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Is daar mutasies of genetiese lokasies wat verband hou met buitengewone lewensduur by mense?

Is daar mutasies of genetiese lokasies wat verband hou met buitengewone lewensduur by mense?


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Individue wat ouderdomverwante siektes in die latere lewe vermy, staan ​​bekend as 'uitsonderlike oorlewendes', en het 'n langer lewensduur in vergelyking met hul 'kontroles' (dié wat op 'n soortgelyke tydstip gebore is, maar tog 'verouder' en vroeër gesterf het). Die Leiden-studie het bepaal dat daar in hierdie langlewende individue 'n aansienlike genetiese komponent is wat bydra tot die oorlewing [Schoenmaker et al] (alhoewel dit ewe epigeneties kan wees - of selfs meer waarskynlik 'n kombinasie van albei).

Ek het gelees oor studies wat genetiese mutasies geïdentifiseer het wat voortydige veroudering fenotipes veroorsaak (byvoorbeeld mutasies in die WRN -geen veroorsaak Werner se sindroom [Yu et al]).

Ek het nog geen studies ontdek wat enige gene/streke bepaal wat verband hou met weerstand teen ouderdomsverwante siektes (d.w.s. dié wat 'goed' verouder nie). My vraag is of iemand van sulke studies weet?

Ongetwyfeld studies wat die geneigdheid tot ouderdomsverwante siektes ondersoek het het het 'n paar mutasies gevind - byvoorbeeld mutasies binne die 9p21 genetiese lokus (dws p16INK4a/CDKN2A) is onafhanklik geassosieer met hartsiektes en beroerte (2 ouderdomsverwante siektes) [Wahlstrand et al]. Daarom kan individue sonder enige van hierdie risiko -toenemende allele beskou word as geneig tot buitengewone oorlewing - maar ek beskou dit as apart van my vraag. Is daar beskermende allele/gene/loci wat die 'globale' oorlewing verhoog? Is daar 'n paar beskermende allele, of baie met klein effekte? Of is die uitsonderlike oorlewendes net sonder siekteveroorsakende/risiko-toenemende allele?

Ek stel belang in gepubliseerde navorsing (natuurlik), maar ook in mense se algemene persepsies van die onderwerp; daar is baie teorieë oor veroudering en lang lewe, maar min feite - so meld asseblief wat u 'glo' en verduidelik waarom dit die geval is.

Dankie vir jou tyd.

p.s. Ek is bewus daarvan dat jy laboratoriummodelle geneties kan verander (bv. C. elegans eet-2 mutante [Lakowski et al]) om langer te lewe. Ek is geïnteresseerd in variante wat natuurlik voorkom, spesifiek by mense (of wat ten minste verband hou met die veroudering van mense).


Opdateer (11 Mei 2012)

Ek het 'n studie gevind wat vroeër vanjaar gepubliseer is, wat bevind dat 'n enkele genetiese polimorfisme (na regstelling vir veelvuldige toetsing) met honderdjariges [Sebastiani] geassosieer word. et al]. Die SNP is in TOMM40/APOE (in LD), wat beslis interessant is gegewe die vorige verband tussen APOE en Alzheimer ('n ouderdomsverwante siekte).

Ek is egter nie oortuig deur die eksperimentele ontwerp nie; hulle gebruik ouderdom by dood by honderdjariges as hul gevalle (nadat hulle dit genotipeer het), en hulle gebruik lewendig bevolkingsbeheer as die kontroles. Alhoewel dit onwaarskynlik is dat al hierdie individue langlewend sal word, sou 'n beter ontwerp (om meer krag te verkry) gewees het om individue te gebruik wat aan 'voortydige' (bv. tussen 60 en 70) ouderdomverwante siekte gesterf het. Ek wag nog steeds op 'n oortuigende studie, maar dit lyk belowend!


Dit lyk asof u die meerderheid van die korpus molekulêre genetika wat verband hou met veroudering ontbloot het. Daar is 'n opvolgontleding wat pas vanjaar gepubliseer is deur Sebastiani et al. Pubmed het 'n kenmerk wat jy kan kyk na die publikasies wat verwys na die koerant waarna jy kyk, en daar is net resensies. Dit lyk asof niemand anders nog een gedoen het nie.

U het waarskynlik die reg om te voel dat die krag van die studie nie groot is nie. Die navorsers het wel probeer. Die Eeufees het lank familielede en die kontroles het albei ouers gesterf voordat hulle 73 was. Alhoewel die kontroles 68 jaar oud was. en ouer. Ja nie wonderlik nie. Die kontroles is baie moeilik, want jy wil nie mense hê wat sal sterf aan hartaanvalle, hipertensie en allerhande dinge wat die agtergrond sal kleur nie. Dit is 'n moeilike studie om te doen.

Geen enkele geen sal 'n fenotipe soos langlewendheid oordra nie. Dit is basies onmoontlik, aangesien die voordele vinnig deur die bevolking sou propageer. Ons kan praat oor hoe hierdie eienskap post-reproduktief is en oor die ouma-effek, maar ek sit dit vir eers opsy.

Die gebrek aan statistiese krag van die studie word ook belemmer omdat slegs 800 honderdjariges waarskynlik tientalle verskillende kombinasies van langlewendheidsgroepe weerspieël. (dit is nog 'n rede waarom al hierdie mense van 'n spesifieke Europese afkoms is - om die aantal variasies in kombinasies te beperk). Daar is 281 beduidende mutasies wat hulle gevind het. APOE is verreweg die belangrikste (fig1), maar gegewe die situasie is dit nie genoeg om net op daardie een te fokus nie.

Byvoorbeeld, selfs in vlieë lyk dit of die sir2-1-geen nie 'n enkele mutante anti-verouderingsfenotipe is nie. As hulle die mutantstam met wildtipe oorsteek, leef die sir2.1 -mutante 15% langer. Dit is ten minste een ander mutasie wat hulle nie opgespoor het nie, wat 'n 50%-lewende bult in die mutante veroorsaak het. Dit is waarskynlik dat as daar nog 5 ander belangrike mutasies was, dat die gemiddelde crossover ook een of meer van hierdie mutasies het.

Om te probeer verstaan ​​hoe hierdie lokusse kan saamwerk om die lewensduur te verleng, het hulle groepe van hierdie SNP's geskep (interessant dat die helfte in gene is wat relatief skaars is). hierdie groepe gene lyk asof dit die lewensduur saam beïnvloed. die koerant is oop en al die 281 gene is aflaaibaar, elkeen van hulle het 'n bekende biologiese eienskap waarna jy kan kyk. Daar moet nog vasgestel word hoe dit bymekaar pas, maar hul groepe kan vir eers die weg wys.


Vandag sal ek 'n paar onlangse publikasies wys oor die onderwerp van langdurige verwante genetiese variante by mense. Die navorsingsgemeenskap bestee baie moeite aan die identifisering en bevestiging van menslike genetiese variante wat met groter langlewendheid geassosieer word. Die koste van die verkryging van genetiese data gaan voort om vinnig te daal, en 'n paar jaar van nou af sal klein word in vergelyking met die ander koste van die bestuur van 'n studie. Al hoe meer navorsers sluit hierby aan omdat genetiese studies binne hul begrotings val. In die wêreld van suiwer wetenskaplike strewe, die soeke na kennis, is dit alles goed. Daar is min gebiede so groot soos die van genetika en sellulêre biochemie, en die sluise van data gaan oop soos nog nooit tevore nie. Dekades se werk lê voor om selfs 'n aansienlike fraksie van die kruising van veroudering en sellulêre metabolisme op die detailvlak van molekulêre biologie te karteer. Op die lang termyn is dit alles nuttig: geen data gaan tot niet nie, en watter soort omvattende molekulêre nanotegnologie ook al kom na medisyne soos ons dit vandag verstaan, sal die volledige kaart van menslike biochemie as 'n beginpunt vereis. Dit is egter ver.

