COMMENTARY
Common or Rare Variants for Complex Traits? Marcus R. Munafò and Jonathan Flint
V
rieze et al. (1) report a failure to identify any rare genetic variants associated with addiction-related phenotypes, using a rare variant genotyping chip in a sample of over 7000 individuals nested within over 2000 pedigrees. While sequencing costs remain high, the use of rare variant chips may be a cost-effective approach, given the large samples that will be required to identify these variants. However, it is worth considering what we can expect to learn from these efforts and what we have already learned. Genome-wide association studies (GWAS) have been extremely successful in identifying genetic variants associated with a range of complex phenotypes. For example, several loci associated with various tobacco use phenotypes have been identified through large consortium-based efforts (2). This is in stark contrast to the candidate gene literature, where few (if any) reliable signals emerged after almost 2 decades of effort. Nevertheless, despite this relative success, variants identified to date via GWAS typically explain considerably less than half the heritability of complex phenotypes estimated by twin and family studies. This is the so-called missing heritability problem (3). As Vrieze et al. (1) note: “One explanation for missing heritability is that there is substantial genetic variation that is not captured by common genetic variants.” It is now clear that some missing heritability will be accounted for by variants that have not yet been identified via GWAS (4). This is, in part, because most common variant chips have relatively poor coverage in the minor allele frequency (MAF) spectrum below 5%. Evolutionary theory predicts that mutations strongly affecting complex phenotypes will tend to occur at low allele frequencies (5), hence the interest in rare variants. Under the evolutionary neutral model (Figure 1), most large effect variants are rare (i.e., MAF ⬍ 5%), but the majority of phenotypic variation can be attributed to common variants of small effect. Under this model, missing heritability is likely to be due to two factors (5): 1) unidentified common variants with very small effects that cannot be detected by current GWAS using existing sample sizes, and 2) rare variants not in sufficient linkage disequilibrium with variants on currently available genotyping chips and therefore undetectable via GWAS. In some ways, the focus on common versus rare variants reflects different methodological perspectives of individual researchers. This is not an uncommon situation—in psychology, for example, variation due to situational variables is treated as a source of error variance by experimental psychologists, but is the subject of study for correlational psychologists (6). The separation of experimental from other forms of psychology owes its origins to disputes over the value of studying individual differences, and From the Medical Research Council Integrative Epidemiology Unit (MRM) and United Kingdom Centre for Tobacco and Alcohol Studies and School of Experimental Psychology (MRM), University of Bristol, Bristol; and Wellcome Trust Centre for Human Genetics (JF), University of Oxford, Oxford, United Kingdom. Address correspondence to Marcus R. Munafò, Ph.D., University of Bristol, School of Experimental Psychology, 12a Priory Road, Bristol BS8 1TU, United Kingdom; E-mail: [email protected]. Received and accepted Mar 6, 2014.
0006-3223/$36.00 http://dx.doi.org/10.1016/j.biopsych.2014.03.010
Figure 1. The evolutionary neutral model. The variance explained by an individual genetic variant is a function of its frequency in the population and its effect size. Therefore, rare variants (i.e., minor allele frequency [MAF] ⬍ 5%) of large effect constitute the majority of causal variants but contribute a small proportion of variance because of their rarity, whereas common variants of very small effect together contribute a substantial proportion of variance. [Reproduced with permission from Visscher et al. (5)].
