基于高通量測序的全基因組關(guān)聯(lián)研究策略

2014-05-10 01:25:04周家蓬裴智勇陳禹保陳潤生

遺傳 2014年11期

周家蓬，裴智勇,2，陳禹保，陳潤生

1. 北京市計(jì)算中心,北京 100094；

2. 中國科學(xué)院北京基因組研究所，北京 100101；

3. 中國科學(xué)院生物物理研究所，北京 100101

全基因組關(guān)聯(lián)研究(Genome-wide association study,GWAS)是對(duì)多個(gè)個(gè)體在全基因組范圍的遺傳變異多態(tài)性進(jìn)行檢測，獲得基因型，進(jìn)而將基因型與可觀測的性狀，即表型，進(jìn)行群體水平的統(tǒng)計(jì)學(xué)分析，根據(jù)統(tǒng)計(jì)量或P值篩選出最有可能影響該性狀的遺傳變異。GWAS的主要方法學(xué)依據(jù)是歸納法中的共變法，是探究復(fù)雜因果關(guān)系的最主要的思想和方法。因此，GWAS特別適用于遺傳機(jī)理不明的復(fù)雜疾病或性狀。得到高密度、高可信的遺傳變異是GWAS的基礎(chǔ)，這些遺傳變異包括單核苷酸多態(tài)性(Single nucleotide polymorphisms,SNP)、插入缺失(Insertion and deletion,InDel)及拷貝數(shù)變異(Copy number variation,CNV)等，其中最主要的是SNP，占標(biāo)記總量的90%以上。目前獲得高密度SNP的方法主要是SNP芯片(Array)，因其高效、易用、廉價(jià)等特點(diǎn)在近些年被廣泛使用。而第二代測序技術(shù)(Next-generation sequencing,NGS)的快速發(fā)展，為GWAS的相關(guān)研究工作提供了新的技術(shù)和思路。NGS可以獲得大量低頻甚至稀有的遺傳變異，一些無法由芯片的高頻或常規(guī)變異(即 MAF ≥ 5%的變異)檢測到的表型關(guān)聯(lián)，有望通過基于NGS的GWAS方法得到有意義的結(jié)果。本文對(duì)基于高通量測序的GWAS的原理、策略及相關(guān)研究進(jìn)展進(jìn)行了闡述，并對(duì)其如何應(yīng)用于個(gè)體化醫(yī)療(Personalized medicine,PM)進(jìn)行了展望。

1 GWAS概述

GWAS的研究思路最早于1996 年由Risch等[1]提出，目的是將人類復(fù)雜疾病的研究從候選基因轉(zhuǎn)向全基因組水平，以期用更大規(guī)模的檢測得到與疾病相關(guān)的每一個(gè)基因。近年來，已有多篇報(bào)道對(duì)GWAS研究進(jìn)展進(jìn)行了綜述[2～4]。多年來，在人類疾病相關(guān)研究中，關(guān)于年齡相關(guān)性黃斑變性[5]、冠心病[6,7]、皮膚病[8,9]、2 型糖尿病[10～13]、癌癥[14,15]、精神分裂癥[16～18]、阿爾茨海默氏癥[19]等 GWAS成果相繼被報(bào)道。據(jù)NHGRI GWA Catalog網(wǎng)站(www.genome.gov/GWAStudies)的統(tǒng)計(jì)，截至 2013年底，在人類疾病或重要性狀研究方面，共有1778篇高質(zhì)量的GWAS研究工作被收錄，累計(jì)發(fā)現(xiàn)12123個(gè)SNP位點(diǎn)，影響癌癥、心血管系統(tǒng)、免疫系統(tǒng)、神經(jīng)系統(tǒng)等17大類800多種重大疾病或其相關(guān)性狀。GWAS不僅在人類醫(yī)學(xué)研究中被廣泛應(yīng)用，在動(dòng)植物遺傳選育等方面也在逐漸興起。在動(dòng)物育種方面，利用GWAS方法研究了奶牛[20～24]、豬[25,26]、肉雞[27,28]等經(jīng)濟(jì)動(dòng)物的數(shù)量性狀。GWAS在植物上也有較多報(bào)道，如玉米的開花時(shí)間[29]、葉片結(jié)構(gòu)[30]、抗枯萎病[31,32]，還有水稻的十幾種農(nóng)藝性狀的研究[33]等。以上研究主要采用基于芯片的GWAS方法。

