邵謙之,姜毅,吳金雨

1. 溫州醫(yī)科大學(xué)基因組醫(yī)學(xué)研究院,溫州 325000;

2. 中國(guó)科學(xué)院北京生命科學(xué)研究院,北京 100101

隨著高通量測(cè)序技術(shù)(Next generation sequencing,NGS)的不斷發(fā)展,特別是隨著測(cè)序費(fèi)用的逐年降低以及數(shù)據(jù)分析流程的日趨成熟,全基因組測(cè)序(WGS)已經(jīng)成為疾病研究、臨床診斷中重要的手段[1,2]。研究者已經(jīng)運(yùn)用全基因組測(cè)序來(lái)檢測(cè)癌癥、孟德?tīng)栠z傳病、復(fù)雜疾病的致病突變和致病基因,取得了前所未有的科研成果[3]。本文就全基因組測(cè)序的數(shù)據(jù)分析及其在疾病研究和臨床診斷中的應(yīng)用進(jìn)行綜述。

1 全基因組測(cè)序的背景介紹

近年來(lái),隨著高通量測(cè)序技術(shù)的不斷發(fā)展與成熟,全基因組測(cè)序被應(yīng)用到了各種領(lǐng)域,尤其是在遺傳性疾病研究方面的應(yīng)用備受關(guān)注[1,2,4～6]。目前人類(lèi)已知的疾病中,大約有4000多種疾病與基因異常有關(guān)[7]。利用全基因組測(cè)序,可在全基因組水平上檢測(cè)與人類(lèi)疾病相關(guān)的單核苷酸變異(SNVs)、插入缺失(InDels)、拷貝數(shù)變異(CNV)和結(jié)構(gòu)變異(SV)等多種全面的突變信息,進(jìn)而找到致病突變并研發(fā)有效的治療藥物,為臨床用藥提供指導(dǎo)。

價(jià)格昂貴一直是全基因組測(cè)序發(fā)展的一個(gè)重大阻礙,然而隨著Hiseq X Ten的出現(xiàn),全基因組測(cè)序的成本已大幅下降,測(cè)序費(fèi)用僅需1000美元。Hiseq X Ten是由Illumina公司研發(fā)的有史以來(lái)最強(qiáng)大的測(cè)序平臺(tái),旨在提供大規(guī)模人類(lèi)基因組測(cè)序服務(wù)。它由10臺(tái)超高通量測(cè)序儀組成,每臺(tái)測(cè)序儀的產(chǎn)出效率是Hiseq 2000的12倍,每天可產(chǎn)出高達(dá)600 GB的數(shù)據(jù)量,全年可以完成約18000人次全基因組測(cè)序。數(shù)據(jù)分析速度慢則是全基因組發(fā)展的另一個(gè)難題,受數(shù)據(jù)量及分析軟件的限制,全基因組數(shù)據(jù)分析需要1 d以上。然而2014年7月,Dutch生物信息公司宣布開(kāi)發(fā)的Genalice Map軟件可以成功實(shí)現(xiàn)1 min比對(duì)人類(lèi)全基因組,并在將來(lái)的合作中繼續(xù)測(cè)試10000個(gè)人類(lèi)全基因組。此外,由Edico Genome開(kāi)發(fā)的生物科技處理器(Dynamic Read Analysis for Genomics,DRAGEN),作為全球首款新一代測(cè)序生物信息特殊應(yīng)用集成電路,可以將用于分析整個(gè)人類(lèi)基因組數(shù)據(jù)所需的24 h銳減為18 min,同時(shí)還確保了分析的準(zhǔn)確性。相信不久以后,其他分析步驟也將在幾分鐘內(nèi)完成。