there are parallels in the debate around the relative importance of common and rare variants. There is an assumption, among some, that real biological insight will only arise out of the discovery of large effect, penetrant mutations. This is because modeling highly penetrant mutations is expected to be easier than modeling low penetrant variants. We know how to engineer a mutation that disrupts gene function in model organisms, but it is not clear how to engineer the small effect variant that underlies most complex disease. However, the distinction between common and rare variants suggests a dichotomy that does not reflect the biological reality of the genetic architecture of complex traits (5). It seems likely that whole genome methods, using chips that capture different parts of the MAF spectrum, will still require large samples to achieve sufficient statistical power (7). It will be straightforward to take advantage of existing collections and further refine our understanding of the role that genetic variants across the minor allele frequency spectrum play in influencing complex behavioral traits. However, the use of genotyping chips that focus on variants with a MAF ⬍ 5% represents an intermediate step— whole exome sequencing studies are already emerging (8), and whole genome studies will follow. In other fields, this is already happening and generating further important insights into the etiology of disease while at the same time giving a sense of the scale necessary for this undertaking to be successful (9). Marcus R. Munafò is a member of the United Kingdom (UK) Centre for Tobacco and Alcohol Studies, a UK Clinical Research Council Public Health Research: Centre of Excellence. Funding from British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. BIOL PSYCHIATRY 2014;75:752–753 & 2014 Society of Biological Psychiatry
Commentary The authors report no biomedical financial interests or potential conflicts of interest. 1. Vrieze SI, Feng S, Miller MB, Hicks BM, Pankratz N, Abecasis GR, et al. (2014): Rare nonsynonymous exonic variants in addiction and behavioral disinhibition. Biol Psychiatry 75:783–789. 2. Tobacco and Genetics Consortium (2010): Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 42: 441–447. 3. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. (2009): Finding the missing heritability of complex diseases. Nature 461:747–753. 4. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. (2010): Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569.
BIOL PSYCHIATRY 2014;75:752–753 753 5. Visscher PM, Goddard ME, Derks EM, Wray NR (2012): Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol Psychiatry 17:474–485. 6. Norman G (2002): What does two disciplines of scientific psychology have to say to medical education? Adv Health Sci Educ Theory Pract 7: 57–62. 7. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, Kathiresan S, et al. (2014): Searching for missing heritability: Designing rare variant association studies. Proc Natl Acad Sci U S A 111:E455–E464. 8. Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, Roussos P, et al. (2014): A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506:185–190. 9. Flannick J, Thorleifsson G, Beer NL, Jacobs SB, Grarup N, Burtt NP, et al. (2014): Loss-of-function mutations in SLC30A8 protect against type 2 diabetes [published online ahead of print March 2]. Nat Genet.
www.sobp.org/journal
Common or Rare Variants for Complex Traits? Marcus R. Munafò and Jonathan Flint
V
rieze et al. (1) report a failure to identify any rare genetic variants associated with addiction-related phenotypes, using a rare variant genotyping chip in a sample of over 7000 individuals nested within over 2000 pedigrees. While sequencing costs remain high, the use of rare variant chips may be a cost-effective approach, given the large samples that will be required to identify these variants. However, it is worth considering what we can expect to learn from these efforts and what we have already learned. Genome-wide association studies (GWAS) have been extremely successful in identifying genetic variants associated with a range of complex phenotypes. For example, several loci associated with various tobacco use phenotypes have been identified through large consortium-based efforts (2). This is in stark contrast to the candidate gene literature, where few (if any) reliable signals emerged after almost 2 decades of effort. Nevertheless, despite this relative success, variants identified to date via GWAS typically explain considerably less than half the heritability of complex phenotypes estimated by twin and family studies. This is the so-called missing heritability problem (3). As Vrieze et al. (1) note: “One explanation for missing heritability is that there is substantial genetic variation that is not captured by common genetic variants.” It is now clear that some missing heritability will be accounted for by variants that have not yet been identified via GWAS (4). This is, in part, because most common variant chips have relatively poor coverage in the minor allele frequency (MAF) spectrum below 5%. Evolutionary theory predicts that mutations strongly affecting complex phenotypes will tend to occur at low allele frequencies (5), hence the interest in rare variants. Under the evolutionary neutral model (Figure 1), most large effect variants are rare (i.e., MAF ⬍ 5%), but the majority of phenotypic variation can be attributed to common variants of small effect. Under this model, missing heritability is likely to be due to two factors (5): 1) unidentified common variants with very small effects that cannot be detected by current GWAS using existing sample sizes, and 2) rare variants not in sufficient linkage disequilibrium with variants on currently available genotyping chips and therefore undetectable via GWAS. In some ways, the focus on common versus rare variants reflects different methodological perspectives of individual researchers. This is not an uncommon situation—in psychology, for example, variation due to situational variables is treated as a source of error variance by experimental psychologists, but is the subject of study for correlational psychologists (6). The separation of experimental from other forms of psychology owes its origins to disputes over the value of studying individual differences, and From the Medical Research Council Integrative Epidemiology Unit (MRM) and United Kingdom Centre for Tobacco and Alcohol Studies and School of Experimental Psychology (MRM), University of Bristol, Bristol; and Wellcome Trust Centre for Human Genetics (JF), University of Oxford, Oxford, United Kingdom. Address correspondence to Marcus R. Munafò, Ph.D., University of Bristol, School of Experimental Psychology, 12a Priory Road, Bristol BS8 1TU, United Kingdom; E-mail: [email protected]. Received and accepted Mar 6, 2014.