2 基于NGS的GWAS

2.1 “合成關(guān)聯(lián)”假設(shè)

傳統(tǒng)GWAS是基于“Common disease-common variation”(CD-CV)的假設(shè)，該假設(shè)比較符合諸如“身高”等數(shù)量性狀的遺傳模式。Allen等[34]收集了約18萬例樣本進(jìn)行分析，發(fā)現(xiàn)了180個(gè)與身高顯著關(guān)聯(lián)的基因座(Loci)在生物學(xué)通路上高度富集，并且這些基因均與骨骼生長缺陷有關(guān)，累計(jì)可解釋約10%～16%的表型變異。另一種觀點(diǎn)認(rèn)為，一個(gè)常見變異的“致病”效應(yīng)可能是從一個(gè)稀有變異的致病效應(yīng)“稀釋”而來，即“Common disease-rare variation”(CD-RV)假設(shè)。Dickson等[35]首次提出“合成關(guān)聯(lián)”(Synthetic association)的概念來描述這一現(xiàn)象，利用模擬數(shù)據(jù)證實(shí)合成關(guān)聯(lián)普遍存在于常見變異與稀有變異之間。越來越多的研究證實(shí)“合成關(guān)聯(lián)”模型的真實(shí)性[36～38]。Yang等[39]對(duì)近4000個(gè)無關(guān)個(gè)體的295 K芯片基因型數(shù)據(jù)和身高表型數(shù)據(jù)進(jìn)行關(guān)聯(lián)分析，發(fā)現(xiàn)全部SNP可解釋高達(dá)45%的表型變異；然而，尚有35%的表型變異無法通過芯片上已有的SNP解釋，這主要由常見變異和稀有變異(Rare variation)間的連鎖不平衡所導(dǎo)致。千人基因組計(jì)劃(1000 Genomics Project,http://www.1000genomes.org/)提供了大量可供研究的人群遺傳變異數(shù)據(jù)，統(tǒng)計(jì)研究發(fā)現(xiàn)，人類基因區(qū)域的遺傳變異一般在進(jìn)化上是近期發(fā)生的，且具有稀有性和人群特異性的特點(diǎn)[40～42]。目前認(rèn)為，常見變異和稀有變異都在致病效應(yīng)上有所貢獻(xiàn)[34,39,43](圖1)，效應(yīng)的大小可能與頻率成反比[44,45]，符合進(jìn)化和選擇的觀點(diǎn)。隨著研究的不斷深入，稀有變異所占的份量可能會(huì)越來越重，如Fu等[42]對(duì)6500例非、歐裔美國人外顯子組進(jìn)行測序發(fā)現(xiàn)，在多達(dá)1.1 M的外顯子區(qū)域變異中，73%在進(jìn)化上是近期發(fā)生，且頻率較低；而在可能致病的變異中，這個(gè)比例高達(dá)86%。以上研究表明，NGS技術(shù)可以為“缺失的遺傳力”[44]問題提供新的解決方案，而隨著NGS技術(shù)的不斷成熟與實(shí)驗(yàn)成本的降低，NGS- GWAS的研究和應(yīng)用可能會(huì)逐漸興起。

2.2 基于NGS的GWAS新策略和方法

圍繞CD-CV假設(shè)而設(shè)計(jì)的GWAS芯片主要面向高頻SNP，國際人類基因組單體型圖計(jì)劃(International HapMap Project，http://www.hapmap.org)的數(shù)據(jù)庫主要基于芯片技術(shù)構(gòu)建，因而也以常見變異為主，目前 HapMap III期共收錄約 10 M 個(gè) SNP。傳統(tǒng)GWAS對(duì) MAF<0.05的 SNP很少研究。隨著 NGS技術(shù)的興起，特別是千人基因組計(jì)劃的實(shí)施及其第Ⅰ階段工作的完成，獲得了37 M SNP，此外還檢測出1.4 M插入缺失(InDel)和14 K結(jié)構(gòu)變異(SV)；2014年發(fā)布的第Ⅲ階段結(jié)果，遺傳變異位點(diǎn)總量已高達(dá)79 M。此外，外顯子組測序和轉(zhuǎn)錄組測序也累積了大量的遺傳變異。