盡管全基因組測(cè)序面臨著價(jià)格昂貴、數(shù)據(jù)分析速度慢等難題,但是由于其能檢測(cè)結(jié)構(gòu)變異以及非編碼區(qū)的SNVs、InDels等,目前在國(guó)內(nèi)已被應(yīng)用于一系列遺傳性疾病的研究。早在2003年,趙國(guó)屏課題組就利用全基因組測(cè)序分析鉤端螺旋體病[8]。此后,全基因組測(cè)序逐漸被應(yīng)用于肝癌[9]、膀胱癌[10]、胰腺癌[11]、腹膜間皮瘤[12]、自閉癥[13]等疾病致病機(jī)理的研究。謝曉亮課題組于2012年底利用其新近發(fā)明的MALBAC擴(kuò)增技術(shù)對(duì)一個(gè)亞洲男子的99個(gè)精子進(jìn)行單細(xì)胞全基因組DNA擴(kuò)增,首次實(shí)現(xiàn)了單個(gè)精子高覆蓋度的全基因組測(cè)序[14]。此外,該課題組還首次利用上述 MALBAC基因組擴(kuò)增高通量測(cè)序?qū)υ嚬軏雰哼M(jìn)行單基因遺傳病篩查,該嬰兒已于 2014年9月19日在北京大學(xué)第三醫(yī)院誕生,標(biāo)志著我國(guó)胚胎植入前遺傳診斷技術(shù)已處于世界領(lǐng)先水平。由此可見(jiàn),全基因組測(cè)序已成為現(xiàn)階段基因測(cè)序工作的重心。全基因組測(cè)序的時(shí)代已經(jīng)到來(lái),勢(shì)不可擋。

2 全基因組測(cè)序的數(shù)據(jù)分析流程

全基因組測(cè)序的數(shù)據(jù)分析流程包括質(zhì)量控制(Quality control)、比對(duì)(Mapping)、突變檢測(cè)(Call variant )、突變注釋(Annotation)。針對(duì)不同數(shù)據(jù)要求,已有多款分析軟件得以開(kāi)發(fā)(表1),目前廣泛使用的分析流程為“BWA+ GATK + ANNOVAR”(附圖 1)。

2.1 質(zhì)量控制

對(duì)測(cè)序產(chǎn)生的原始數(shù)據(jù)(Raw data)進(jìn)行去接頭、過(guò)濾低質(zhì)量處理,得到 Clean data的過(guò)程稱(chēng)為質(zhì)量控制。質(zhì)量控制能除去部分測(cè)序效果較差的序列,提高后續(xù)分析的準(zhǔn)確性。經(jīng)過(guò)該步驟通常會(huì)過(guò)濾掉5%～15%低質(zhì)量的序列。

2.2 比對(duì)到參考基因組

將質(zhì)量控制后的Clean data比對(duì)到參考基因組上,得到每條序列的比對(duì)位置、比對(duì)質(zhì)量值等信息。目前最主流的比對(duì)軟件為 BWA(Burrows-Wheeler Aligner)[18],它能將短序列準(zhǔn)確快速地比對(duì)到參考基因組上,生成通用的 SAM 格式的文件。自 2013年起 BWA發(fā)布了新算法 BWA MEM,可以比對(duì)70bp～1 Mb的序列,比原來(lái)的算法更加準(zhǔn)確,運(yùn)行速度也更加快[54]。

表1 全基因組數(shù)據(jù)分析常用軟件

2.3 突變檢測(cè)

比對(duì)好的SAM文件通常會(huì)轉(zhuǎn)換成BAM文件并進(jìn)行去重(Remove duplication),然后進(jìn)行突變的檢測(cè)。目前主流檢測(cè)SNV和InDel的軟件為Genome Analysis Toolkit (GATK,http://www.broadinstitute.org/gatk/),GATK準(zhǔn)確度非常高,它會(huì)對(duì)BAM文件進(jìn)行兩次校正過(guò)程以提高突變檢測(cè)的準(zhǔn)確率,但是速度比較慢。2014年 3月,Broad宣布最新版GATK(version3.1)在突變檢測(cè)速度上將比原來(lái)快3～5倍,使全基因組的分析時(shí)間從3 d縮短到1 d。