Uit 'n praktiese oogpunt, in die konteks van die vervaardiging van maniere om veroudering gou genoeg te behandel om saak te maak, is die vasstelling van die redes waarom sommige mense geneig is om ietwat langer as ander te lewe, egter 'n byvertoning. Dit het min of geen relevansie om die gesonde lewensduur van almal betekenisvol te verleng nie. Vir een is dit duidelik uit werk tot op datum dat (a) daar baie, baie bydraende faktore is tot die verband tussen genetika en veroudering, (b) enige enkele faktor 'n klein, soms byna ononderskeibare statistiese effek op mortaliteit het, en (c ) die oorgrote meerderheid van die faktore verskil vir elke studiepopulasie. U kan 'n boek vul met die assosiasies wat tot dusver gevind is en nooit herhaal is nie, terwyl daar slegs 'n paar genetiese variante is wat in verskeie studies geld, soos APOE. Tweedens, gegewe medisyne of ander terapieë wat die gene en proteïenvlakke van 'n mens akkuraat verander om die van 'n eeufeesvrou na te boots, wat help dit u? 'n Baie klein hupstoot vir jou kanse om meer jare in 'n toestand van gevorderde veroudering en toenemende broosheid te leef. Die oorgrote meerderheid van diegene met dieselfde genetiese variante as langlewende studiepopulasies sterf op baie dieselfde skedule as die res van ons. As die navorsingsgemeenskap tyd en moeite gaan belê in behandelings vir veroudering, dan behoort dit ten minste behandelings te wees met 'n groot verwagtingswaarde in terme van mortaliteitvermindering en gesonde lewensverlenging.

Hierdie studies is verteenwoordigend van die reeks werk wat tans plaasvind: aanvanklike identifikasie van moontlike assosiasies met langlewendheid bevestiging studies wat die meerderheid van assosiasies wat elders gevind word weggooi en studies wat maniere uiteensit om die proses van die identifisering van genetiese assosiasies met langlewendheid te verbeter.

Lang lewe is 'n komplekse fenotipe, en min genetiese variante wat die lewensduur beïnvloed, is geïdentifiseer. Veroudering en siektes is egter nou verwant, en baie is bekend oor die genetiese basis van siekterisiko. Hier wys ons met behulp van genoomwye assosiasiestudies (GWAS) van langlewendheid en siekte dat daar 'n oorvleueling is tussen lokusse wat by langlewendheid betrokke is en lokusse wat betrokke is by verskeie siektes, soos Alzheimer se siekte en koronêre arteriesiekte. Ons ontwikkel dan 'n nuwe statistiese raamwerk om genetiese variante te vind wat verband hou met uiterste langlewendheid. Die metode, ingeligte GWAS (iGWAS), maak gebruik van kennis van 14 groot studies van siekte en siekteverwante eienskappe om die soektog na SNP's wat met langlewendheid geassosieer word, te beperk. Deur iGWAS te gebruik, het ons agt SNP's gevind wat betekenisvol is in ons ontdekkingskohorte, en ons was in staat om vier hiervan te bekragtig in replikasiestudies van langlewende vakke. Ons resultate impliseer nuwe plekke in lang lewe en toon 'n genetiese oorvleueling tussen langlewendheid en ouderdomsverwante siektes en eienskappe. Behalwe die studie van menslike lewensduur, kan iGWAS toegepas word om die statistiese krag in enige GWAS van 'n teikenfenotipe te verhoog deur groter GWAS van geneties verwante toestande te gebruik.

In 'n standaard GWAS -analise is slegs een lokus in hierdie studies beduidend (APOE/TOMM40). Met iGWAS identifiseer ons agt genetiese lokusse om aansienlik te assosieer met uitsonderlike menslike langlewendheid. Ons het die agt hoof SNP's in onafhanklike kohorte opgevolg en bewys gevind van replikasie van vier loci en suggestiewe bewyse vir nog een met buitengewone lang lewe. Die lokusse wat herhaal is, sluit in APOE/TOMM40 (geassosieer met Alzheimer se siekte), CDKN2B/ANRIL (geïmpliseer by die regulering van sellulêre veroudering), ABO (merk die O -bloedgroep) en SH2B3/ATXN2 ('n seingene wat lewensduur verleng by Drosophila en 'n geen betrokke by neurologiese siektes).


Leef tot 100: Nuwe gene vir lang lewe gevind

Verskeie nuwe gene wat aan 'n buitengewone lang lewe gekoppel is, is ontdek, volgens 'n nuwe studie wat die genome ondersoek het van mense wat tot in hul 100's leef, bekend as honderdjariges.

Deur 'n nuwe metode te gebruik, het die navorsers vier gene gevind wat met 'n baie lang lewe gekoppel is: 'n Geen genaamd ABO, wat betrokke is by die bepaling van bloedtipe 'n geen genaamd CDKN2B, wat seldeling reguleer 'n geen genaamd APOE, wat gekoppel is aan Alzheimer se siekte en 'n geen genaamd SH2B3, wat voorheen gevind is dat die lewe in vrugtevlieë verleng kan word.

Die navorsers hoop dat toekomstige studies nog meer gene ontdek wat met lang lewe verband hou, en sal uitvind hoe hierdie gene die verouderingsproses kan beïnvloed.

"Daar is 'n redelike sterk genetiese komponent om 'n honderdjarige te word, en ons wil uitvind wat dit is," sê studienavorser Stuart Kim, 'n professor in die Departement Ontwikkelingsbiologie en Genetika aan die Sanford Universiteit. "Ons begin die raaisel ontrafel" waarom sommige mense so suksesvol verouder in vergelyking met die normale bevolking, het Kim gesê. [Verleng die lewe: 7 maniere om oor 100 te leef]

Vorige studies het gepoog om variasies in gene te vind wat meer gereeld by die ou mense voorkom, in vergelyking met jonger mense, maar het nie veel geluk gehad nie. Hierdie studies het miljoene variasies in die menslike genoom ondersoek, maar dit het moontlik 'n paar belangrike assosiasies gemis.

Die nuwe studie was daarop gemik om die soektog na gene wat met 'n lang lewe verband hou, te beperk deur te konsentreer op diegene wat bekend is dat dit die risiko van 'n persoon op ouderdomsverwante siektes, soos hartsiektes en Alzheimer, sterk beïnvloed. Die gedagte is dat hierdie siektes 'n persoon se risiko verhoog om vroeg te sterf, en dus sal genetiese variante wat die risiko van hierdie siektes verhoog ook die kanse op 'n lang lewe verminder, het die navorsers gesê.

Die navorsers het eers gesoek na langlewendheid-gekoppelde gene in 'n bevolking van ongeveer 800 mense ouer as 100 en 5 400 mense ouer as 90.

Hulle het agt gene gevind wat verband hou met 'n lang lewensduur, en hulle kon vier van hierdie gene bevestig in 'n opvolganalise van ongeveer 1000 mense van 100 jaar of ouer.

Die studie het bevind dat sekere variante in die ABO-, CDKN2B-, APOE- en SH2B3 -gene meer by honderdjariges voorkom as by mense met 'n tipiese lewensduur. (Volwassenes in die Verenigde State het 'n gemiddelde lewensverwagting van ongeveer 79 jaar, volgens die Centers for Disease Control and Prevention.)

Die studie het byvoorbeeld bevind dat die 'n genetiese variasie wat met tipe O-bloed geassosieer word, meer algemeen was by honderdjariges as in die studie se kontrolegroep, wat beteken dat daar effens meer honderdjariges met tipe O-bloed was, in vergelyking met mense met 'n tipiese lewensduur. Vorige studies het bevind dat mense met tipe O-bloed 'n laer risiko vir koronêre hartsiekte en kanker het, en laer cholesterolvlakke het as mense met ander bloedgroepe.

Dit lyk asof 'n ander genetiese variant in die CDKN2B -geen 'n rol speel in die vraag of selle voortgaan om te verdeel of op te hou deel. Aangesien die stop van seldeling, genoem veroudering, vermoedelik bydra tot veroudering, kan 'n geenvariasie wat selveroudering verminder, 'n faktor wees wat bydra tot suksesvolle veroudering, het Kim gesê.

Kim vermoed dat daar nog meer gene is wat verband hou met 'n langer lewensduur.

"Ek hoop ons koerant inspireer ander mense om voort te gaan soek na" gene wat verband hou met lang lewe, het Kim gesê.

Die studie is gister (17 Desember) in die joernaal PLOS Genetics gepubliseer.


