated QTLMAS2010 data, it is shown that the LAVA ADMM together with Bayesian optimization of the regularization parameters provides an efficient approach with lower test prediction mean-squared-error (65.89) than the LASSO (66.11), Ridge regression (83.41) and Elastic net (66.11). For the real pig data the test MSE of the LAVA ADMM is 0.850 compared to the LASSO, RR and EN with 0.875, 0.853 and 0.853, respectively. Conclusions This study presents the LAVA ADMM that is capable of joint modelling of monogenic major genetic effects and polygenic minor genetic effects which can be used for both genome-wide assoiciation and prediction purposes. The statistical evaluations based on both simulated and real pig data set shows that the LAVA ADMM has better prediction properies than the LASSO, RR and EN. Julia code for the LAVA ADMM is available at:

2021 ◽  
Frauke Beyer ◽  
Katrin Horn ◽  
Stefan Frenzel ◽  
Edith Hofer ◽  
Maria J Knol ◽  

Introduction: Head motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion. Methods: We conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult. Results: No variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion. Conclusion: Based on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing.

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