基于NGS的GWAS可以驗(yàn)證CD-RV假設(shè)。該假設(shè)認(rèn)為，復(fù)雜疾病是由低頻或稀有變異引起的，且這些攜帶較大遺傳效應(yīng)的變異往往不與常規(guī) SNP緊密連鎖，這可能是造成芯片GWAS的所謂“缺失的遺傳力”問題[44,45]的主要原因。NGS技術(shù)采用高通量的平行測序方式，可以快速地獲取高密度的SNP。隨著該技術(shù)的完善和成熟，以及實(shí)驗(yàn)成本的降低，研究者開始嘗試進(jìn)行基于NGS技術(shù)的GWAS工作，一些新的策略和方法也應(yīng)運(yùn)而生。

圖1 基于等位基因頻率的復(fù)雜疾病遺傳假設(shè)

2.2.1 外顯子組測序

根據(jù)對(duì)孟德爾遺傳病的研究發(fā)現(xiàn)，外顯子突變是其主要病因，而復(fù)雜疾病很可能是由與其功能相關(guān)孟德爾遺傳病的致病變異所影響。因此，外顯子組測序相當(dāng)于對(duì)基因組水平的致病變異進(jìn)行了濃縮，只考慮外顯子組，這樣較易于解釋生物學(xué)功能，且易于取得醫(yī)學(xué)上的應(yīng)用，是一種合理的優(yōu)化策略。一些稀有變異與復(fù)雜性狀的關(guān)聯(lián)關(guān)系，可以通過外顯子組測序的方法進(jìn)行研究[46]。

在一些疾病研究中，如帕金森氏綜合征，基于外顯子測序的關(guān)聯(lián)分析可在一定程度上得到更加豐富的數(shù)據(jù)[47]。Ng等[48]對(duì)4個(gè)Freeman-Sheldon綜合征患者和 8個(gè)正常對(duì)照進(jìn)行了外顯子組測序，在 4個(gè)患者上發(fā)現(xiàn)的致病基因變異位點(diǎn)，不存在于任一對(duì)照個(gè)體中，也未在dbSNP數(shù)據(jù)庫中發(fā)現(xiàn)，表明該策略在發(fā)掘致病變異研究中是可靠的。外顯子組測序同樣可用于檢測病因未知的疾病，Ng等[49]通過對(duì)4個(gè)未知病因的Miller綜合征患者和8個(gè)正常對(duì)照進(jìn)行外顯子組測序，通過對(duì)非同義突變、InDel、可變剪切變異等篩查，檢測到DHODH基因在4個(gè)病例中存在上述變異而在對(duì)照個(gè)體中不存在。外顯子組測序還可用于疾病輔助診斷和治療。在皮膚病的相關(guān)研究中，Tang等[50]對(duì)781名銀屑病患者以及676名健康對(duì)照個(gè)體的樣本進(jìn)行了外顯子組測序，并在第二階段擴(kuò)大樣本總量至 21309例。通過 GWAS分析，科研人員在IL23R和GJB2基因上檢測到了2個(gè)低頻錯(cuò)義突變，在LCE3D、ERAP1、CARD14 和ZNF816A基因上檢測到了5個(gè)常見錯(cuò)義突變，都與銀屑病的發(fā)生顯著關(guān)聯(lián)。Leslie等[51]通過對(duì)2005個(gè)個(gè)體進(jìn)行外顯子組測序及GWAS分析，發(fā)現(xiàn)PCSK9、LDLR和 APOB基因與人體內(nèi)低密度脂蛋白膽固醇的含量相關(guān)。這兩項(xiàng)研究在GWAS分析中均采用了BURDEN test的方法對(duì)稀有變異進(jìn)行檢測，該方法在近些年的相關(guān)研究中應(yīng)用較為普遍。Choi等[52]通過檢測致病突變位點(diǎn)，間接確認(rèn)某患者的疾病癥狀。他們識(shí)別出一些純合同義突變，這些突變從無脊椎動(dòng)物到人類都高度保守。檢測這些突變可能引發(fā)的疾病類型和癥狀后，發(fā)現(xiàn)其中一個(gè)突變位于導(dǎo)致先天性失氯性腹瀉(Congenital chloridediarrhea,CCD)的基因上。外顯子組測序也在復(fù)雜疾病研究中得以應(yīng)用，如 Bowden等[53]對(duì)漿乙二腈水平無顯著性差別的2個(gè)家系中的3個(gè)患者進(jìn)行外顯子組測序，發(fā)現(xiàn)ADIPOQ基因上的1個(gè)頻率為1.1%的低頻變異，能解釋 17%的西班牙裔美國人的血漿乙二腈水平，63%的家族存在該突變。Bilguvar等[54]對(duì)1列腦皮質(zhì)發(fā)育異?；颊哌M(jìn)行測序，發(fā)現(xiàn)WDR62基因與該疾病相關(guān)，結(jié)合功能分析可以解釋一系列的嚴(yán)重皮質(zhì)畸形，如小頭畸形、胼胝體發(fā)育不全等；WDR62基因突變的某些患者還會(huì)發(fā)生腦裂畸形、小腦發(fā)育不全等。

