Diversity and association analysis of important agricultural trait based on miniature inverted-repeat transposable element specific marker in Brassica napus L.

Miniature inverted-repeat transposable elements(MITEs)are a group of DNA transposable element(TE)which preferentially distributed with gene associated regions.Tens of MITEs families have been revealed in Brassica napus genome,they scatter across the genome with tens of thousands copies and produce polymorphisms both intra-and inter-species.Our previous studies revealed a Tourist-like MITE,Monkey King,associated with vernalization requirement of B.napus,however there are still few studies reveal MITE association with agricultural traits in B.napus.In the present study,80 polymorphic markers were developed from 55 MITEs,and used to evaluate genetic diversity in a panel of B.napus accessions consisting of 101 natural and 25 synthetic genotypes.Five agricultural traits,oil content,glucosinolate content,erucic acid content,weight of thousand seeds(WTS)and plant height,were investigated across 3-years field experiments,in addition,two traits,hypocotyl length and root length,were evaluated at the 4-leaf stage in the laboratory.Correlations between the MITE-based markers and seven traits were analyzed,finally,10 polymorphic markers produced by 6 pairs of MITE specific primers were revealed relatively high correlation with 5 traits.Two polymorphic markers were anchored with two candidate genes,BnaA02g13530D and BnaA08g20010D,respectively,which may contribute to glucosinolate content and WTS.This research may contribute to genetic improvement through utilization of MITE-induced polymorphisms in Brassica species.

中国汉族人近期基因组及表型进化特征

了解人类进化过程中经历的正选择,可以洞察塑造我们物种的事件,以及今天继续困扰我们的疾病.作为人口最多的民族,汉族在其特定的历史、地理和文化环境中一直在经历基因选择.本文通过对3946个中国汉族人高深度全基因组测序数据进行分析,鉴定了24个近期选择的基因组区域,包括免疫相关基因区(MHC cluster、IGH cluster、STING1、PSG)、酒精代谢相关基因区(ADH1B、ALDH2、ALDH3B2)和嗅觉感知基因OR4C16.其中MHC cluster、ADH1B和ALDH2也被TOPMed和西湖生物库WBBC鉴定.在这些信号中,IGH cluster值得关注,其中变异14105737776CT(rs117518546,IgG1-G396R)的优势等位基因可促进免疫反应,但也增加了自身免疫性疾病系统性红斑狼疮(SLE)的风险.另外,本研究新发现的ALDH3B2的进化方向对酒精代谢的影响与ALDH2的相反.除了单基因性状,本文发现多个复杂性状经历了多基因适应;特别是,多种方法一致表明,在自然选择中,较低的血压更受青睐.最后建立了一个名为RePoS的数据库(http://bigdata.ibp.ac.cn/RePoS/)以集成和显示多种群选择信号.综上,本研究扩展了我们对汉族和其他人群的自然进化和表型适应的理解.

Modeling the epidemic dynamics and control of COVID-19 outbreak in China

Background:The coronavirus disease 2019(COVID-19)is rapidly spreading in China and more than 30 countries over last two months.COVID-19 has multiple characteristics distinct from other infectious diseases,including high infectivity during incubation,time delay between real dynamics and daily observed number of confirmed cases,and the intervention effects of implemented quarantine and control measures.Methods:We develop a Susceptible,Un-quanrantined infected,Quarantined infected,Confirmed infected(SUQC)model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures,which is more suitable for analysis than other existing epidemic models.Results:The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan,Hubei(excluding Wuhan),China(excluding Hubei)and four first-tier cities of China.We found that,before January 30,2020,all these regions except Beijing had a reproductive number R>\,and after January 30,all regions had a reproductive number/?<1,indicating that the quarantine and control measures are effective in preventing the spread of COVID-19.The confirmation rate of Wuhan estimated by our model is 0.0643,substantially lower than that of Hubei excluding Wuhan(0.1914),and that of China excluding Hubei(0.2189),but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines.The number of unquarantined infected cases in Wuhan on February 12,2020 is estimated to be 3,509 and declines to 334 on February 21,2020.After fitting the model with data as of February 21,2020,we predict that the end time of COVID-19 in Wuhan and Hubei is around late March,around mid March for China excluding Hubei,and before early March 2020 for the four tier-one cities.A total of 80,511 individuals are estimated to be infected in China,among which 49,510 are from Wuhan,17,679 from Hubei(excluding Wuhan),and the rest 13,322 from other regions of China(excluding Hubei).Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty.Conclusions:We suggest that rigorous quarantine and control measures should be kept before early March in Beijing,Shanghai,Guangzhou and Shenzhen,and before late March in Hubei.The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak,such as South Korea,Japan,Italy and Iran.

