Environmental and heritable factors in the causation of cancer The genetic epidemiology of cancer Interpreting family and twin studies Week 4 Stat 246 2002 Background to and discussion of Lichtenstein et al NEJM 343 2000 78 84 and Risch Cancer Epi Biom Prev 10 2001 733 741 1 Science July 27 2001 Genes Come to the Fore in New Cancer Analysis Last summer scientists in Sweden and Finland got a lot of publicity when they published a paper based on data from mammoth Scandinavian twin studies concluding that inherited factors make a minor contribution to most cancers But they were using the wrong methodology says genetic epidemiologist Neil Risch of Stanford University Risch has done an analysis that comes to the opposite conclusion Genes play a strong role in who gets cancer Risch looked at the same data as in the earlier study headed by Paul Lichtenstein of Sweden s Karolinska Institute In the model Lichtenstein used to extract estimates of the relative contributions of genes and environment to cancer liability environment nearly always won out But Risch says that was the wrong model one problem with it is that there aren t enough people with rare cancers to produce meaningful calculations Risch instead looked at people in twin and family studies who had developed cancer and then estimated the likelihood that a first degree family member would develop the same cancer He found that in the great majority of cancers a family member was about twice as likely as the average person to develop the cancer If anything contrary to Lichtenstein s conclusions the genetic risk was higher for rarer cancers Risch reports in the July issue of Cancer Epidemiology Biomarkers Prevention Prostate colorectal and breast cancers are usually seen as having the strongest genetic components But the top three on Risch s list are thyroid and testicular cancers and multiple myeloma The exercise means that we should be looking for susceptibility genes for all cancers says Risch Lichtenstein was on vacation and unavailable for comment But cancer epidemiologist Sholom Wacholder of the National Cancer Institute in Bethesda Maryland says Risch s work is a reminder of the need to be cautious about interpreting studies that attempt to distinguish genetic and environmental factors 2 The papers in brief Lichtenstein et al 2000 Combined data on 44 788 pairs of twins listed in the Swedish Danish and Finnish twin registries in order to assess the risks of cancer at 28 anatomical sites for the twins of persons with cancer Statistical modeling was used to estimate the relative importance of heritable and environmental factors in causing cancer at 11 of those sites Risch 2001 Offers a reassessment of the role of genetic factors in cancer susceptibility generally and for site specific cancers in particular Presents an detailed critique of Lichtenstein et al 2000 3 Summary of conclusions Lichtenstein et al Inherited genetic factors make a minor contribution to susceptibility to most types of cancers This finding indicates that the environment has the principal role in causing sporadic cancers Risch a All cancers are familial to approximately the same degree with only a few exceptions b early age of diagnosis is generally associated with increased familiality c familiality does not decrease with decreasing prevalence of the tumor in fact the trend is toward increasing familiality with decreasing prevalence d a multifactorial polygenic threshold model fits the twin data for most cancers less well than single gene or genetic heterogeneity type models e recessive inheritance is less likely generally than dominant or additive models f heritability decreases for rarer tumors only in the context of the polygenic model but not in the context of single locus or heterogeneity models g although the family and twin data do not account for gene environment interaction or confounding they are still consistent with genes contributing high 4 attributable risks for most cancer sites Setting the scene I Lichtstenstein et al use the multifactorial polygenic threshold MFT model and infer the relative contributions of heredity and environment within that model Their analysis rests on the usual assumptions of a classic twin study that there was random mating no interactions between genes and environment and equivalent environments for monozygotic and dizygotic twins Phenotypic variance was divided into a component due to inherited genetic factors heritability a component due to environmental factors common to both members of the pair of twins the shared environmental component and a component due to environmental factors unique to each twin the nonshared environmental component 5 Setting the scene II By contrast Risch makes extensive use of familial risk ratios FRRs These are quantities denoted by R where R denotes a relationship S sib O offspring DZ dizygotic twin etc and whose values are the risks of relatives of type R of affected individuals being themselves affected here by cancer divided by the population prevalence A way to think of R is as pr affected R affected pr affected the ratio of the probability risk of someone being affected given that their relative of type R is affected divided by the unconditional probability of that person being affected In this view it is entirely analogous to the coincidence coefficient we met in the study of interference If we denote the population prevalence of our trait by K and the frequency of affected pairs with relationship R by K2 then R K2 K2 6 More on familial risk ratios They can be estimated directly from family data and can also be studied theoretically by calculating them under different assumptions concerning penetrances and susceptibility allele frequencies In particular we can estimate R where R MZ and R DZ from twin data and also study the behaviour of these quantities under different genetic models e g a single rare dominant gene causing susceptibility or a recessive gene whose susceptibility allele frequency ranges from very common to very rare 7 Points to consider when comparing two types of models for the involvement of genes in disease susceptibility How do the models relate to our current understanding of genetics in general and that of disease susceptibility in particular How interpretable are the models parameters How do the models relate to available data Do they fit Does their qualitative behaviour reflect broadly observed trends 8 Single factor models Suppose that the levels of a
View Full Document
Unlocking...