基于外顯子測序進(jìn)行GWAS的計(jì)算和統(tǒng)計(jì)方法[55]包括：(1)沿用傳統(tǒng)的單SNP位點(diǎn)模型，由于低頻突變樣本數(shù)少，可選用Fisher精確檢驗(yàn)；(2)使用多因素模型，將單SNP位點(diǎn)的效應(yīng)加和及校正，計(jì)算過程也需要借助一些降維的方法如Lasso[56]等，以減少運(yùn)算量；(3)折疊法(Collapsing methods)，其原理是將同一個(gè)功能元件上的變異合并，根據(jù)功能元件的不同，分為 CAST[57]和 CMC[58]兩種主要檢測方法，前者考慮全部稀有變異，后者則關(guān)注非同義稀有突變，前文提到的BURDEN test即屬于此方法，此外還有“變量閾值”法[59]和 RareCover[60]等方法；(4)聚合法(Aggregation methods)，相當(dāng)于對(duì)折疊法中稀有變異以及常規(guī)GWAS中的常規(guī)變異進(jìn)行加權(quán)，可分為“加權(quán)和”法[61]和 KBAC[62]兩種。

由于外顯子組測序只關(guān)注外顯子及其剪切位點(diǎn)，因而對(duì)某些類型的致病變異無能為力，如線粒體基因中的突變、結(jié)構(gòu)變異、內(nèi)含子中的基因、調(diào)控序列、CNV、表觀遺傳學(xué)改變、“單親二倍體”、基因之間的相互作用等；另外，有些外顯子藏在染色體末端的重復(fù)區(qū)域內(nèi)，因而無法被外顯子測序所檢測。

2.2.2 低覆蓋度測序結(jié)合基因型填充

低覆蓋度測序結(jié)合基因型填充的策略，是利用已有的公共基因組數(shù)據(jù)，如千人基因組數(shù)據(jù)，來填充覆蓋度較低的測序數(shù)據(jù)，使之達(dá)到有效進(jìn)行GWAS研究的數(shù)據(jù)量。該策略的有效性已有許多研究報(bào)道。該策略方案中應(yīng)當(dāng)主要注重兩點(diǎn)：一是檢測效力；二是計(jì)算速度。

檢測效力的高低主要取決于數(shù)據(jù)量，因此，關(guān)鍵要找到SNP數(shù)量與樣本量的均衡點(diǎn)。SNP數(shù)據(jù)量要足以涵蓋致病突變；樣本數(shù)量也應(yīng)充足，以期檢測到致病效應(yīng)較小的變異，獲得更多的缺失遺傳力。Zheng等[63]對(duì)153例樣本分別用3種芯片(317 K、610K和1 M)進(jìn)行基因分型，分別用HapMap2和千人基因組預(yù)實(shí)驗(yàn)(1000G pilot)數(shù)據(jù)作為參考，進(jìn)行缺失基因型填充。HapMap2填充的準(zhǔn)確性大約 94%，1000G pilot約84%。對(duì)于MAF介于0.3%～5%的稀有SNP，三款芯片數(shù)據(jù)的填充準(zhǔn)確性分別為49%，60%和69%。值得一提的是，盡管1000G pilot的準(zhǔn)確性比HapMap2低，但其填充SNP的數(shù)據(jù)量(約8.5 M)要遠(yuǎn)高于后者(約2.5 M)。