Abstrak

Tweelingstudies toon dat genetiese verskille ongeveer 'n kwart van die variasie in volwasse menslike lewensduur uitmaak. Algemene polimorfismes wat 'n beskeie effek op lewensduur het, is in een geen geïdentifiseer, APOE, wat hoop verskaf dat ander genetiese determinante ontbloot kan word. Alhoewel daar voorgestel word dat variante met aansienlike voordelige gevolge bestaan ​​en verskeie kandidate voorgestel is, moet die uitwerking daarvan nog nie bevestig word nie. Menslike studies oor langlewendheid staar talle teoretiese en logistieke uitdagings in die gesig, aangesien die bepalende faktore van hul lewensduur buitengewoon kompleks is. Grootskaalse skakelingstudies van langlewende gesinne, longitudinale kandidaat-geen-assosiasiestudies en die ontwikkeling van analitiese metodes bied egter die potensiaal vir toekomstige vordering.


Resultate

Primêre en sekondêre stelle

Ons primêre stel (ontdekkingset) het bestaan ​​uit 801 onverwante vakke wat ingeskryf was vir die New England Centenarian Study (NECS) en 914 geneties ooreenstemmende kontroles. NECS -vakke was Kaukasiërs wat tussen 1890 en 1910 gebore is met 'n ouderdomsgroep van 95 tot 119 jaar (gemiddelde ouderdom van 104 jaar). Ongeveer een derde van die NECS-steekproef het honderdjariges ingesluit met 'n eerstegraadse familielid wat ook uitsonderlike langlewendheid behaal het, en sodoende die steekproef se krag verbeter [19]. Kontroles het 241 geneties ooreenstemmende NECS-verwysende proefpersone ingesluit wat eggenote was van honderdjarige nageslag of kinders van ouers wat op 'n ouderdom ≤73 jaar gesterf het, en 673 geneties ooreenstemmende proefpersone wat uit die Illumina-kontroledatabasis gekies is. Vir genetiese ooreenstemming gebruik ons ​​'n voorheen beskryfde algoritme [20] wat onderwerpe volgens etnisiteite groepeer op grond van groepsanalise van die mees insiggewende hoofkomponente van genoomwye genotipe data (Figuur S1). Let daarop dat die gemiddelde lewensverwagting in die kohort gebaseer op die lewenstabel van die geboortekohort van die Amerikaanse Social Security Administration in 1920, 82 jaar is, met 'n standaardafwyking van 7,9 jaar, sodat die gemiddelde ouderdom van die gevalle in ons studie en die gemiddelde lewensverwagting in die kohort verskil met 2,69 keer die standaardafwyking. Verder was die gemiddelde ouderdom van NECS-kontroles 75 jaar, met standaardafwyking 7 jaar. Daarom was die verskil tussen die gemiddelde ouderdom van honderdjariges in die ontdekkingset en NECS -kontroles meer as 4 keer die standaardafwyking, wat die krag van die studie verhoog het. Vir replikasie het ons twee ekstra stelle gebruik. Die replikasiestel 1 (“ELIX”) bestaan ​​uit 253 Noord -Amerikaanse blanke vakke wat deur Elixir Pharmaceuticals tussen 2001 en 2003 ingeskryf is. Hierdie individue is gebore tussen 1890 en 1910 (ouderdomsgroep 89–114 jaar, gemiddelde ouderdom 100) en is gewerf en fenotipeer met behulp van 'n protokol soortgelyk aan die NECS. Referentpersone (n = 341) is uit die oorblywende Illumina -kontroles geïdentifiseer en geneties aangepas by die 253 gevalle met dieselfde bypassende algoritme wat in die ontdekkingsstel gebruik is. Die replikasiestel 2 was saamgestel uit 60 honderdjariges wat 39 vakke van Europese afkoms ingesluit het wat tussen Junie 2009 en September 2010 by die NECS ingeskryf is (ouderdomsreeks 100–114, gemiddelde ouderdom 108) plus 21 honderdjariges (ouderdomsreeks 101–115, gemiddelde ouderdom 100) ) nie ingesluit in die ontdekkingsreeks tydens die genetiese bypassing nie, en alle beskikbare blanke monsters van die Illumina -kontroledatabasis word nie in bogenoemde vergelykings gebruik nie. Eeufeesgangers en kontroles in replikasiestel 2 is nie geneties aangepas om die veralgemeenbaarheid van die resultate te toets nie. Figuur 2 toon die ouderdomsverspreidings van eeufeesmense in die ontdekkings- en replikasiestelle 1 en 2. Ons gebruik ook 'n bykomende stel 867 neurologies normale proefpersone wat gebruik word as kontroles vir 'n Parkinson -siekte GWAS [21], om die robuustheid van enkele SNP -assosiasies te toets. Ons het 243 980 SNP's ontleed wat 'n streng gehaltebeheerprotokol geslaag het wat in die metodes beskryf word.

NECS: honderdjariges van die ontdekkingset, ELIX: nie -agters en honderdjariges uit die ELIX -replikasiestel, NECS 2: addisionele NECS -replikasiestel van 60 eeufees. Die y-as gee die digtheid aan, en die x-as dui die ouderdom aan, in groepe van 2 jaar. Die frekwensie van proefpersone met ouderdomme tussen x en x+2 is 2*digtheid* (steekproefgrootte).

Enkel SNP Analise

Eerstens het ons 'n tradisionele enkele SNP -analise uitgevoer waarin ons SNP's ingedeel het in die ontdekking wat deur die sterkte van assosiasie gestel is. Ons het beide Bayesiaanse en tradisionele frekwensieanalises van 4 verskillende genetiese modelle (algemene/genotipiese, alleliese/additiewe, resessiewe en dominante assosiasies) gebruik om krag [22], [23] te maksimeer. Met die Bayesiese analise het ons elke SNP-assosiasie volgens die Bayes-faktor (BF) behaal, wat die posterior kans vir die assosiasie is wanneer die nulhipotese van geen assosiasie en die alternatiewe hipotese van 'n assosiasie dieselfde voorafwaarskynlikheid het [24], en dan gebruik ons ​​die maksimum BF (MBF) as 'n maatstaf vir statistiese betekenis. Figuur S2 toon die foutkoers van besluitreëls gebaseer op verskeie drempels vir MBF. Die passingstrategie het blykbaar verwarring deur stratifikasie verwyder omdat ons geen inflasie van assosiasies waargeneem het nie en die genomiese beheerfaktor in alleliese assosiasie was 0.99 (Figuur S3). Ons het ook addisionele ontledings wat hieronder beskryf word, uitgevoer om te ondersoek of die oorblywende verwarring deur bevolkingsstratifikasie die resultate kan bevoordeel en geen bewyse van vooroordeel gevind het nie.

Die Manhattan-komplot (Figuur 3) vertoon die log10(MBF) vir elke getoetsde SNP. Hierdie analise identifiseer 'n enkele SNP in APOE/TOMM40 as onweerlegbaar genoomwyd betekenisvol (P & lt10e-8, Tabel 1). Die assosiasie is in die ELIX-stel gerepliseer en is gehandhaaf toe ons 867 verwysende proefpersone gebruik het wat in 'n GWAS van Parkinson se siekte ingesluit is as alternatiewe kontroles (Tabel 1).

Die SNP's word georden volgens chromosoom (alternatiewe kleurbande) en, binne chromosoom, volgens fisiese posisie (x-as). Ons het die assosiasie van elke SNP met uitsonderlike langlewendheid getoets deur algemene, alleliese, dominante en resessiewe modelle te gebruik en die y-as rapporteer die maksimum log10 (Bayes-faktor) wat vir elke SNP waargeneem is. Die SNP rs2075650 in APOE/TOMM40 onweerlegbare genoomwye betekenis bereik (log10(MBF) = 7.9 en p-waarde<e-10). Figuur S3 toon die Manhattan-plot en QQ-plot vir die additiewe model met behulp van logistiese regressie.

Die apolipoproteïen E (APOE) word geassosieer met menslike lewensduur [25], [26], [27]. SNP rs2075650 kom voor in 'n intron van TOMM40 maar dit is 'n sterk volmag van die SNP's wat die APOE allele [28]. Hierdie SNP word geassosieer met Alzheimer se siekte (AD) [29], [30] en lipiedvlakke [31], [32].