由于全基因組測(cè)序具有較好的均一性和覆蓋度,因此在 CNVs的檢測(cè)方面具有眾多優(yōu)勢(shì)。目前已經(jīng)發(fā)表了多種CNV的檢測(cè)方法與軟件,可以分為兩大類(lèi)別：(1)基于深度差異的檢測(cè)方法受測(cè)序局部不均一性的影響,往往假陽(yáng)性率比較高; (2)基于讀段對(duì)之間的距離檢測(cè) CNV的方法能相對(duì)準(zhǔn)確地找到斷點(diǎn)。若讀段對(duì)之間的距離明顯超過(guò)正常大小,就可以認(rèn)為這對(duì)讀段之間存在 CNV。另外,有些比對(duì)不上的讀段拆成兩條讀段后能分別比對(duì)到染色體上不同位置,這兩個(gè)位置之間也可能存在 CNV。廣義上的SVs包括CNVs和倒位、易位等多種類(lèi)別,因此SVs的檢測(cè)比 CNVs更為復(fù)雜,往往需要多款軟件結(jié)合使用,才能更準(zhǔn)確地找到可能的SVs。CNVs和SVs都需要通過(guò)Sanger測(cè)序?qū)帱c(diǎn)進(jìn)行驗(yàn)證才能最終確定,如果無(wú)法確定斷點(diǎn)的則需要通過(guò) qPCR驗(yàn)證。

最近,越來(lái)越多研究表明新生突變(de novo mutation)在散發(fā)性疾病中扮演重要的角色[55],特別是在神經(jīng)精神疾病中鑒定到一系列的致病基因[56,57]。因此,具有核心家系(例如：患者以及患者的父親與母親)的全基因組測(cè)序也開(kāi)始得到廣泛應(yīng)用。目前已經(jīng)開(kāi)發(fā)出了一系列的軟件與工具,這些軟件對(duì)多個(gè)樣品同時(shí)鑒定突變,并篩選出僅在患者出現(xiàn)突變。新生突變通常都是極端稀有,對(duì)散發(fā)性疾病具有重要作用。

2.4 注釋突變及預(yù)測(cè)致病基因

每一個(gè)全基因組的樣品,平均可以檢測(cè)到大約3000000個(gè)突變。為了篩選致病的候選突變并用于后續(xù)功能驗(yàn)證,需要通過(guò)諸如 ANNOVAR[37]等軟件對(duì)其進(jìn)行注釋。一方面,利用已知突變數(shù)據(jù)庫(kù)(如dbSNP139[58]、ESP6500[59]、1000 Genome[60]等),去除在數(shù)據(jù)庫(kù)中出現(xiàn)頻率較高的突變,并將剩下的突變注釋到基因組上的各個(gè)基因區(qū)間(如外顯子區(qū)、內(nèi)含子區(qū)、5′-UTR區(qū)或3′-UTR區(qū))和突變對(duì)蛋白質(zhì)編碼的改變情況(如錯(cuò)義突變、無(wú)義突變或移碼突變);另一方面,通過(guò)多個(gè)疾病數(shù)據(jù)庫(kù)(OMIM[48]、MGI[49]、Cosmic[50]、ClinVar[51]、HGMD[52]等)將部分已知突變與疾病表型聯(lián)系起來(lái),并利用多款預(yù)測(cè)軟件(如SIFT[61]、Polyphen[62]、GERP++[63]、LRT[64]等)對(duì)這些突變進(jìn)行有害性和保守型預(yù)測(cè),最終鑒定導(dǎo)致疾病發(fā)生的相關(guān)基因及突變。

隨著科研人員對(duì)遺傳性疾病的進(jìn)一步研究,發(fā)現(xiàn)在非編碼區(qū)域,特別是一些位于高度保守區(qū)域、啟動(dòng)子區(qū)域以及重要調(diào)控區(qū)域的突變對(duì)疾病的發(fā)生仍然具有不可替代的作用[65,66]。非編碼區(qū)的功能分析常用 FunSeq[53]軟件進(jìn)行。FunSeq過(guò)濾掉 1000 genomes中的突變后,根據(jù)突變是否在某些功能元件上、是否在敏感區(qū)域、是否中斷轉(zhuǎn)錄因子模體、靶標(biāo)基因是否已知及靶標(biāo)基因是否在網(wǎng)絡(luò)中心等對(duì)剩下的突變進(jìn)行打分,篩選出可能有害的突變。如果有多個(gè)樣本一起分析,FunSeq還可以判斷一個(gè)突變是否是頻發(fā)突變(Recurrent mutation)。另外,還需要充分利用ENCODE數(shù)據(jù)庫(kù)(http://genome.ucsc.edu/ENCODE/),里面包含了多種細(xì)胞系不同功能元件的注釋信息(如啟動(dòng)子、增強(qiáng)子、轉(zhuǎn)錄因子等),可以為非編碼的研究提供參考。