對(duì)原始數(shù)據(jù)的產(chǎn)出量的選擇問題，即如何控制測序覆蓋度以達(dá)到SNP分型目的，千人基因組研究[64]給出了參考，即2～4×測序深度即可獲得個(gè)人基因組約 85%的區(qū)域，數(shù)據(jù)產(chǎn)出和測序成本的比例最優(yōu)。而這個(gè)最優(yōu)解是針對(duì)個(gè)人基因組而非群體基因組測序而估計(jì)的，隨著參考數(shù)據(jù)的不斷累積，個(gè)體測序的覆蓋度可以降得更低，如Pasaniuc等[65]采用極低覆蓋度的策略(平均～0.24×)依然可以獲得較好的填充效果。

由于NGS的數(shù)據(jù)量大，基因型填充的過程運(yùn)算量大、耗時(shí)長，因而一些研究者開發(fā)出了加快運(yùn)算的優(yōu)化算法。Howie等[66]開發(fā)的Pre-phasing填充方法，通過對(duì)GWAS樣本進(jìn)行連鎖相構(gòu)建，進(jìn)而利用參考庫的單倍型進(jìn)行缺失基因型填充。該方法可以在很大程度上縮短運(yùn)算時(shí)間，在大樣本中效果更加明顯。Howie等使用MaCH、IMPUTE2軟件，利用WTCCC2、GAIN、WHI以及 1000G數(shù)據(jù)，對(duì)該方法進(jìn)行了測試，結(jié)果顯示Pre-phasing方法的效率明顯高于常規(guī)方法。

表1 高密度芯片與低覆蓋度測序技術(shù)對(duì)比

因此，低覆蓋度全基因組重測序結(jié)合缺失基因型填充的方法應(yīng)當(dāng)是一種可行的策略。高密度 SNP芯片與低覆蓋度測序的技術(shù)參數(shù)比較見表1。

Rohland等[67]發(fā)明了一種廉價(jià)高效的建庫方法，一個(gè)技術(shù)人員可以在一天內(nèi)構(gòu)建 192個(gè)測序庫，使建庫成本降至每樣本15美元。這些庫不僅可以用于低覆蓋度測序，還可以在多達(dá) 100例加標(biāo)簽樣本(Barcoded samples)混池的條件下進(jìn)行有效測序。他們用極低覆蓋度的外顯子組測序數(shù)據(jù)(0.1～0.5×)結(jié)合千人基因組基因型數(shù)據(jù)做填充，證明了該方法的有效性。這使得在成本降低的情況下，捕獲或填充的SNP的數(shù)目、分型的準(zhǔn)確性都有所增加，檢測效力也得到提高[65]。目前該策略的缺點(diǎn)是對(duì)稀有變異的基因型推斷與填充效果不夠理想。填充策略的準(zhǔn)確性和有效性取決于實(shí)驗(yàn)樣本和參考數(shù)據(jù)庫的數(shù)據(jù)樣本量的多少，隨著測序技術(shù)不斷提高、成本不斷降低，以及公共數(shù)據(jù)庫數(shù)據(jù)量的快速增加，低覆蓋度測序的策略可能將會(huì)更多地被采用。此外，測序的準(zhǔn)確性對(duì)于科學(xué)研究十分重要，測序錯(cuò)誤來源有許多因素[68]，應(yīng)當(dāng)在研究中注意。