Genetiese risikomodellering

In die enkele SNP -analise het ons 'n aansienlike verryking waargeneem vir beduidende assosiasies wat nie aan die streng drempel vir genoomwye betekenis voldoen nie. Byvoorbeeld, 112 SNP's word geassosieer met 'n buitengewone lang lewe met log10 (MBF) & gt2 teen 'n geskatte foutsyfer van 4 in 100,000 onafhanklike toetse en dus 8-10 vals positiewe assosiasies wat toevallig verwag word in ∼250,000 getoetsde SNP's as daar geen beduidende assosiasies was nie en alle SNP's was onafhanklik (Figuur S2). Die groepe assosiasies in chromosome 8, 9 en 21 in Figuur 3 dui op interessante streke, hoewel hulle nie 'n genoomwye betekenis bereik nie. Verskeie skrywers het aangevoer dat SNP's wat nie 'n genoomwye betekenis bereik nie, biologies belangrik kan wees vanweë hul gesamentlike effek [33], [34], [35], [36] en dat hulle suksesvolle risikomodelle gebou het wat genetiese vatbaarheid kan voorspel aan verskeie komplekse eienskappe wat hoogs oorerflik is [37], [38], [39], [40], [41]. Ons het ook die hipotese ondersoek dat verskillende stelle SNP's wat verband hou met buitengewone lang lewe, alhoewel met matige effekte, die genetiese aanleg tot buitengewone lang lewe gesamentlik kan kenmerk [42], [43] en dus 'n model kan bied vir siliko -analise wat kan aandui doelwitte en genetiese paaie na buitengewone lang lewe.

Seleksie van voorspellende SNP's.

Om met hierdie analise voort te gaan, moes ons verskeie besluite neem oor die klas modelle om mee te werk, hoe om die aantal SNP's wat in die model ingesluit moet word, te bepaal en die algehele soekstrategie. Ons het gekies om die genetiese risiko wat verband hou met 'n stel SNP's te bereken deur 'n eenvoudige maar effektiewe Bayesiaanse klassifikasiemodel te gebruik, ook bekend as die naïewe Bayes-klassifiseerder (Figuur 4A) [44]. Hierdie benadering - ook gebruik in [39] om die vatbaarheid vir carotis -aterosklerose akkuraat te voorspel - klassifiseer 'n onderwerp as geneig tot buitengewone lang lewe as die posterior waarskynlikheid van buitengewone lang lewe, gegewe genotipes van 'n stel SNP's, die posterior waarskynlikheid van gemiddelde lang lewe oorskry (Figuur 4A). Die voordeel van hierdie metode is dat daar feitlik geen boonste limiet is vir die aantal SNP's wat vir klassifikasie gebruik kan word nie, en dit kan gebruik word vir risiko -voorspelling, selfs al is die data wat vir die analise gebruik is, uit 'n gevallestudie -studie. Ons het 'n vorentoe -soekprosedure ontwerp om 'n voldoende aantal voorspellende SNP's te ontdek (Figuur 4A). Die prosedure bou 'n reeks geneste genetiese risikomodelle wat begin met die belangrikste SNP in die ontdekkingsreeks en voeg een SNP geleidelik op 'n slag by 'n gesnoeide stel SNP's wat in volgorde van log10 (MBF) gesorteer is. Elke model word gebruik vir voorspelling, en die akkuraatheid van elke model om buitengewone lang lewe en gemiddelde lewensduur te voorspel, word geëvalueer deur sensitiwiteit en spesifisiteit (Figuur 4B). Die neiging van sensitiwiteit en spesifisiteit in Figuur 4B toon dat die insluiting van meer SNP's beide sensitiwiteit en spesifisiteit verhoog, maar die wins van akkuraatheid word minder en minder namate SNP's met dalende statistiese betekenisvolheid (laer MBF) bygevoeg word. Veral die sensitiwiteitsplato's tussen 275-285 SNP's, sodat die insluiting van meer SNP's blykbaar nie die sensitiwiteit verder verbeter nie (Figuur 4B). Omdat die model met 281 die naaste sensitiwiteit en spesifisiteit gee, het ons die soektog na voorspellende SNP's by 281 gestaak. Ons het ook 'n hersteekproefbenadering gebruik (Figuur S4A) om hierdie keuse te bekragtig, en ondersoek die effek van die verandering van die SNP -volgorde in ons heuristiese soektog (Figuur S4C en D) en moontlike vooroordeel in die laboratoriumgenotipering (Figuur S4B).

Ons bestel SNP's volgens die maksimum Bayes Factor in die ontdekkingset en bou geneste SNP -stelle, begin met die belangrikste SNP en voeg dan een SNP op 'n slag by die geordende lys. Die voorwaardelike waarskynlikhede van SNP -genotipes by eeufeesgangers (p (SNPi| EL)) en kontroles (p (SNPi| AL)) word gebruik om die agterkans van buitengewone lang lewe te bereken (p (EL | Σk)) met behulp van Bayes se stelling en vorige waarskynlikheid p (EL) = 0,5. Die klassifikasiereël is die standaard Bayesiaanse klassifikasiereël wat optimaal is onder 'n 0–1 verliesfunksie. B) Gevoeligheid en spesifisiteit van 400 geneste modelle. Die x-as meld die aantal SNP's in elk van die geneste modelle aan, en die y-as rapporteer sensitiwiteit (% van die honderdjariges met 'n posterior waarskynlikheid van 'n buitengewone lang lewe en 'n groter kans op gemiddelde lewensduur) en spesifisiteit (% van die kontroles met 'n posterior waarskynlikheid van 'n buitengewone lang lewe) waarskynlikheid van gemiddelde lang lewe).

Tabel S1 bied volledige besonderhede van al die 281 SNP's en die waarskynlikhede wat gebruik word om die voorspelling te bereken met behulp van die formule in Figuur 4A. Betroubaarheid van die Illumina genotipering is dubbel gekontroleer deur die top 28 SNP's van die model te her-genotipeer met behulp van TaqMan genotipering in 'n onafhanklike laboratorium, en die 99.7% konkordansie dui daarop dat die data betroubaar is (Figuur S5). Intensiteitsplotte van die 281 SNP's is beskikbaar by www.bumc.bu.edu/centenarian. 137 SNP's van die 281 SNP's kom in 130 gene voor, waarvan sommige voorheen met veroudering geassosieer is, soos LMNA (rs915179), WRN (rs1800392), en SOD2 (rs2758331) en verskeie van hulle is in die nabyheid van koderende SNP's [45]. Die LMNA gene, wat vir die kernomhulselproteïene lamin A en lamin C kodeer, word geassosieer met die progeroïde (voortydige verouderingsagtige) sindroom, Hutchinson-Gilford-sindroom [46]. Die WRN geen is 'n DNA-helikase en eksonuklease wat 'n deterministiese rol speel in DNA-herstel en 'n ander progeroïedsindroom, Werner se sindroom [47]. Die WRN gene is geassosieer met lang lewe in die Framingham Heart Study (FHS) monster [48]. Dit is opmerklik dat die twee gene wat verantwoordelik is vir die bekendste progeroïdesindrome in die genetiese risikomodel verskyn, en dit kan die krag weerspieël van die steekproef wat sulke uiterste ouderdomme insluit. Nog 'n geen, wat ook geassosieer word met langlewendheid in die FHS-monster sowel as die Jerusalem-studie, is SOD2, of superoksied dismutase 2 [49]. SOD2 is 'n sleutel vrye radikale aasvreter en vrye radikale skade speel waarskynlik 'n belangrike patogeniese rol in veroudering en talle ouderdomsverwante siektes [50]. CDKN2A (rs1063192) voer 'n sleutelstap in die p53-pad uit wat veronderstel is om 'n sleutelrol te speel in die indusering van sellulêre veroudering [51] en dit is geassosieer met volwasse diabetes [52]. SORCS1 (rs7907713) en SORCS2 (rs6812745) is gekoppel AD [53]. Maag inhiberende polipeptied (GIP), wat algemeen na verwys word as glukose-afhanklike insulienotropiese peptied, kodeer 'n proteïen wat insulienafskeiding reguleer en aktiveer AKT [54]. Die assosiasie van hierdie geen (rs9899404) ondersteun die potensiële rol van insulienregulering in uitsonderlike langlewendheid [55], en stel nuwe teikengene voor vir menslike veroudering buite FOXO1, FOXO3A en IGF-IR [56], [57], [58]. Daar is ook groeiende bewyse van GIP speel 'n beskermende rol in beide diabetes en AD en GIP word ondersoek as 'n terapeutiese teiken [59].