3 全基因組測(cè)序在疾病研究及臨床診斷中的應(yīng)用

全基因組測(cè)序給疾病研究以及致病基因的篩選帶來(lái)了前所未有的機(jī)遇。近年來(lái),通過(guò)全基因組測(cè)序方法,已在孟德?tīng)栠z傳病、癌癥等疾病中鑒定到了一系列的致病突變和基因,已經(jīng)成為致病基因鑒定和臨床診斷的重要手段之一[4,7]。

3.1 在癌癥中的應(yīng)用

癌癥是指細(xì)胞的生長(zhǎng)與增生不受機(jī)體控制,從而引起機(jī)體功能受損的一類(lèi)疾病。癌細(xì)胞的基因組缺乏穩(wěn)定性,容易發(fā)生各種突變,進(jìn)而改變細(xì)胞功能,使患者產(chǎn)生一系列的臨床癥狀。高通量測(cè)序技術(shù)特別是全基因組測(cè)序?qū)Π┌Y中體細(xì)胞突變的鑒定,疾病的診斷與治療提供了最直接有效的方法之一,并得到了廣泛應(yīng)用。通過(guò)全基因組測(cè)序,許多癌癥已經(jīng)被廣泛研究,并取得了一系列的研究成果。Pleasance等[67]在 2010年首次通過(guò)全基因組測(cè)序得到了黑色素瘤的全基因組突變譜。他們發(fā)現(xiàn),黑色素瘤的體細(xì)胞突變?cè)诨蚪M上面不均一分布,絕大部分的突變都是C>T/G>A這種類(lèi)型,而這些突變絕大部分發(fā)生在CpC/GpG上面。產(chǎn)生這種特異突變普的原因可能是黑色素瘤患者長(zhǎng)期暴露于紫外線照射中。 Pleasance等[68]采用全基因組測(cè)序技術(shù),在小細(xì)胞肺癌中卻發(fā)現(xiàn) G>T/C>A轉(zhuǎn)換在所有突變中占主要部分,并且更傾向于發(fā)生在 CpG上面,揭示這種特殊的突變譜可能與患者的長(zhǎng)期吸煙有關(guān)。Lee等[69]對(duì)肺癌進(jìn)行全基因組測(cè)序卻發(fā)現(xiàn) C>T/G>A轉(zhuǎn)換占突變的比例最高,并且富集于 CpG上面,暗示可能同甲基化的脫氨基作用有關(guān)。由于不同癌癥具有不同的發(fā)病機(jī)理,因此可能會(huì)表現(xiàn)出不同的突變譜。全基因組測(cè)序提供了最直接有效、無(wú)偏向性地的方法系統(tǒng)分析癌癥突變譜,為深入了解致病機(jī)理提供指導(dǎo)。