Population Genetics of SARS-CoV-2: Disentangling Effects of Sampling Bias and Infection Clusters

A novel RNA virus,the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is responsible for the ongoing outbreak of coronavirus disease 2019(COVID-19).Population genetic analysis could be useful for investigating the origin and evolutionary dynamics of COVID-19.However,due to extensive sampling bias and existence of infection clusters during the epidemic spread,direct applications of existing approaches can lead to biased parameter estimations and data misinterpretation.In this study,we first present robust estimator for the time to the most recent common ancestor(TMRCA)and the mutation rate,and then apply the approach to analyze 12,909 genomic sequences of SARS-CoV-2.The mutation rate is inferred to be 8.69×10^(−4) per site per year with a 95%confidence interval(CI)of[8.61×10^(−4),8.77×10^(−4)],and the TMRCA of the samples inferred to be Nov 28,2019 with a 95%CI of[Oct 20,2019,Dec 9,2019].The results indicate that COVID-19 might originate earlier than and outside of Wuhan Seafood Market.We further demonstrate that genetic polymorphism patterns,including the enrichment of specific haplotypes and the temporal allele frequency trajectories generated from infection clusters,are similar to those caused by evolutionary forces such as natural selection.Our results show that population genetic methods need to be developed to efficiently detangle the effects of sampling bias and infection clusters to gain insights into the evolutionary mechanism of SARS-CoV-2.Software for implementing VirusMuT can be downloaded at https://bigd.big.ac.cn/biocode/tools/BT007081.

Will the large-scale vaccination succeed in containing the COVID-19 pandemic and how soon?

Background:The availability of vaccines provides a promising solution to contain the COVID-19 pandemic.However,it remains unclear whether the large-scale vaccination can succeed in containing the COVID-19 pandemic and how soon.We developed an epidemiological model named SUVQC(Suceptible-Unquarantined-Vaccined-Quarantined-Conflrmed)to quantitatively analyze and predict the epidemic dynamics of COVID-19 under vaccination.Methods:In addition to the impact of non-pharmaceutical interventions(NPIs),our model explicitly parameterizes key factors related to vaccination,including the duration of immunity,vaccine efficacy,and daily vaccination rate etc.The model was applied to the daily reported numbers of confirmed cases of Israel and the USA to explore and predict trends under vaccination based on their current epidemic statuses and intervention measures.We further provided a formula for designing a practical vaccination strategy,which simultaneously considers the effects of the basic reproductive number of COVID-19,intensity of NPIs,duration of immunological memory after vaccination,vaccine efficacy and daily vaccination rate.Results:In Israel,53.83%of the population is fully vaccinated,and under the current NPI intensity and vaccination scheme,the pandemic is predicted to end between May 14,2021,and May 16,2021,assuming immunity persists for 180 days to 365 days.If NPIs are not implemented after March 24,2021,the pandemic will end later,between July 4,2021,and August 26,2021.For the USA,if we assume the current vaccination rate(0.268%per day)and intensity of NPIs,the pandemic will end between January 20,2022,and October 19,2024,assuming immunity persists for 180 days to 365 days.However,assuming immunity persists for 180 days and no NPIs are implemented,the pandemic will not end and instead reach an equilibrium state,with a proportion of the population remaining actively infected.Conclusions:Overall,the daily vaccination rate should be decided according to vaccine efficacy and immunity duration to achieve herd immunity.In some situations,vaccination alone cannot stop the pandemic,and NPIs are necessary to supplement vaccination and accelerate the end of the pandemic.Considering that vaccine efficacy and duration of immunity may be reduced for new mutant strains,it is necessary to remain cautiously optimistic about the prospect of,ending the pandemic under vaccination.