Ons het Genomatix (http://www.genomatix.de) gebruik om die lys van 130 gene wat in die genetiese risikomodel ingesluit is te annoteer en die ontleding het getoon dat die lys verryk is vir verskeie groepe gene wat aan beide algemene en seldsame siektes gekoppel is (MeSH) ). Gene wat verband hou met Alzheimer se siekte, demensie en tauopatieë was die belangrikste: 38 van die 130 gene was in die literatuur gekoppel aan AD (p-waarde om die nulhipotese te toets dat dit toevallig gebeur, was 6.17 e-7) en hulle word vertoon in Figuur 5 42 gene is aan demensie gekoppel (Figuur S6, p-waarde om die nulhipotese te toets dat dit toevallig gebeur was 1.07 e-6) en 38 na tauopatieë (p-waarde 8.47e-7). Die feit dat so baie gene 'n rol speel by demensie, stem ooreen met die epidemiologiese bevinding dat demensie afwesig is of aansienlik vertraag word by eeufeesmense (gemiddelde aanvangsouderdom, 93 jaar) [60]. Gene wat verband hou met ander ouderdomverwante siektes is ook beduidend verteenwoordig: 24 gene is gekoppel aan koronêre arteriesiekte (Figuur 5), en verskeie gene is aan neoplasmas gekoppel.

Die twee netwerke vertoon 38 van die 130 gene in die genetiese risikomodel wat gekoppel is aan Alzheimer se siekte (bo) en 24 van die 130 gene wat gekoppel is aan kransslagader (onder) in die literatuur, hetsy deur funksionele of genetiese assosiasiestudies . Die nodusse wat deur 'n rand gekoppel is, verteenwoordig óf gene wat "saamaangehaal" is (gestreepte lyne) of "geassosieer deur deskundige kurasie" (deurlopende lyne). Die pylkop beteken dat die assosiasies aktivering (driehoek), inhibisie (sirkel), modulasie (diamant), omskakeling (pylkop) is. Die knoopvorm gee inligting oor bekende rolle van die gene (sien insetsel). Die nodusse wat enkeltonig is, is in die literatuur aan AD/CAD gekoppel, maar nie saam met ander gene nie. Die aantal gene wat aan elke siekte gekoppel is, is vergelyk met wat per toeval verwag word met behulp van Fisher se presiese toets, en die p-waardes toon aan dat die gene seta ongelukkig die gevolg is van toeval. (Netwerke gegenereer met Genomatix).

Genetiese risikoprofiele en ensemble van risikomodelle.

Om die rol van hierdie 281 SNP's in die vorming van die genetiese vatbaarheid vir buitengewone langlewendheid beter te verstaan, het ons 'n genetiese risikoprofiel vir elke proefpersoon gegenereer deur die posterior waarskynlikheid van uitsonderlike langlewendheid (p(EL|Σ) te teken.k), y-as) teenoor die aantal SNP's in elk van die 281 SNP-stelle Σk (x-as) en hul patrone ondersoek. Figuur 6 toon byvoorbeeld die profiele van 3 eeufeesmanne en 'n kontrole. In elke profiel toon 'n toenemende posterior waarskynlikheid van buitengewone lang lewe sterk verryking van variëteite wat verband hou met lang lewe, omdat die posterior waarskynlikheid van buitengewone lang lewe toeneem wanneer die profiel 'n nuwe SNP -genotipe insluit wat meer gereeld by eeufeesgangers voorkom as in kontroles (sien metodes).

281 geneste SNP-stelle is gebruik om die posterior waarskynlikheid van buitengewone lang lewe in die 4 vakke (y-as) te bereken en is geteken teen die aantal SNP's in elke stel (x-as). In die 107-jarige stel die eerste 5 SNP Σ1 = [rs2075650], Σ2 = [Σ1, rs1322048], …, Σ5 = [Σ4, rs6801173] bepaal 'n posterior waarskynlikheid van uitsonderlike langlewendheid wat wissel tussen 0.54 en 0.28. Hierdie proefpersoon dra genotipes AA, AG, AG, CC, AA vir die 5 SNP's onderskeidelik en, met die uitsluiting van genotipe AA van rs2075650 wat meer algemeen by honderdjariges voorkom, is die ander genotipes meer algemeen in kontroles as honderdjariges en bepaal dit 'n posterior waarskynlikheid van buitengewone lang lewe wat laer is as die posterior waarskynlikheid van gemiddelde lewensduur. Die sesde SNP -stel, Σ6 = [Σ5, rs337656], voorspel 'n byna 30%-kans op uitsonderlike langlewendheid. Die onderwerp dra die AA -genotipe vir die SNP rs337656 wat meer gereeld by eeufeesgangers voorkom (tabel S1), en die dra van hierdie genotipe verhoog die agterkans van buitengewone lewensduur. The probability predicted by the next SNP sets increases steadily and all models with more than 20 SNPs predict more than a 50% chance of exceptional longevity. This genetic profile shows that the subject carries some combinations of SNP alleles that are associated with exceptional longevity, while other alleles are associated with “average longevity”. However, the overall genetic risk profile determined by all 281 SNP sets makes a strong case for exceptional longevity because the majority of models predict more than an 80% chance of exceptional longevity. The genetic risk profile of the centenarian who died at age 119 years is even more convincing: with the exception of the first SNP, all subsequent SNP sets determine more than a 70% chance of exceptional longevity, and 272 of the 281 models predict more than an 80% chance for exceptional longevity. This profile shows that this subject is highly enriched for SNPs alleles that are more common in centenarians (longevity associated variants) and that probably played a determinant role in the extreme survival. The profile of the third subject, age 108 years, shows that different SNP sets determine different chances for exceptional longevity, and only the overall trend of genetic risk provides evidence for exceptional longevity. The fourth plot displays the profile of a control, and shows that this subject carries some longevity associated variants however, the overall trend of genetic risk points to average longevity rather than exceptional longevity.

These examples support the hypothesis that exceptional longevity is determined by varying combinations of longevity associated variants and some number of SNPs may be optimal for classifying some subjects but not others. Consistent with this observation, we choose an ensemble of all 281 genetic risk models to compute the posterior probability of exceptional longevity. This ensemble of 281 genetic risk models provides 89% specificity and sensitivity in the discovery set (Figuur 7A). We next evaluated the predictive accuracy of this ensemble of models in the two replication sets, the ELIX set and a recently enrolled sample of NECS centenarians.

Panel A: Posterior probability of exceptional longevity (EL) and average longevity (AL) (x axis) in the centenarians (red boxplots) and controls (AL1: Illumina controls, blue boxplots, AL2: NECS controls, green boxplots) of the discovery set (NECS, top left). Both sensitivity and specificity were 89%. The boxplots in blue and green show that the distributions of the posterior probability of EL in the two control groups are not statistically different (p-value from t-test comparing the posterior probability of EL = 0.21). Panel B: Posterior probability of EL and AL (x axis) in the centenarians (red boxplots) and controls of the replication set 1. Sensitivity and specificity were 60% and 58% and the distributions of the predictive score are significantly different (t-test p-value = 0.001). Panel C: Median values of the posterior probability of EL (predictive score) in subsets of centenarians of the replication set 1 with increasing ages. The barplot shows that the median score increases with older ages. Panel D: Sensitivity of the classification rule in subsets of centenarians of the replication set 1 with increasing ages. The barplot shows the increasing sensitivity in older groups that reaches 85% in 20 subjects aged 106 and older. Panel E: Distribution of the posterior probability of exceptional longevity in the 253 cases of the replication set divided into two age groups (<103 years, pale blue, mean age 99 years, and ≥103 years, red, mean age 106). The sensitivities in the two groups are 57% and 71.4%. The three distributions are significantly different (p-value = 0.04 from t-test comparing Illumina controls and centenarians aged <103 p-value = 0.004 from t-test comparing the centenarians stratified by age). Panel F: Sensitivity and specificity in an additional set of 2863 controls from the Illumina database (blue), and an additional set of 60 centenarians that include 39 centenarians enrolled since June 2009 (mean age 108) and 21 centenarians that were excluded from older analysis because of genetic matching (mean age 106). The specificity in the additional Illumina controls is 61.2%. The sensitivity in the additional centenarians was 71.5% in the set of 21, and 82% in the additional 39 for a total of 78% (p-value from t-test comparing the posterior probabilities of EL in controls and centenarians <1e-10).