不同癌癥不但具有特異的突變譜,同時(shí)還具有不同的突變頻率,差距可能達(dá)到1000倍以上[70]。橫紋肌樣瘤的突變頻率最小,每一Mb區(qū)域約發(fā)生0.1個(gè)突變; 然而黑色素瘤的突變頻率最高,達(dá)到100/Mb。研究表明,組織差異性可能是造成突變頻率差異最直接的原因,而且受較大外界壓力(如吸煙、紫外線照射等)的癌癥通常具有較高的突變頻率。另外,同一種癌癥的不同患者攜帶的突變數(shù)量同樣具有很大的差異性。例如,在黑色素瘤和肺癌中,突變頻率最少的樣品只有 0.1/Mb,而突變頻率最高的樣品卻達(dá)到100/Mb以上。盡管如此,研究者們使用全基因組技術(shù),從 SNVs、InDels、CNVs和 SVs等多個(gè)角度尋找致病突變,找到一系列可復(fù)制的致病基因。Puente等[71]對(duì) 4對(duì)慢性淋巴細(xì)胞性白血病(Chronic lymphocytic leukaemia)樣品進(jìn)行全基因組測(cè)序,鑒定到46個(gè)對(duì)蛋白功能有害的突變。大樣本量驗(yàn)證后發(fā)現(xiàn) 4個(gè)基因(NOTCH1、XPO1、MYD88和KLHL6)攜帶復(fù)發(fā)突變。Roberts等[72]對(duì)15例急性淋巴細(xì)胞白血病樣品進(jìn)行全基因組測(cè)序,在多個(gè)基因(ABL1、JAK2、PDGFRB、CRLF2 和 EPOR)中發(fā)現(xiàn)了結(jié)構(gòu)變異,同時(shí)在 IL7R、FLT3和 SH2B3基因中鑒定到多個(gè)害突變。對(duì)這些基因的功能進(jìn)行深入分析后發(fā)現(xiàn),體細(xì)胞突變減弱了相應(yīng)蛋白同絡(luò)氨酸激酶抑制劑的結(jié)合,因此與絡(luò)氨酸酶抑制劑相關(guān)的藥物對(duì)這些患者的定向治療將具有重要臨床指導(dǎo)意義。最近,Wang等[73]使用全基因組測(cè)序技術(shù),對(duì)100對(duì)胃癌樣品進(jìn)行全面分析,包括編碼區(qū)域和非編碼區(qū)域的點(diǎn)突變、插入缺失、拷貝數(shù)變異、結(jié)構(gòu)變異、基因表達(dá)以及甲基化圖譜,成功鑒定已知的胃癌致病基因(TP53、ARID1A 和 CDH1)以及新的胃癌致病基因(MUC6、 CTNNA2、GLI3和RNF43等)。通過(guò)全基因組測(cè)序,已經(jīng)在白血病[71,72,74]、黑色素瘤[75]、腦膜瘤[76]、乳腺癌[77]、成神經(jīng)管細(xì)胞瘤[78]、腎癌[79]、小細(xì)胞肺癌[80]、結(jié)腸癌[81]和甲狀腺癌[82]等多種癌癥中鑒定到一系列的致病突變和基因。

由于全基因組測(cè)序?qū)Y(jié)構(gòu)變異與非編碼區(qū)變異的檢測(cè)具有無(wú)可比擬的優(yōu)勢(shì),該技術(shù)已經(jīng)全面應(yīng)用于癌癥領(lǐng)域,使得科研工作者對(duì)癌癥的發(fā)生發(fā)展有更深入的了解。隨著測(cè)序成本的降低以及數(shù)據(jù)分析手段的發(fā)展,更多的癌癥和樣品將被測(cè)序,并鑒定到一系列有可重復(fù)的致病基因。為了更好的研究癌癥,科研工作者們已經(jīng)成立了國(guó)際基因組聯(lián)盟(International Cancer Genome Consortium,ICGC),到目前為止該聯(lián)盟已經(jīng)公布了超過(guò)10000個(gè)癌癥基因組數(shù)據(jù)。全基因組測(cè)序已經(jīng)成為癌癥研究的工作重心,有益于系統(tǒng)分析致病基因參與的分子通路,將為臨床用藥提供最有效依據(jù),使得癌癥的治愈也將成為可能。