0006-3223/$36.00 http://dx.doi.org/10.1016/j.biopsych.2014.03.010
Figure 1. The evolutionary neutral model. The variance explained by an individual genetic variant is a function of its frequency in the population and its effect size. Therefore, rare variants (i.e., minor allele frequency [MAF] ⬍ 5%) of large effect constitute the majority of causal variants but contribute a small proportion of variance because of their rarity, whereas common variants of very small effect together contribute a substantial proportion of variance. [Reproduced with permission from Visscher et al. (5)].
there are parallels in the debate around the relative importance of common and rare variants. There is an assumption, among some, that real biological insight will only arise out of the discovery of large effect, penetrant mutations. This is because modeling highly penetrant mutations is expected to be easier than modeling low penetrant variants. We know how to engineer a mutation that disrupts gene function in model organisms, but it is not clear how to engineer the small effect variant that underlies most complex disease. However, the distinction between common and rare variants suggests a dichotomy that does not reflect the biological reality of the genetic architecture of complex traits (5). It seems likely that whole genome methods, using chips that capture different parts of the MAF spectrum, will still require large samples to achieve sufficient statistical power (7). It will be straightforward to take advantage of existing collections and further refine our understanding of the role that genetic variants across the minor allele frequency spectrum play in influencing complex behavioral traits. However, the use of genotyping chips that focus on variants with a MAF ⬍ 5% represents an intermediate step— whole exome sequencing studies are already emerging (8), and whole genome studies will follow. In other fields, this is already happening and generating further important insights into the etiology of disease while at the same time giving a sense of the scale necessary for this undertaking to be successful (9). Marcus R. Munafò is a member of the United Kingdom (UK) Centre for Tobacco and Alcohol Studies, a UK Clinical Research Council Public Health Research: Centre of Excellence. Funding from British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. BIOL PSYCHIATRY 2014;75:752–753 & 2014 Society of Biological Psychiatry
Commentary The authors report no biomedical financial interests or potential conflicts of interest. 1. Vrieze SI, Feng S, Miller MB, Hicks BM, Pankratz N, Abecasis GR, et al. (2014): Rare nonsynonymous exonic variants in addiction and behavioral disinhibition. Biol Psychiatry 75:783–789. 2. Tobacco and Genetics Consortium (2010): Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 42: 441–447. 3. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. (2009): Finding the missing heritability of complex diseases. Nature 461:747–753. 4. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. (2010): Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569.
BIOL PSYCHIATRY 2014;75:752–753 753 5. Visscher PM, Goddard ME, Derks EM, Wray NR (2012): Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol Psychiatry 17:474–485. 6. Norman G (2002): What does two disciplines of scientific psychology have to say to medical education? Adv Health Sci Educ Theory Pract 7: 57–62. 7. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, Kathiresan S, et al. (2014): Searching for missing heritability: Designing rare variant association studies. Proc Natl Acad Sci U S A 111:E455–E464. 8. Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, Roussos P, et al. (2014): A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506:185–190. 9. Flannick J, Thorleifsson G, Beer NL, Jacobs SB, Grarup N, Burtt NP, et al. (2014): Loss-of-function mutations in SLC30A8 protect against type 2 diabetes [published online ahead of print March 2]. Nat Genet.
www.sobp.org/journal