2.2.3 家系病例或極端性狀個(gè)體重測序

由于全基因組測序的費(fèi)用比較高，要求對(duì)樣本進(jìn)行選擇性測序。在這一策略中，可挑選有多個(gè)發(fā)病個(gè)體的家系進(jìn)行測序，也可以挑選表型比較極端的個(gè)體進(jìn)行測序。Yang等[69]使用發(fā)病個(gè)體家系的設(shè)計(jì)，對(duì)發(fā)病家系的19個(gè)發(fā)病個(gè)體和27個(gè)正常對(duì)照進(jìn)行選擇性測序，最終識(shí)別出11個(gè)風(fēng)險(xiǎn)CNV位點(diǎn)。之后對(duì)其中4個(gè)CNV在大群體進(jìn)行驗(yàn)證，發(fā)現(xiàn)其確實(shí)在發(fā)病人群中高度富集。Sobreira等[70]使用類似的設(shè)計(jì)發(fā)現(xiàn)了混合性軟骨瘤的致病變異。Lander等[71]在 1989年就提出選擇極端表型個(gè)體進(jìn)行分析可以降低實(shí)驗(yàn)成本，同時(shí)保證檢測效力不會(huì)過多喪失。在高密度芯片興起之后，Manolio等[72]根據(jù)HapMap高密度SNP數(shù)據(jù)討論了這種策略的可行性；Verlaan等[73]進(jìn)一步對(duì)其進(jìn)行了檢測效力的研究。Cirulli等[74]首先篩選出一般人群中的極端表型個(gè)體，然后對(duì)這些個(gè)體進(jìn)行高覆蓋度測序，找出明顯高于一般人群等位基因頻率的SNP位點(diǎn)，再對(duì)這些位點(diǎn)區(qū)域進(jìn)行目標(biāo)區(qū)域測序或分型。

基于醫(yī)院病例(Hospital-based)數(shù)據(jù)的實(shí)驗(yàn)設(shè)計(jì)屬于該策略[50,51]，例如高血壓患者就是由極端表型個(gè)體(160/100 mmHg)轉(zhuǎn)化為病例的，其他種類疾病的診斷過程也可看作是極端表型的篩選，即以某指標(biāo)為閾值進(jìn)行病例篩選。該策略目前主要的問題是有可能會(huì)因?yàn)槌闃悠罨蚝雎阅承┲匾膮f(xié)變量產(chǎn)生假陽性結(jié)果。如基于醫(yī)院病例的數(shù)據(jù)，一般只重視對(duì)癥狀的診斷，而忽略患者的發(fā)病影響因素。

2.2.4 其他策略

除了以上3種主要的策略，還有其他研究策略或方法，如目標(biāo)區(qū)域捕獲測序[75]、混池測序[76]等。目標(biāo)區(qū)域捕獲測序的原理和外顯子組測序一樣，而目標(biāo)更加明確，通常將幾個(gè)至幾十個(gè)疾病風(fēng)險(xiǎn)基因的外顯子、內(nèi)含子、上下游序列進(jìn)行測序，從而降低實(shí)驗(yàn)成本?；斐販y序則是將多個(gè)樣本混合成一個(gè)樣本進(jìn)行測序，該策略適用于病例對(duì)照設(shè)計(jì)，而不太適合于連續(xù)型表型；若結(jié)合加標(biāo)簽(Barcoding)技術(shù)則可區(qū)分個(gè)體，可用于連續(xù)型表型。另外，隨著Illumina Hiseq X Ten和Nextseq 500等新的測序平臺(tái)的應(yīng)用，實(shí)驗(yàn)成本將進(jìn)一步降低。

3 結(jié)語與展望

NGS技術(shù)在實(shí)驗(yàn)成本和速度上優(yōu)于傳統(tǒng)的Sanger測序，在數(shù)據(jù)類型和通量等方面優(yōu)于芯片技術(shù)。目前，在植物育種方面已經(jīng)有多篇NGS-GWAS的文章發(fā)表，如采用低覆蓋度測序結(jié)合基因型填充策略對(duì)水稻14個(gè)農(nóng)藝性狀的研究[77]，利用RNA-seq數(shù)據(jù)對(duì)玉米產(chǎn)油量性狀的 eQTL研究[78]等。然而，對(duì)關(guān)聯(lián)分析顯著性結(jié)果的解釋及功能發(fā)掘，需要進(jìn)一步研究。例如，利用生物信息學(xué)的工具可以發(fā)掘出許多新的功能作用元件。此外，用變異位點(diǎn)解釋復(fù)雜疾病的機(jī)理有一定難度。目前，傳統(tǒng)GWAS主要使用單SNP位點(diǎn)模型，顯得過于單薄，因此需要開發(fā)更加復(fù)雜和精密的模型，例如針對(duì)外顯子組測序數(shù)據(jù)的 Lasso回歸、折疊法、聚合法，及針對(duì)生物調(diào)控網(wǎng)絡(luò)的互作模型等。隨著大量新的遺傳變異類型及其變異位點(diǎn)被發(fā)現(xiàn)，對(duì)變異的注釋和使用方式將面臨新的挑戰(zhàn)。NGS將產(chǎn)生海量的新變異，不僅包括SNP、InDel、SV，還包括cSNP、表達(dá)量變異、可變剪切、甲基化變異等數(shù)據(jù)，使分析變得更加復(fù)雜。