Genomic Epidemiology of SARS-CoV-2 in Pakistan

COVID-19 has swept globally and Pakistan is no exception.To investigate the initial introductions and transmissions of the SARS-CoV-2 in Pakistan,we performed the largest genomic epidemiology study of COVID-19 in Pakistan and generated 150 complete SARS-CoV-2 genome sequences from samples collected from March 16 to June 1,2020.We identified a total of 347 mutated positions,31 of which were over-represented in Pakistan.Meanwhile,we found over 1000 intra-host single-nucleotide variants(iSNVs).Several of them occurred concurrently,indicating possible interactions among them or coevolution.Some of the high-frequency iSNVs in Pakistan were not observed in the global population,suggesting strong purifying selections.The genomic epidemiology revealed five distinctive spreading clusters.The largest cluster consisted of 74 viruses which were derived from different geographic locations of Pakistan and formed a deep hierarchical structure,indicating an extensive and persistent nation-wide transmission of the virus that was probably attributed to a signature mutation(G8371T in ORF1ab)of this cluster.Furthermore,28 putative international introductions were identified,several of which are consistent with the epidemiological investigations.In all,this study has inferred the possible pathways of introductions and transmissions of SARS-CoV-2 in Pakistan,which could aid ongoing and future viral surveillance and COVID-19 control.

Ongoing Positive Selection Drives the Evolution of SARS-CoV-2 Genomes

SARS-CoV-2 is a new RNA virus affecting humans and spreads extensively throughout the world since its first outbreak in December,2019.Whether the transmissibility and pathogenicity of SARS-CoV-2 in humans after zoonotic transfer are actively evolving,and driven by adaptation to the new host and environments is still under debate.Understanding the evolutionary mechanism underlying epidemiological and pathological characteristics of COVID-19 is essential for predicting the epidemic trend,and providing guidance for disease control and treatments.Interrogating novel strategies for identifying natural selection using within-species polymorphisms and 3,674,076 SARSCoV-2 genome sequences of 169 countries as of December 30,2021,we demonstrate with population genetic evidence that during the course of SARS-CoV-2 pandemic in humans,1)SARS-CoV-2 genomes are overall conserved under purifying selection,especially for the 14 genes related to viral RNA replication,transcription,and assembly;2)ongoing positive selection is actively driving the evolution of 6 genes(e.g.,S,ORF3a,and N)that play critical roles in molecular processes involving pathogen–host interactions,including viral invasion into and egress from host cells,and viral inhibition and evasion of host immune response,possibly leading to high transmissibility and mild symptom in SARS-CoV-2 evolution.According to an established haplotype phylogenetic relationship of 138 viral clusters,a spatial and temporal landscape of 556 critical mutations is constructed based on their divergence among viral haplotype clusters or repeatedly increase in frequency within at least 2 clusters,of which multiple mutations potentially conferring alterations in viral transmissibility,pathogenicity,and virulence of SARS-CoV-2 are highlighted,warranting attention.

Pinpointing the animal origins of SARS-CoV-2:a genomic approach

Understanding the origins of zoonoses is critical to developing the means to prevent zoonotic spillover into the future.Like 60% of emerging diseases in humans(Jones et al.,2008),SARS-CoV-2 is likely to have zoonotic origins with possible immediate hosts(Holmes et al.,2021;Wang et al.,2021).Determination of the origin of SARS-CoV-2,that is,when,where,and how it emerges in humans through possible zoonotic transfer,facilitates prevention of the emergence and establishment of new zoonotic diseases.