Rare and common variants: twenty arguments

Few GWAS-significant associations have been confirmed as the true risk variant through molecular, biochemical, or biophysical work.

“Very few of the thousands of GWAS significant associations have been shown using molecular genetics, biochemistry or biophysics to be the actual risk variant38, 109.”

• cites [38] Chorley, B. N. et al. Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: current and developing technologies. Mutat. Res. 659, 147–157 (2008). sourceNot checked yet

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• cites [109] Bauer, R. C., Stylianou, I. M., Rader, D. J. Functional validation of new pathways in lipoprotein metabolism identified by human genetics. Curr. Opin. Lipidol. 22, 123–128 (2011). sourceNot checked yet

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GWAS provide very little evidence for gene-environment or gene-gene interactions.

“It must be noted that there is very little evidence from GWAS for either GxE or GxG interactions108, but such effects could be mild at the level of individual associations and be below the power of detection.”

• cites [108] Hamza, T. H. et al. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee. PLoS Genet. 7, e1002237 (2011). sourceNot checked yet

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It is unclear whether the same methods are as efficient for schizophrenia, arthritis, or intelligence.

“and it is not clear that they are as efficient for schizophrenia, arthritis or intelligence75,76.”

• cites [75] Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genet. 43, 519–525 (2011). sourceNot checked yet

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• cites [76] Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011). sourceNot checked yet

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Four hypertriglyceridemia genes showed a significant excess of rare coding variants.

“whereas a significant excess of rare coding variants was found in 4 genes for hypertriglyceridemia116.”

• cites [116] Johansen, C. T. et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nature Genet. 42, 684–687 (2010). sourceNot checked yet

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An initial inflammatory bowel disease screen found rare-variant evidence for just one of 63 GWAS-identified genes.

“only 1 of 63 genes in an initial screen for inflammatory bowel disease found evidence for rare variant effects (and these were protective114, but see also115)”

• cites [113] Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009). sourceNot checked yet

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• cites [114] Momozawa, Y. et al. Resequencing of positional candidates identifies low frequency IL23R coding variants protecting against inflammatory bowel disease. Nature Genet. 43, 43–47 (2011). sourceNot checked yet

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• cites [115] Rivas, M. A. et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nature Genet. 43, 1066–1073 (2011). sourceNot checked yet

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As more people are sequenced, many rare coding variants are found in healthy controls.

“As more individuals are sequenced, many of these appear in healthy controls”

The common disease-common variant hypothesis has been refuted by the missing heritability problem.

“When GWAS began the field was dominated by the simple common disease-common variant (CD-CV) hypothesis1–4 has been refuted in light of the so-called ‘missing heritability problem’”

• cites [1] Lander, E. S. The new genomics: global views of biology. Science 274, 536–539 (1996). sourceNot checked yet

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• cites [2] Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001). sourceNot checked yet

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• cites [3] Pritchard, J. K. & Cox, N. J. The allelic architecture of human disease genes: common disease–common variant... or not? Hum. Mol. Genet. 11, 2417–2423 (2002). sourceNot checked yet

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• cites [4] Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease. Nature Genet. 33, 228–237 (2003). sourceNot checked yet

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• cites [5] Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008). sourceNot checked yet

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• cites [6] Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). This paper provides a compendium of arguments, which were assembled by participants in a US National Institutes of Health (NIH) workshop, relating to the possible sources of missing heritability. sourceNot checked yet

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Affected individuals tend to carry a slight excess of risk variants because the distribution of risk allele counts in affecteds is skewed relative to unaffecteds.

“affected individuals will tend to carry a slight excess of risk variants, since the overall distribution of the number of risk alleles per affected individual is skewed relative to unaffecteds.”

If hundreds of mutations each below 1% frequency affect a disease, many more people will be doubly or triply heterozygous for combinations than homozygous for any single variant.

“If hundreds of mutations each at a frequency of less than 1% all affect a particular disease, then many more people will be doubly or triply heterozygous for various combinations, than are homozygous for any one variant.”

Under the rare allele model, most unaffected individuals are expected to carry one or more risk alleles.

“as shown in Figure 2b, the vast majority of unaffected individuals are expected to carry one or more risk alleles.”

Selection is efficient enough that even a fitness reduction of a fraction of a percent prevents allele frequencies from reaching common levels.

“But selection is sufficiently efficient that even a fitness reduction of a fraction of a percent will keep allele frequencies from reaching common levels.”

We still lack efficient procedures for identifying enhancers and other regulatory regions that polymorphisms could disrupt.

“we still lack efficient procedures for identifying enhancers and other regulatory regions that polymorphisms could disrupt38.”