關(guān)于人類基因組研究，目前基于NGS的GWAS策略多是圍繞降低成本而設(shè)計(jì)，但不同策略中需要考慮的問題是相同的，即如何更全面系統(tǒng)地檢測出致病變異并有效應(yīng)用于醫(yī)藥。因此，不斷積累并共享的數(shù)據(jù)必不可少，系統(tǒng)性的生物信息學(xué)挖掘也至關(guān)重要。由于NGS和GWAS兩種技術(shù)成本高，因此人們需要將其策略和實(shí)驗(yàn)設(shè)計(jì)進(jìn)行優(yōu)化，在保證不過多喪失檢測效力和準(zhǔn)確性的情況下，極大提高研究實(shí)施的可行性。目前，隨著公共數(shù)據(jù)庫的不斷累積和共享，外顯子組測序、極低覆蓋度重測序結(jié)合基因型填充策略，可能會(huì)在醫(yī)學(xué)健康研究領(lǐng)域被廣泛采用。隨著基于NGS的RNA-seq、ChIP-seq等功能學(xué)方面數(shù)據(jù)不斷積累，以及非編碼RNA(ncRNA)等新型功能數(shù)據(jù)庫的發(fā)展，有必要對(duì)前人的一些結(jié)果進(jìn)行重新注釋，并嘗試在醫(yī)學(xué)上加以實(shí)際應(yīng)用。

運(yùn)用GWAS方法對(duì)復(fù)雜疾病的研究、早期預(yù)警及個(gè)性化醫(yī)療方面已開始起步。以高血壓為例，結(jié)合遺傳變異信息的降壓治療方法已有多篇文獻(xiàn)報(bào)道[79]。高血壓的傳統(tǒng)療法是服用抗高血壓類藥物，如噻嗪類利尿劑、β-受體阻滯劑、ACE抑制劑、血管緊張素受體阻滯劑和鈣通道阻滯劑等。全世界范圍內(nèi)約有30%的患者只服用一種藥物，40%服用兩種，30%服用三種或以上。但是這類藥物對(duì)收縮壓或舒張壓的控制率不到35%[80]。其根本原因之一在于個(gè)體遺傳變異對(duì)藥物反應(yīng)的特異性，因此開展藥物基因組學(xué)研究具有重要意義。藥物基因組學(xué)研究藥物反應(yīng)的遺傳機(jī)制及藥物反應(yīng)的個(gè)體差異性，是功能基因組學(xué)和分子藥物學(xué)的結(jié)合。早期的研究主要圍繞單個(gè)候選基因與降壓藥物的作用關(guān)系，如ACE、ADD1、NEDD4L、ADRB1和KCNMB1基因。2008年，第1篇基于GWAS的藥物基因組學(xué)研究被報(bào)道，發(fā)現(xiàn)人類12號(hào)染色體YEATS4基因附近區(qū)段影響噻嗪類利尿劑的治療效果[81]。之后，越來越多的采用GWAS方法的藥物基因組學(xué)工作被相繼報(bào)道，如抗高血壓藥物氫氯噻嗪的治療效果[82]等。未來的疾病防治工作，應(yīng)該是治療向預(yù)防前移，防大于治，并且應(yīng)該結(jié)合遺傳因素、環(huán)境因素、生活方式、藥物反應(yīng)等，對(duì)患者或潛在患者進(jìn)行全方位、個(gè)體化的評(píng)估、預(yù)警、診斷和治療?；诓粩喟l(fā)展的 NGS新技術(shù)的GWAS策略將在人類醫(yī)學(xué)研究領(lǐng)域發(fā)揮重要作用。

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