3.2 在神經(jīng)與精神疾病中的應(yīng)用

全基因組測(cè)序技術(shù)不僅在癌癥等疾病中得以應(yīng)用,也逐步被應(yīng)用到其他常見(jiàn)遺傳病中,尤其是神經(jīng)與精神疾病。全基因組測(cè)序在結(jié)構(gòu)變異的鑒定方面存在無(wú)可比擬的優(yōu)勢(shì),可以準(zhǔn)確的找到斷點(diǎn)位置,精確定位致病基因。Talkowski等[83]對(duì)具有神經(jīng)發(fā)育障礙的患者進(jìn)行全基因組測(cè)序并鑒定到 33個(gè)區(qū)域。這些區(qū)域的致病基因可以歸類(lèi)為4種類(lèi)別：(1)已知的致病基因(AUTS2、FOXP1和CDKL5); (2)單個(gè)基因的區(qū)域(SATB2、EHMT1); (3)新的候選基因與區(qū)域(CHD8、KIRREL3和ZNF507); (4)同其他神經(jīng)精神疾病相關(guān)的基因(TCF4、ZNF804A、PDE10A、GRIN2B和ANK3)。他們的研究表明多個(gè)基因可能共同作用,并產(chǎn)生多種多樣的表型。Michaelson等[33]在2012年對(duì)10個(gè)自閉癥譜系障礙(Autism spectrum disorder,ASD)核心家系(患者以及正常的父母親)進(jìn)行全基因組測(cè)序。分析發(fā)現(xiàn)新生突變?cè)谌蚪M范圍內(nèi)的分布不是隨機(jī)的,而是存在一定的熱點(diǎn)區(qū)域,而且這些熱點(diǎn)區(qū)域同疾病具有重要的關(guān)系。他們還發(fā)現(xiàn)基因組不同區(qū)域的突變速速率同基因組中的多種因素(如 CG含量、復(fù)制時(shí)間、轉(zhuǎn)錄水平和敏感位點(diǎn)等)存在一定聯(lián)系。最終他們提出了一種回歸模型,引入上面多種因素,可以準(zhǔn)確地計(jì)算自閉癥患者在基因組不同區(qū)域的突變速率,為熱點(diǎn)區(qū)域的鑒定提供參考和依據(jù)。同時(shí)他們還發(fā)現(xiàn)公共數(shù)據(jù)庫(kù)中的致病基因,不管是顯性遺傳還是隱性遺傳都具有較高的突變速率。此外, Kong等[84]在全基因組水平證明新生突變的個(gè)數(shù)與父親的年齡存在著顯著的關(guān)系,而不是母親的年齡。而且父親的年齡每增加一歲,小孩攜帶的平均新生突變個(gè)數(shù)將增加兩個(gè),從而增加了患神經(jīng)精神疾病的風(fēng)險(xiǎn)。

科研人員通過(guò)全基因組測(cè)序不但揭示了突變發(fā)生的一些本質(zhì)規(guī)律,同時(shí)還有效地鑒定了一系列致病基因。Jiang等[13]對(duì)32個(gè)自閉癥(ASD)核心家系進(jìn)行全基因組測(cè)序,最大可能地將臨床表型同遺傳變異聯(lián)系起來(lái),從新生突變、稀有遺傳變異等多個(gè)角度進(jìn)一步解釋ASD的發(fā)病機(jī)理。他們的研究鑒定到一系列與 ASD 相關(guān)的致病基因,包括 CAPRIN1、AFF2、VIP、SCN2A、KCNQ2和 CHD7等。針對(duì)ASD這類(lèi)具有高度異質(zhì)性的遺傳病,全基因組測(cè)序能夠更有效地鑒定致病突變與基因。最近,Nature雜志發(fā)表了對(duì) 50個(gè)智力殘疾的核心家系進(jìn)行全基因組測(cè)序的研究,鑒定到84個(gè)在編碼區(qū)域的新生突變,以及8個(gè)新生CNVs[5]。得力于與全基因測(cè)序的高覆蓋度和均一性,能夠?qū)?62%的患者進(jìn)行臨床診斷,找到明確的致病基因,充分肯定了全基因組測(cè)序的重要意義。

全基因組測(cè)序在神經(jīng)精神疾病的運(yùn)用才剛剛開(kāi)始,更多的基因組測(cè)序?qū)⒈煌瓿?。例?中美科研機(jī)構(gòu)將合作完成“萬(wàn)人自閉癥基因組研究計(jì)劃”。 這個(gè)項(xiàng)目有助于更全面地了解、發(fā)現(xiàn)絕大多數(shù)自閉癥兒童患病原因,并能應(yīng)用于對(duì)自閉癥兒童的早期臨床診斷和家庭的產(chǎn)前篩查,最終了解自閉癥的發(fā)病機(jī)理并開(kāi)發(fā)出有效治療方法?？傊?全基因組測(cè)序?qū)⒃谏窠?jīng)精神疾病中得到更為廣泛的應(yīng)用。