Sensitivity and specificity in the replication set 1 (the ELIX sample) comprised of 253 nonagenarians and centenarians and 341 genetically matched controls were 60% and 58% (Figuur 7B) and AUC = 0.58 (Figure S7). Although the distributions of the predictive scores are significantly different (p-value from t-test comparing the predicted probabilities of exceptional longevity in the two groups was 0.001), the discrimination of the model is less remarkable. Since the ages of subjects in this replication set are younger compared to the centenarians in the discovery set (median age in the ELIX set was 100 years compared to 104 in centenarians of the discovery set) and because we expect that the genetic component of exceptional longevity increases with age, we next examined the distribution of the predictive score and the trend of sensitivity in subsets of subjects with older ages. The median probability of exceptional longevity in subsets of increasing age of survival increases to more than 68% in the 81 subjects with ages >101 (Figure 7C) and, consistently, the sensitivity of the model to correctly classify older subjects increases with older ages and reaches 85% in 20 subjects ages 106 and older (Figure 7D). For example, when the 253 cases of the replication set were divided into two age groups to better match the ages of the substantially older discovery set (204 subjects, age <103, median age 100 years, and 49 subjects, age ≥103, median age 105) the sensitivity of the model was 71% (Figure 7E).

To further investigate our hypothesis that the genetic contribution to exceptional longevity increases with older ages we evaluated the sensitivity of the classification rule in a second replication set of newly enrolled NECS centenarians (n = 39) plus NECS centenarians not included in the discovery set (n = 21), the sum of which had a median age of 107 years (Figure 7F). The sensitivity was 78% (71.5% in the group of 21 with median age 106 and 82% in the recently enrolled and older group of 39) confirming increasing sensitivity with increasing ages. The boxplot in Figure 7F shows that the specificity in an additional set of 2863 controls of replication set 2 was is 61.2%, and the AUC in this second replication set was 0.74 (Figure S7). Figure S8 shows that classification rules based on randomly ordering the top 281 SNPs (mid panels) or selecting 281 SNPs at random have lower sensitivity and specificity.

Our analysis used genetic matching to remove confounding by population structure. However, since we matched subjects within clusters, residual stratification might still confound the association and possibly affect the classification rule. To test the hypothesis that there is no confounding by residual stratification, we conducted two traditional analyses. In one analysis, we adjusted the associations of the 281 SNPs by the top 4 principal components, and in the second analysis we did not. We then checked whether adjusting the analysis by the principal components would change the results of the unadjusted analysis. Figure S9 shows that the distributions of p-values for the two analyses in different genetic models are essentially identical (correlation coefficient 0.98 to 0.99). This analysis would indicate that there is no confounding due to residual stratification. We repeated the analysis adjusting for the top 10 principal components. The effect of this more stringent adjustment made 3 of the 281 SNPs borderline significant. We also checked if there is any residual correlation between the top two PCs and the score predicted by our model, and there appears to be none (Figure S10).

Genetic Signatures

Some genetic risk profiles were recurrent and we speculated that groups of centenarians may have genetic risk profiles that are associated with different sub-types of exceptional longevity such as different prevalences or ages of onset of age-related diseases. To test this hypothesis, we used cluster analysis to group the genetic risk profiles into prototypical signatures. We then investigated whether groups of centenarians with particular genetic risk profiles shared specific age-related sub-phenotypes.

Cluster analysis identified 26 groups of 8 to 94 centenarians (90% of the discovery set) with similar genetic risk profiles, while 10% of the centenarians had rare profiles that occur in groups of 7 centenarians or less. Figuur 8 shows, for example, the 9 largest clusters while all clusters are shown in Figure S11. The prototypical genetic risk profiles associated with each cluster are informative displays of the longevity associated variants, and represent different genetic signatures of exceptional longevity. While the ensemble of genetic risk models provides a global estimate of the probability of exceptional longevity, the pattern itself provides information about the different sets of longevity associated variants that drive a subject toward this probability. The same cluster analysis of predicted profiles in centenarians of the merged replication sets 1 and 2 identified 15 clusters with 8 or more subjects, while approximately 35% profiles clustered in groups of 7 or less. The two most predictive and the one least predictive clusters from the replication set are also shown in Figuur 8. Figure S12 depicts all 15 clusters with 8 or more subjects in the merged replication sets.

In each plot, the x-axis reports the number of SNPs in each genetic risk model (1,…,281), and the y-axis reports the posterior probability of exceptional longevity predicted by each model. The boxplots (one for each SNP set on the x axis) display the genetic risk profiles of the centenarians grouped in the same cluster. Numbers N in parentheses are the cluster sizes, and the average posterior probability of exceptional longevity. Color coding represents the strength of the genetic risk to predict EL (Blue: P(EL|∑281)>0.95 Red: 0.5<P(EL|∑281)<0.95 Orange: 0.20<P(EL|∑281)<0.5 Green: P(EL|∑281)<0.2). The full set of 26 clusters is in Figure S11 and includes more than 90% of centenarians in the discovery set.

To examine the specificity of the profiles in characterizing exceptional longevity, we also generated genetic risk profiles of the control subjects in the discovery set and used cluster analysis to group them. Only 5 subjects had profiles that predicted exceptional longevity with more than 90% posterior probability (Figure S13). Other clusters with more than 8 subjects show that the majority of these profiles match either the lack of a predictive genetic signature as in cluster C26 or the sporadic presence of longevity associated variants of clusters C24–C25 in Figure S11. To further extend this analysis, we clustered the genetic profiles of all 4118 controls that include all controls in the discovery and replication sets 1 and 2. Cluster analysis identified several signatures, of which only 17% predict exceptional longevity with more than 70% posterior probability, and 67% predict average longevity (Figure S14). The most predictive genetic signatures that characterize exceptional longevity are rare amongst control subjects, and only 0.6% of the genetic signatures of control subjects have a posterior probability of exceptional longevity >0.95.

Interestingly, the patterns of genetic risk profiles that cluster into genetic signatures distinctly differ from clusters of genetic risk profiles generated from SNPs selected at random (Figure S15). We also investigated if some clusters were enriched for specific ethnicities, but no clusters showed enrichment for any specific European ethnicity.

We next investigated whether different genetic signatures correlate with different life spans (Figuur 9). Some genetic signatures were indeed associated with significantly different life spans. For example, the most predictive signature (C1) was comprised of centenarians with significantly longer survival compared to centenarians with signatures C2 (the second most predictive) or cluster C26 (the least predictive), and the median survival in centenarians with signature C1 was 105 years compared to 104 years in centenarians with signature C2 or 103 years in centenarians with signature C26. We observed a similar result when we compared the survival of centenarians with the most predictive signatures in the merged replication sets (R1 and R2), and when we compared the survival of centenarians with the most and the least predictive signatures (R1 and R15) (See Figuur 9). However, not all signatures correlated with different survival, for example centenarians with signatures C1 and C3 did not demonstrate different survival (See Figure S16). Preliminary analyses provided in the supplementary material (in need of replication) suggest that the different genetic signatures of exceptional longevity associate with varying prevalences and ages of onset of various age-related diseases (Figure S17, Table S2).

Panel A: Some genetic signatures are associated with significantly different life-span. For example the most predictive signature (C1) comprises centenarians with significant longer survival compared to centenarians with signatures C2 or C26. (p-value 0.01 and 0.02) More examples are in Figure S15. Panel B: The two most predictive genetic signatures and the least predictive signature in the centenarians of the merged replications sets show consistent results. The comparison between survival of centenarians with the most predictive signature R1 and the least predictive signature R15 reaches statistical significance, (p-value = 0.003) while the comparison between survival distributions of centenarians with signatures R1 and R2 does not reach statistical significance (p-value 0.10).

For 17 of the 28 centenarians in cluster C26 who lack almost all the longevity associated variants discovered in this study, we had information about familial longevity. Twenty-five percent (n = 5) had >50% of siblings who survived past the age of 90 and some had evidence for longevity as shown in some pedigrees in Figure S18. This could indicate that such families have more private or rare variants not captured by either the genotyping or the model.


Aanvullende inligting

(GWAS). A study that involves genotyping large numbers of participants to identify statistical associations between genetic variants and traits of interest.

Changes to the genetic code arising from errors during DNA damage repair, DNA replication or mitosis, occurring in somatic (non-germline) tissues.

The proportion of variance in a phenotype that can be attributed to genetic differences among individuals in a given population. Narrow-sense heritability estimates additive genetic effects. Broad-sense heritability includes both additive and dominance effects.

