2DkcTXceO

458 Statistical geneticssufficiently interesting to drive follow-up experimentation.” (Shendure,2008)39.6 The 2010s: From association to relatednessFor several reasons there is a move back from population studies to a considerationof related individuals. As the sizes of case-control GWAS grow, problemsof population structure increase (Price et al., 2006). Further, these samples oftencontain related individuals. Closely related individuals may be discarded,but large numbers of distant relatives also impact results. In addition to otherheterogeneity between cases and controls, the relationship structure of thecase sample may differ from that of the controls. Secondly, GWAS are predicatedon the “common disease, common variant” model, but there is growingrecognition of the role of rare variants in many diseases (Cohen et al., 2004).There are many different mutations that can affect the function of any givengene, and many different genes that function jointly in gene networks. Whileassociation tests for rare variant effects in GWAS designs have been developed(Madsen and Browning, 2009), the use of inferred shared descent can provideamorepowerfulapproach(BrowningandThompson,2012).Not only does using family information in conjunction with associationtesting provide more power (Thornton and McPeek, 2007, 2010), but, usingmodern SNP data, genome shared IBD (Section 39.4) can be detected amongindividuals not known to be related (Brown et al., 2012; Browning and Browning,2012). Once IBD in a given region of the genome is inferred from geneticmarker data, whether using a known pedigree or from population data, itssource is irrelevant. The IBD graph (Figure 39.2(b)) summarizes all the relevantinformation for the analysis of trait data on the observed individuals.The use of inferred IBD, or more generally estimated relatedness (Lee et al.,2011), is becoming the approach of choice in many areas of genetic analysis.39.7 To the futureComputational and molecular technologies change ever faster, and the relevantprobability models and statistical methodologies will likewise change. For theresearchers of the future, more important than any specific knowledge is theapproach to research. As has been said by statistical philosopher Ian Hacking:“Statisticians change the world not by new methods and techniquesbut by ways of thinking and reasoning under uncertainty.”