• cites [38] Chorley, B. N. et al. Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: current and developing technologies. Mutat. Res. 659, 147–157 (2008). sourceNot checked yet

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A few dozen loci of moderate effect and intermediate frequency do not each explain several percent of disease risk in a population.

“it is simply not the case that a few dozen loci of moderate effect and intermediate frequency each explain several percent of disease risk in a population”

Both rare and common variant effects can be readily reconciled.

“Here I review 20 arguments for and against each of these models of the genetic basis of complex traits, and conclude that both classes of effect can be reconciled readily.”

Whole-exome sequence data show non-synonymous substitutions are even more skewed toward low frequencies, almost certainly reflecting purifying selection.

“the finding from whole-exome sequence data that non-synonymous substitutions are even more significantly skewed towards low frequencies almost certainly reflects the operation of purifying selection32–34.”

• cites [32] Cargill, M. et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nature Genet. 22, 231–238 (1999). sourceNot checked yet

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• cites [33] Kryukov, G. V., Pennacchio, L. A. & Sunyaev, S. R. Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am. J. Hum. Genet. 80, 727–739 (2007). sourceNot checked yet

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• cites [34] Zhu, Q. et al. A genome-wide comparison of the functional properties of rare and common genetic variants in humans. Am. J. Hum. Genet. 88, 458–468 (2011). sourceNot checked yet

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The number of loci contributing to a disease depends on baseline incidence, number of rare variants per locus, and genotypic relative risk.

“The total number of loci that may contribute to any disease is a function of the baseline disease incidence, the number of rare variants per locus, and the genotypic relative risk (GRR, namely, effect size).”

If half a dozen common variants explain 10% of population risk, the remaining risk is due to many variants each explaining much less than 1% with genotype relative risks below 1.1.

“If half a dozen common variants explain 10% of risk in the population, the remainder is attributable to a myriad of variants that each explain considerably less than 1% and have genotype relative risks less than 1.112.”

• cites [12] Park, J. H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nature Genet. 42, 570–575 (2010). This paper discusses how the true number of associations and their effect sizes can be inferred from observed GWAS results. sourceNot checked yet

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As a class, amino acid substitutions appear deleterious.

“As a class, amino acid substitutions appear to be deleterious.”

Loci detected by GWAS almost always explain only a small minority of the inferred genetic variance.

“loci detected by GWAS almost without exception explain a small minority of the inferred genetic variance5,6”

• cites [5] Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008). sourceNot checked yet

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• cites [6] Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). This paper provides a compendium of arguments, which were assembled by participants in a US National Institutes of Health (NIH) workshop, relating to the possible sources of missing heritability. sourceNot checked yet

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The existence of disease-promoting variants reflects a balance between mutation creating new susceptibility variants and selection preventing them from drifting into the population.

“The existence of disease-promoting variants reflects the balance between mutation creating new susceptibility variants, and selection preventing them from drifting into the population2,28.”

• cites [2] Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001). sourceNot checked yet

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• cites [28] Bulmer, M. G. Maintenance of genetic variability by mutation-selection balance: a child's guide through the jungle. Genome 31, 761–767 (1989). sourceNot checked yet

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Because heritability of risk is generally estimated as narrow-sense variance, interaction effects should be considered secondary to main effects of common and rare variants.

“since the heritability of risk is generally estimated as narrow sense variance, interaction effects should be considered secondary to the main effects of common and rare variants.”

• AssumptionRests on an assumption: The heritability of risk is generally estimated as narrow-sense variance.

If risk variants follow the same allele-frequency distribution as neutral variation, they will include many rare variants.

“If these follow the same distribution of allele frequencies as neutral variation, then they will include a large number of rare variants as well.”

The finding is consistent with the theory that selection keeps fitness-reducing alleles at low frequency at a large proportion of genes.

“However, the finding is consistent with the theory that selection keeps fitness-reducing alleles at a large proportion of genes at low frequency35.”

• cites [35] Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011). sourceNot checked yet

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If each rare variant explains most risk in only a few people, their effects will not explain enough population variance to be detected by standard GWAS.

“If each of these variants explains most of the risk in just a handful of people, their effects will not explain enough of the variance in a total population to be detected by standard GWAS procedures.”

Animal breeders have used genomic selection successfully for a decade, selecting sires and dams on predicted breeding values from full genomic marker sets.

“Animal breeders have been using ‘genomic selection’ methods with great success for the past decade72, basing their selection of sires and dams on the overall predicted breeding value determined from the full set of genomic markers that capture variation distributed throughout the genome.”