Individual-level scores that summarize genetic risk (or protection) for a given phenotype. For each person, a score is computed by counting the number of effect alleles (genetic variants, weighted by their effect) that the person carries. A polygenic score is computed by summing scores from a large number, potentially all, of the variants in the genome.

(LD). Non-random associations between alleles at different loci.

A method that uses single-nucleotide polymorphisms associated with an exposure as instruments to probe the causal nature of the relationship between this exposure and an outcome of interest.

Theory arguing that some mutations are selected because they are beneficial to early-life fitness but become harmful later in life, thus causing ageing.

The production of daughter cells from a single parent cell, all sharing a particular characteristic or trait.


Slotopmerkings

Despite the enormous progress achieved by DNA- investigating technologies, such as SNP arrays and exome capturing/re-sequencing, the current knowledge on how genetic variants influence exceptional longevity in humans is still based on the old candidate gene approaches. The adoption of innovative study designs combined with novel genetic platforms and innovative statistical methods hopefully will bring to the identification of new intervention points at which to modulate aging and the diseases of aging.


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19. Chen Y, Whetstone HC, Lin AC, Nadesan P, Wei Q, et al. Beta-catenin signaling plays a disparate role in different phases of fracture repair: implications for therapy to improve bone healing. PLoS Med 20074:e249.

20. Ito M, Yang Z, Andl T, Cui C, Kim N, et al. Wnt-dependent de novo hair follicle regeneration in adult mouse skin after wounding. Natuur 2007447:316-20.

21. Reya T, Clevers H. Wnt signalling in stem cells and cancer. Natuur 2005434:843-50.

22. Giannakis M, Hodis E, Jasmine Mu X, Yamauchi M, Rosenbluh J, et al. RNF43 is frequently mutated in colorectal and endometrial cancers. Nat Genet 201446:1264-6.

23. Mandai S, Mori T, Nomura N, Furusho T, Arai Y, et al. WNK1 regulates skeletal muscle cell hypertrophy by modulating the nuclear localization and transcriptional activity of FOXO4. Wetenskaplike Rep 20188:9101.

24. Klotz LO, Sánchez-Ramos C, Prieto-Arroyo I, Urbánek P, Steinbrenner H, et al. Redox regulation of FoxO transcription factors. Redox Biol 20156:51-72.

25. Newhouse S, Farrall M, Wallace C, Hoti M, Burke B, et al. Polymorphisms in the WNK1 gene are associated with blood pressure variation and urinary potassium excretion. PLoS One 20094:e5003.

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Inhoud

Any two human genomes differ in millions of different ways. There are small variations in the individual nucleotides of the genomes (SNPs) as well as many larger variations, such as deletions, insertions and copy number variations. Any of these may cause alterations in an individual's traits, or phenotype, which can be anything from disease risk to physical properties such as height. [9] Around the year 2000, prior to the introduction of GWA studies, the primary method of investigation was through inheritance studies of genetic linkage in families. This approach had proven highly useful towards single gene disorders. [10] [9] [11] However, for common and complex diseases the results of genetic linkage studies proved hard to reproduce. [9] [11] A suggested alternative to linkage studies was the genetic association study. This study type asks if the allele of a genetic variant is found more often than expected in individuals with the phenotype of interest (e.g. with the disease being studied). Early calculations on statistical power indicated that this approach could be better than linkage studies at detecting weak genetic effects. [12]

In addition to the conceptual framework several additional factors enabled the GWA studies. One was the advent of biobanks, which are repositories of human genetic material that greatly reduced the cost and difficulty of collecting sufficient numbers of biological specimens for study. [13] Another was the International HapMap Project, which, from 2003 identified a majority of the common SNPs interrogated in a GWA study. [14] The haploblock structure identified by HapMap project also allowed the focus on the subset of SNPs that would describe most of the variation. Also the development of the methods to genotype all these SNPs using genotyping arrays was an important prerequisite. [15]

The most common approach of GWA studies is the case-control setup, which compares two large groups of individuals, one healthy control group and one case group affected by a disease. All individuals in each group are genotyped for the majority of common known SNPs. The exact number of SNPs depends on the genotyping technology, but are typically one million or more. [8] For each of these SNPs it is then investigated if the allele frequency is significantly altered between the case and the control group. [17] In such setups, the fundamental unit for reporting effect sizes is the odds ratio. The odds ratio is the ratio of two odds, which in the context of GWA studies are the odds of case for individuals having a specific allele and the odds of case for individuals who do not have that same allele.

As an example, suppose that there are two alleles, T and C. The number of individuals in the case group having allele T is represented by 'A' and the number of individuals in the control group having allele T is represented by 'B'. Similarly, the number of individuals in the case group having allele C is represented by 'X' and the number of individuals in the control group having allele C is represented by 'Y'. In this case the odds ratio for allele T is A:B (meaning 'A to B', in standard odds terminology) divided by X:Y, which in mathematical notation is simply (A/B)/(X/Y).

When the allele frequency in the case group is much higher than in the control group, the odds ratio is higher than 1, and vice versa for lower allele frequency. Additionally, a P-value for the significance of the odds ratio is typically calculated using a simple chi-squared test. Finding odds ratios that are significantly different from 1 is the objective of the GWA study because this shows that a SNP is associated with disease. [17] Because so many variants are tested, it is standard practice to require the p-value to be lower than 5 × 10 −8 to consider a variant significant.

There are several variations to this case-control approach. A common alternative to case-control GWA studies is the analysis of quantitative phenotypic data, e.g. height or biomarker concentrations or even gene expression. Likewise, alternative statistics designed for dominance or recessive penetrance patterns can be used. [17] Calculations are typically done using bioinformatics software such as SNPTEST and PLINK, which also include support for many of these alternative statistics. [16] [18] GWAS focuses on the effect of individual SNPs. However, it is also possible that complex interactions among two or more SNPs, epistasis, might contribute to complex diseases. Due to the potentially exponential number of interactions, detecting statistically significant interactions in GWAS data is both computationally and statistically challenging. This task has been tackled in existing publications that use algorithms inspired from data mining. [19] Moreover, the researchers try to integrate GWA data with other biological data such as protein-protein interaction network to extract more informative results. [20] [21]

A key step in the majority of GWA studies is the imputation of genotypes at SNPs not on the genotype chip used in the study. [22] This process greatly increases the number of SNPs that can be tested for association, increases the power of the study, and facilitates meta-analysis of GWAS across distinct cohorts. Genotype imputation is carried out by statistical methods that combine the GWAS data together with a reference panel of haplotypes. These methods take advantage of sharing of haplotypes between individuals over short stretches of sequence to impute alleles. Existing software packages for genotype imputation include IMPUTE2, [23] Minimac, Beagle [24] and MaCH. [25]

In addition to the calculation of association, it is common to take into account any variables that could potentially confound the results. Sex and age are common examples of confounding variables. Moreover, it is also known that many genetic variations are associated with the geographical and historical populations in which the mutations first arose. [26] Because of this association, studies must take account of the geographic and ethnic background of participants by controlling for what is called population stratification. If they fail to do so, these studies can produce false positive results. [27]

After odds ratios and P-values have been calculated for all SNPs, a common approach is to create a Manhattan plot. In the context of GWA studies, this plot shows the negative logarithm of the P-value as a function of genomic location. Thus the SNPs with the most significant association stand out on the plot, usually as stacks of points because of haploblock structure. Importantly, the P-value threshold for significance is corrected for multiple testing issues. The exact threshold varies by study, [28] but the conventional threshold is 5 × 10 −8 to be significant in the face of hundreds of thousands to millions of tested SNPs. [8] [17] [29] GWA studies typically perform the first analysis in a discovery cohort, followed by validation of the most significant SNPs in an independent validation cohort. [30]

Attempts have been made at creating comprehensive catalogues of SNPs that have been identified from GWA studies. [32] As of 2009, SNPs associated with diseases are numbered in the thousands. [33]

The first GWA study, conducted in 2005, compared 96 patients with age-related macular degeneration (ARMD) with 50 healthy controls. [34] It identified two SNPs with significantly altered allele frequency between the two groups. These SNPs were located in the gene encoding complement factor H, which was an unexpected finding in the research of ARMD. The findings from these first GWA studies have subsequently prompted further functional research towards therapeutical manipulation of the complement system in ARMD. [35] Another landmark publication in the history of GWA studies was the Wellcome Trust Case Control Consortium (WTCCC) study, the largest GWA study ever conducted at the time of its publication in 2007. The WTCCC included 14,000 cases of seven common diseases (