3.3 在臨床診斷中的應(yīng)用

全基因組測(cè)序技術(shù)不僅在疾病致病基因的研究中扮演著重要的角色,它還廣泛地應(yīng)用于臨床上一些疾病的診斷、篩查,為疾病的預(yù)防以及治療提供依據(jù)。目前,大多數(shù)的產(chǎn)前診斷都是基于有創(chuàng)性的侵入檢查手段,如羊膜腔穿刺術(shù)、胎兒臍帶血穿刺等。這種侵入性技術(shù)對(duì)孕婦以及嬰兒都存在一定的傷害,甚至可能導(dǎo)致流產(chǎn)[85,86]。侵入性產(chǎn)前診斷通過(guò)分析母親血樣中的胎兒DNA,避免了穿刺損失、感染和流產(chǎn)的風(fēng)險(xiǎn),減輕了孕婦的精神壓力,易為廣大孕婦和家屬接受。 目前,全基因組測(cè)序已在無(wú)創(chuàng)產(chǎn)前診斷(Non-invasive prenatal testing,NIPT)領(lǐng)域顯現(xiàn)雛形。一方面,可通過(guò)全基因組測(cè)序技術(shù),非侵入性檢查染色體非整倍異常,為 21三體綜合征、18三體綜合征等的準(zhǔn)確診斷提供了一個(gè)有效的解決方案。Lau等[87]通過(guò)全基因組測(cè)序技術(shù)對(duì) 5例孕婦進(jìn)行無(wú)創(chuàng)產(chǎn)前診斷,并在 4例中發(fā)現(xiàn)了染色體異常,準(zhǔn)確診斷出21三體綜合征、18三體綜合征等。Lau等[2]通過(guò)全基因組測(cè)序?qū)?1982例樣品的游離 DNA進(jìn)行分析,并證明即使深度較低(0.1x),全基因組測(cè)序也能檢測(cè)到染色體結(jié)構(gòu)變異,對(duì)常見(jiàn)的三體綜合征做出準(zhǔn)確的診斷。另一方面,還可通過(guò)全基因組測(cè)序,非侵入性診斷諸如癌癥等基因異常性疾病。Leary等[88]對(duì) 10例結(jié)直腸癌、乳腺癌樣品以及 10例正常人進(jìn)行全基因組測(cè)序,在 ERBB2基因和CDK6基因找到了染色體拷貝數(shù)變異和重排,并證明可以不依賴(lài)活組織檢查而進(jìn)行無(wú)創(chuàng)診斷。

隨著高通量測(cè)序技術(shù)的發(fā)展,以高通量、自動(dòng)化、高準(zhǔn)確度為顯著特征的第二代測(cè)序技術(shù)(NGS)已被成熟地運(yùn)用于一些疾病的診斷和篩查。其中,基于全外顯子測(cè)序技術(shù)的基因診斷已成功地對(duì)先天性氯腹瀉[89]、新生兒糖尿病[90]、難治性炎性腸病[91]和 Charcot-Marie-Tooth atrophy綜合征[92]等疾病進(jìn)行分子水平的診斷。但是由于該技術(shù)對(duì)結(jié)構(gòu)變異與非編碼區(qū)變異的研究具有局限性,在一些由結(jié)構(gòu)變異或非編碼區(qū)變異所導(dǎo)致的復(fù)雜疾病(如 21三體綜合征)面前則力不能及。

4 全基因組測(cè)序數(shù)據(jù)分析面臨的挑戰(zhàn)