• cites [72] Goddard, M. E. & Hayes, B. J. Genomic selection. J. Animal Breed. Genet. 124, 323–330 (2007). sourceNot checked yet

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If rare variants contribute, they do so in the context of many other genetic variants in pedigrees, as shown by a recent analysis of schizophrenia genetics.

“If rare variants are contributing then they are doing so in the context of many other genetic variants in the pedigrees, as shown by a recent analysis of schizophrenia genetics54.”

• cites [54] Ruderfer, D. M. et al. A family-based study of common polygenic variation and risk of schizophrenia. Mol. Psychiatry 16, 887–888 (2011). sourceNot checked yet

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Common variants are often consistent across populations, such as between Caucasians and Asians, despite differences in allele frequencies.

“common variants are often consistent across populations — such as between Caucasians and Asians — despite differences in allele frequencies65,66.”

• cites [65] Waters, K. M. et al. Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups. PLoS Genet. 6, e1001078 (2010). sourceNot checked yet

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• cites [66] Shriner, D. et al. Transferability and fine-mapping of genome-wide associated loci for adult height across human populations. PLoS ONE 4, e8398 (2009). sourceNot checked yet

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In humans, using all nominally significant SNPs rather than only GWAS-significant ones captures much more genetic variance than the highly significant loci explain.

“Similarly in humans, by taking all nominally significant SNPs rather than just the GWAS significant ones, it is possible to capture much more of the genetic variance than is explained by the highly significant loci73,74”

• cites [73] Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009). sourceNot checked yet

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• cites [74] So, H.-C., Li, M. & Sham, P. C. Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet. Epidemiol. 35, 447–456 (2011).Not checked yet

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Synthetic association is not expected to account for most missing heritability.

“Synthetic association is not expected to account for most of the missing heritability19, 51”

• cites [19] Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011). sourceNot checked yet

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• cites [51] Anderson, C. A., Soranzo, N. Barrett, J. C. & Zeggini, E. Synthetic associations are unlikely to account for many common disease genome-wide association signals. PLoS Biol. 9, e1000580 (2011). sourceNot checked yet

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Partitioning genetic variance by chromosome for diverse traits shows the proportion of variance explained is consistently proportional to chromosome length.

“partitioning of the genetic variance on a chromosome-by-chromosome basis, for a diverse set of traits, shows that the proportion of variance explained is consistently proportional to chromosome length75,76.”

• cites [75] Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genet. 43, 519–525 (2011). sourceNot checked yet

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• cites [76] Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011). sourceNot checked yet

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Half of X-linked mental retardation cases can be ascribed to rare protein-coding mutations found by sequencing X-chromosomal exons.

“the demonstration that half of the cases of X-linked mental retardation can be ascribed to rare protein-coding mutations, which were discovered by comprehensive sequencing of X-chromosomal exons43.”

• cites [43] Tarpey, P. S. et al. A systematic, large-scale resequencing screen of X-chromosome coding exons in mental retardation. Nature Genet. 41, 535–543 (2009). This was one of the first whole-exome sequencing studies that was designed to detect rare variants of large effect. sourceNot checked yet

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Rare variants can easily induce apparently additive statistical effects because homozygotes for a tagging variant are twice as likely as heterozygotes to carry the rare variant.

“However, it turns out that rare variants can easily induce apparently additive effects statistically, because the homozygotes for the tagging variant are twice as likely as the heterozygotes to carry the rare variant.”

There is extensive precedent for rare variants contributing substantially to special cases of complex disease, including infection risk.

“There is thus extensive precedent for rare variants contributing substantially to special cases of complex disease, including risk of infection44.”

• cites [44] George, J. et al. Two human MYD88 variants, S34Y and R98C, interfere with MyD88–IRAK4–Myddosome assembly. J. Biol. Chem. 286, 1341–1353 (2011). sourceNot checked yet

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Standard quantitative genetic theory provides ample support for the infinitesimal model.

“standard quantitative genetic theory provides ample support for the infinitesimal model7,8.”

• cites [7] Fisher, R. A. The Genetical Theory of Natural Selection (Oxford Univ. Press, Oxford, 1930). sourceNot checked yet

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• cites [8] Visscher, P. M., Hill, W. G. & Wray, N. Heritability in the genomics era — errors and misconceptions. Nature Rev. Genet. 9, 255–266 (2008). This is an accessible modern introduction to the concept of heritability. sourceNot checked yet

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Regression of phenotypic similarity on genetic relatedness implies that common variants capture the majority of genetic variants.

“A multivariate version of this approach, implemented by regression of phenotypic similarity on genetic relatedness, also implies that common variants capture the majority of the genetic variants71.”