2,000 individuals for each of coronary heart disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, Crohn's disease, bipolar disorder, and hypertension) and 3,000 shared controls. [16] This study was successful in uncovering many new disease genes underlying these diseases. [16] [36]

Since these first landmark GWA studies, there have been two general trends. [37] One has been towards larger and larger sample sizes. In 2018, several genome-wide association studies are reaching a total sample size of over 1 million participants, including 1.1 million in a genome-wide study of educational attainment [38] and a study of insomnia containing 1.3 million individuals. [39] The reason is the drive towards reliably detecting risk-SNPs that have smaller odds ratios and lower allele frequency. Another trend has been towards the use of more narrowly defined phenotypes, such as blood lipids, proinsulin or similar biomarkers. [40] [41] These are called intermediate phenotypes, and their analyses may be of value to functional research into biomarkers. [42] A variation of GWAS uses participants that are first-degree familielede of people with a disease. This type of study has been named genome-wide association study by proxy (GWAX). [43]

A central point of debate on GWA studies has been that most of the SNP variations found by GWA studies are associated with only a small increased risk of the disease, and have only a small predictive value. The median odds ratio is 1.33 per risk-SNP, with only a few showing odds ratios above 3.0. [2] [44] These magnitudes are considered small because they do not explain much of the heritable variation. This heritable variation is estimated from heritability studies based on monozygotic twins. [45] For example, it is known that 80-90% of variance in height can be explained by hereditary differences, but GWA studies only account for a minority of this variance. [45]

A challenge for future successful GWA study is to apply the findings in a way that accelerates drug and diagnostics development, including better integration of genetic studies into the drug-development process and a focus on the role of genetic variation in maintaining health as a blueprint for designing new drugs and diagnostics. [46] Several studies have looked into the use of risk-SNP markers as a means of directly improving the accuracy of prognosis. Some have found that the accuracy of prognosis improves, [47] while others report only minor benefits from this use. [48] Generally, a problem with this direct approach is the small magnitudes of the effects observed. A small effect ultimately translates into a poor separation of cases and controls and thus only a small improvement of prognosis accuracy. An alternative application is therefore the potential for GWA studies to elucidate pathophysiology. [49]

One such success is related to identifying the genetic variant associated with response to anti-hepatitis C virus treatment. For genotype 1 hepatitis C treated with Pegylated interferon-alpha-2a or Pegylated interferon-alpha-2b combined with ribavirin, a GWA study [50] has shown that SNPs near the human IL28B gene, encoding interferon lambda 3, are associated with significant differences in response to the treatment. A later report demonstrated that the same genetic variants are also associated with the natural clearance of the genotype 1 hepatitis C virus. [51] These major findings facilitated the development of personalized medicine and allowed physicians to customize medical decisions based on the patient's genotype. [52]

The goal of elucidating pathophysiology has also led to increased interest in the association between risk-SNPs and the gene expression of nearby genes, the so-called expression quantitative trait loci (eQTL) studies. [53] The reason is that GWAS studies identify risk-SNPs, but not risk-genes, and specification of genes is one step closer towards actionable drug targets. As a result, major GWA studies by 2011 typically included extensive eQTL analysis. [54] [55] [56] One of the strongest eQTL effects observed for a GWA-identified risk SNP is the SORT1 locus. [40] Functional follow up studies of this locus using small interfering RNA and gene knock-out mice have shed light on the metabolism of low-density lipoproteins, which have important clinical implications for cardiovascular disease. [40] [57] [58]

Atrial fibrillation Edit

For example, a meta-analysis accomplished in 2018 revealed the discovery of 70 new loci associated with atrial fibrillation. It has been identified different variants associated with transcription factor coding-genes, such as TBX3 and TBX5, NKX2-5 o PITX2, which are involved in cardiac conduction regulation, in ionic channel modulation and cardiac development. It was also identified new genes involved in tachycardia (CASQ2) or associated with alteration of cardiac muscle cell communication (PKP2). [59]

Schizophrenia Edit

While there is some research using a High-Precision Protein Interaction Prediction (HiPPIP) computational model that discovered 504 new protein-protein interactions (PPIs) associated with genes linked to schizophrenia, [60] [61] the evidence supporting the genetic basis of schizophrenia is actually controversial and may suffer from some of the limitation of this method of study. [62]

Plant growth stages and yield components Edit

GWA studies act as an important tool in plant breeding. With large genotyping and phenotyping data, GWAS are powerful in analyzing complex inheritance modes of traits that are important yield components such as number of grains per spike, weight of each grain and plant structure. In a study on GWAS in spring wheat, GWAS have revealed a strong correlation of grain production with booting data, biomass and number of grains per spike. [63]

Plant pathogens Edit

The emergences of plant pathogens have posed serious threats to plant health and biodiversity. Under this consideration, identification of wild types that have the natural resistance to certain pathogens could be of vital importance. Furthermore, we need to predict which alleles are associated with the resistance. GWA studies is a powerful tool to detect the relationships of certain variants and the resistance to the plant pathogen, which is beneficial for developing new pathogen-resisted cultivars. [64]

GWA studies have several issues and limitations that can be taken care of through proper quality control and study setup. Lack of well defined case and control groups, insufficient sample size, control for multiple testing and control for population stratification are common problems. [3] Particularly the statistical issue of multiple testing wherein it has been noted that "the GWA approach can be problematic because the massive number of statistical tests performed presents an unprecedented potential for false-positive results". [3] Ignoring these correctible issues has been cited as contributing to a general sense of problems with the GWA methodology. [65] In addition to easily correctible problems such as these, some more subtle but important issues have surfaced. A high-profile GWA study that investigated individuals with very long life spans to identify SNPs associated with longevity is an example of this. [66] The publication came under scrutiny because of a discrepancy between the type of genotyping array in the case and control group, which caused several SNPs to be falsely highlighted as associated with longevity. [67] The study was subsequently retracted, [68] but a modified manuscript was later published. [69]

In addition to these preventable issues, GWA studies have attracted more fundamental criticism, mainly because of their assumption that common genetic variation plays a large role in explaining the heritable variation of common disease. [70] Indeed, it has been estimated that for most conditions the SNP heritability attributable to common SNPs is <0.05. [71] This aspect of GWA studies has attracted the criticism that, although it could not have been known prospectively, GWA studies were ultimately not worth the expenditure. [49] GWA studies also face criticism that the broad variation of individual responses or compensatory mechanisms to a disease state cancel out and mask potential genes or causal variants associated with the disease. [72] Additionally, GWA studies identify candidate risk variants for the population from which their analysis is performed, and with most GWA studies stemming from European databases, there is a lack of translation of the identified risk variants to other non-European populations. [73] Alternative strategies suggested involve linkage analysis. [74] [75] More recently, the rapidly decreasing price of complete genome sequencing have also provided a realistic alternative to genotyping array-based GWA studies. It can be discussed if the use of this new technique is still referred to as a GWA study, but high-throughput sequencing does have potential to side-step some of the shortcomings of non-sequencing GWA. [76]

Genotyping arrays designed for GWAS rely on linkage disequilibrium to provide coverage of the entire genome by genotyping a subset of variants. Because of this, the reported associated variants are unlikely to be the actual causal variants. Associated regions can contain hundreds of variants spanning large regions and encompassing many different genes, making the biological interpretation of GWAS loci more difficult. Fine-mapping is a process to refine these lists of associated variants to a credible set most likely to include the causal variant.

Fine-mapping requires all variants in the associated region to have been genotyped or imputed (dense coverage), very stringent quality control resulting in high-quality genotypes, and large sample sizes sufficient in separating out highly correlated signals. There are several different methods to perform fine-mapping, and all methods produce a posterior probability that a variant in that locus is causal. Because the requirements are often difficult to satisfy, there are still limited examples of these methods being more generally applied.


Slotopmerkings

Despite the enormous progress achieved by DNA- investigating technologies, such as SNP arrays and exome capturing/re-sequencing, the current knowledge on how genetic variants influence exceptional longevity in humans is still based on the old candidate gene approaches. The adoption of innovative study designs combined with novel genetic platforms and innovative statistical methods hopefully will bring to the identification of new intervention points at which to modulate aging and the diseases of aging.



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