盡管全基因組測(cè)序能夠有效地挖掘全基因組范圍內(nèi)的多種變異,為遺傳性疾病的研究以及臨床診斷提供極大便利。但是,由于下機(jī)數(shù)據(jù)量的巨大增加給全基因組測(cè)序的數(shù)據(jù)分析帶來(lái)巨大挑戰(zhàn)。(1)數(shù)據(jù)存儲(chǔ)：一個(gè)標(biāo)準(zhǔn)的全基因數(shù)據(jù)通常在100 GB左右,再加上分析得到的clean data、BAM文件、SAM文件以及突變結(jié)果文件,一個(gè)全基因組數(shù)據(jù)往往還需要額外300 GB的存儲(chǔ)空間。例如,100個(gè)標(biāo)本的全基因組數(shù)據(jù),完成所有數(shù)據(jù)分析至少需要 30 TB以上的存儲(chǔ)空間。(2)數(shù)據(jù)分析效率：如此巨大的數(shù)據(jù)將給數(shù)據(jù)分析效率以及服務(wù)器的運(yùn)算性能帶來(lái)巨大的挑戰(zhàn)。數(shù)據(jù)分析過(guò)程中往往需要使用多線程,同時(shí)還需要將數(shù)據(jù)分成多份同時(shí)運(yùn)算,以加快數(shù)據(jù)分析效率。(3)篩選致病變異：通常情況下,通過(guò)全基因組測(cè)序?qū)⒎治龅玫酱蠹s 3000000個(gè) SNV以及InDels,如何從如此眾多的突變中,特別是非編碼區(qū)域重要調(diào)控原件中尋找致病突變成為亟待解決的問(wèn)題。與此同時(shí),還有可能找到多個(gè)CNV/SV,如何確定這些變異對(duì)疾病的貢獻(xiàn)也存在巨大挑戰(zhàn)。(4)CNV/SV鑒定的準(zhǔn)確率：盡管目前發(fā)表了多款基于全基因組測(cè)序鑒定 CNV/SV的方法與工具,但是準(zhǔn)確率都不高,同時(shí)還存在一定的假陰性。盡管如此,在闡明疾病的發(fā)病機(jī)理時(shí),全基因組測(cè)序在疾病的基因診斷和致病基因的研究中仍具有不可替代的作用。

5 展 望

目前,全基因組測(cè)序技術(shù)已在疾病研究和臨床診斷中得到日益廣泛的應(yīng)用,特別是對(duì)妊娠過(guò)程中母體血漿中存在游離的胎兒DNA (Fetal DNA)[93]通過(guò)全基因組測(cè)序進(jìn)行無(wú)創(chuàng)產(chǎn)前診斷。另一方面,隨著大數(shù)據(jù)時(shí)代的來(lái)臨,為了使大數(shù)據(jù)能夠得到更快的分析和更有效的利用,全基因組測(cè)序必然向著數(shù)據(jù)的云存儲(chǔ)、云計(jì)算等方向發(fā)展。(1)云存儲(chǔ)：基于分布式原理存儲(chǔ)高通量數(shù)據(jù),極大地降低數(shù)據(jù)分析時(shí)輸入、輸出和中間數(shù)據(jù)量,從而加快數(shù)據(jù)分析速度,在相同的時(shí)間里處理更多的測(cè)序數(shù)據(jù); (2)云計(jì)算：開(kāi)發(fā)基于并行原理的生物信息學(xué)軟件,并行地處理高通量數(shù)據(jù),提高數(shù)據(jù)分析過(guò)程中每個(gè)步驟的效率,充分利用計(jì)算資源,從而消耗資源更少,數(shù)據(jù)分析更迅速; (3)測(cè)序與分析一體化：即把高通量測(cè)序與后續(xù)數(shù)據(jù)分析相結(jié)合,下機(jī)得到的數(shù)據(jù)不僅有測(cè)序結(jié)果,還有檢測(cè)到的各種突變(SNVs、InDels、CNVs或 SVs),并與云存儲(chǔ)的疾病數(shù)據(jù)庫(kù)相關(guān)聯(lián),以預(yù)估病人各種遺傳疾病的風(fēng)險(xiǎn)。目前已有相關(guān)的工具得以開(kāi)發(fā)如 MegaSeq[94],利用全基因組測(cè)序進(jìn)行疾病的基因診斷和致病基因的研究將是一個(gè)非常有前景的領(lǐng)域。又如,biobambam[95]可以在不損害比對(duì)重要信息的情況下,對(duì) BAM 文件進(jìn)行大幅度壓縮100倍以上,極大地縮減了數(shù)據(jù)存儲(chǔ)空間。最近,基因組學(xué)重要軟件 SAMtools也在基因組數(shù)據(jù)量的快速上升的背景下進(jìn)行了重要升級(jí),最新版本支持壓縮和全球共享數(shù)據(jù)。總而言之,全基因組測(cè)序的時(shí)代已經(jīng)到來(lái),將會(huì)在遺傳性疾病的研究和臨床診斷中發(fā)揮更重要的作用。

附錄

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