• cites [71] Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010). This paper introduces a multivariate approach for capturing the effects of common variant associations genome-wide. sourceNot checked yet

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Direct empirical support for the infinitesimal model comes from genomic variance analyses.

“Direct empirical support for the infinitesimal model comes from genomic variance analyses70, 71.”

• cites [70] Schork, N. J. Genome partitioning and whole genome analysis. Adv. Genet. 42, 299–322 (2001). sourceNot checked yet

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• cites [71] Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010). This paper introduces a multivariate approach for capturing the effects of common variant associations genome-wide. sourceNot checked yet

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Multiplicative interactions between rare variants cannot be measured in GWAS because of low power.

“Multiplicative interactions between rare variants cannot be measured in GWAS because of low power”

There is no evidence that rare single nucleotide variants at the same loci are the major source of genetic variance.

“There is no evidence to support the hypothesis that rare single nucleotide variants at the same loci are the major source of genetic variance9”

• cites [9] Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nature Rev. Genet. 11, 415–425 (2010). sourceNot checked yet

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Sibling recurrence rates are generally much higher than can be attributed to rare variants with the postulated effect sizes.

“Sibling recurrence rates are generally much higher than can be attributed to rare variants with the postulated effect sizes17.”

• cites [17] Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nature Genet. 40, 695–701 (2008). sourceNot checked yet

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Five percent of schizophrenia cases and five percent of autism cases have been attributed to CNVs at fewer than six genomic locations.

“Five percent of cases of schizophrenia and of autism have each been attributed to CNVs at fewer than a half dozen genomic locations46–48.”

• cites [46] Stefansson, H. et al. Large recurrent microdeletions associated with schizophrenia. Nature 455, 232–236 (2008). sourceNot checked yet

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• cites [47] Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007). This paper provided the first demonstration that rare copy number variants associate with psychiatric disease. sourceNot checked yet

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• cites [48] Cook, E. H. Jr & Scherer, S. W. Copy-number variations associated with neuropsychiatric conditions. Nature 455, 919–923 (2008). sourceNot checked yet

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Linkage disequilibrium considerations using standard quantitative genetic theory place strong constraints on the number and effect-size range of rare variants that could make a major contribution yet remain undetected in GWAS.

“considerations of linkage disequilibrium using standard quantitative genetic theory19 place strong constraints on the number of rare variants and their range of effect sizes that would be compatible with them making a major contribution yet remaining undetected in GWAS”

• cites [19] Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011). sourceNot checked yet

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Rare variants alone cannot explain the demographic distribution of disease incidence.

“In other words, rare variants alone cannot explain the demographic distribution of disease incidence.”

Analysis of allele frequency distributions in GWAS data is a major argument against rare variants as the predominant source of missing heritability.

“A major argument against rare variants as the predominant source of missing heritability comes from the analysis of allele frequency distributions of GWAS data19.”

• cites [19] Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011). sourceNot checked yet

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The OMIM database provides examples of near-Mendelian cases of many relatively common disorders.

“perusal of the Online Mendelian Inheritance in Man (OMIM) database provides examples of near-Mendelian cases of many relatively common disorders42.”

• cites [42] Hamosh, A. et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl. Acids Res. 30, 52–55 (2002). sourceNot checked yet

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A common variant present in 20% of cases and explaining 1% of susceptibility may actually reflect two or three rare variants that each substantially elevate risk in 1-2% of cases.

“Thus, a common variant present in 20% of cases that mathematically explains one percent of disease susceptibility may actually simply report the activity of two or three rare variants that each substantially elevate risk in one or two percent of the cases.”

Causal common variants associated with continuous endophenotypes have been associated with disease in many cases.

“In many cases, causal common variants associated with a continuous endophenotype have been associated with disease [eg80–82]”

• cites [80] Kilpeläinen, T. O. et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nature Genet. 43, 753–760 (2011). sourceNot checked yet

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• cites [81] Bis, J. C. et al. Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque. Nature Genet. 43, 940–947 (2011). sourceNot checked yet

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• cites [82] Speliotes, E. K. et al. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet. 7, e1001324 (2011). sourceNot checked yet

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Due to improved imputation, most minor alleles down to 5% frequency can now be inferred with high accuracy if the reference panel is appropriately matched for population structure.

“Due to improved methods for imputation, most minor alleles down to 5% frequency can now be inferred with high accuracy, if the reference panel is appropriately matched for population structure129.”

• cites [129] Shea, J. et al. Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nature Genet. 43, 801–805 (2011). sourceNot checked yet

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Tumor samples have discrete transcriptome profile differences.

“Thus, tumor samples have discrete transcriptome profile differences89,90”

• cites [89] Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000). sourceNot checked yet

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• cites [90] Slavov, N. & Dawson, K. A. Correlation signature of the macroscopic states of the gene regulatory network in cancer. Proc. Natl Acad. Sci. USA 106, 4079–4084 (2009). sourceNot checked yet

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At least some cortex samples from autistic brains converge on a common transcriptional profile.

“Similarly, at least some cortex samples from autistic brains converge on a common transcriptional profile91.”

• cites [91] Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011). sourceNot checked yet

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Transgressive segregation in mapping populations of mice and flies from eight founder strains provides strong empirical support for common polygenes.

“the phenomenon of transgressive segregation in mapping populations of mice and flies established from eight founder strains provides strong empirical support for the existence of common polygenes94–96.”

• cites [94] Aylor, D. L. et al. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Res. 21, 1213–12122 (2011). sourceNot checked yet

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• cites [95] Philip, V. M. et al. Genetic analysis in the Collaborative Cross breeding population. Genome Res. 21, 1223–1238 (2011). sourceNot checked yet

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• cites [96] Macdonald, S. J. & Long, A. D. Joint estimates of quantitative trait locus effect and frequency using synthetic recombinant populations of Drosophila melanogaster. Genetics 176, 1261–1281 (2007). sourceNot checked yet

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Threshold-dependent models postulate that disease is more likely in individuals with extreme values of underlying endophenotypes.

“Threshold-dependent models77 postulate that disease is more likely to arise in individuals who have extreme values of underlying endophenotypes78,79.”

• cites [77] Falconer, D. S. Introduction to Quantitative Genetics Ch. 18 (Longman, New York, 1981).Not checked yet

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• cites [78] Cannon, T. D. & Keller, M. C. Endophenotypes in the genetic analysis of mental disorders. Annu. Rev. Clin. Psychol. 2, 267–290 (2006). sourceNot checked yet

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• cites [79] Kendler, K. S. & Meale, M. C. Endophenotype: a conceptual analysis. Mol. Psychiatry 15, 789–797 (2010). sourceNot checked yet

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eQTL analysis shows gene expression and splicing are heavily influenced by common variants, possibly for most transcripts.

“eQTL analysis shows that gene expression and splicing are heavily influenced by common variants, possibly for the majority of transcripts86–88.”

• cites [86] Cookson, W. et al. Mapping complex disease traits with global gene expression. Nature Rev. Genet. 10, 184–194 (2009). sourceNot checked yet

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• cites [87] Majewski, J. & Pastinen, T. The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet. 27, 72–79 (2011). sourceNot checked yet

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• cites [88] Lalonde, E. et al. RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression. Genome Res. 21, 545–554 (2011). sourceNot checked yet

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Under the infinitesimal model, disease risk should blend smoothly when unrelated individuals reproduce.

“In the infinitesimal model, disease risk ought to blend smoothly when unrelated people have children.”

The common variant of moderate effect version of the CD-CV model needs to be discarded.

“It is the common variant of moderate effect version of the CD-CV model that needs to be discarded.”

Classical quantitative genetics provides overwhelming evidence that traits are regulated by many loci with a wide range of effect sizes.

“The overwhelming evidence from classical quantitative genetics is that traits are regulated by many loci with a wide range of effect sizes.”

Disease risk differs pervasively between geographic areas in ways not obviously explained by genetic differentiation.

“(i) the pervasiveness of differences in disease risk between geographic areas that are not obviously explained by genetic differentiation”

Some QTL that coarse mapping detects are actually multiple linked variants that cannot be separated into individual SNPs.

“while in an unknown proportion of cases, QTL will turn out to be due to multiple linked variants that coarse mapping fails to resolve into individual SNPs103.”

• cites [103] Pasyukova, E. G., Vieira, C. & Mackay, T. F. C. Deficiency mapping of quantitative trait loci affecting longevity in Drosophila melanogaster. Genetics 156, 1129–1146 (2000). sourceNot checked yet

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Narrow-sense genetic effects alone are unlikely to fully explain disease risk.

“This does not counter the existence of thousands of small effect loci affecting each trait or disease risk profile, but it suggests that the narrow-sense genetic effects alone are unlikely to be sufficient explanation.”

The data are generally consistent with the infinitesimal model to a first approximation, even where variants remain unidentified.

“Simply put, the data is generally consistent with the infinitesimal model to a first approximation, even where variants are yet to be identified.”

Facial features are an example of traits that do not follow only blending or Mendelian inheritance.

“Granularity of traits is difficult to document, but facial features provide a good example of a suite of traits that do not simply follow either blending or Mendelian inheritance105.”

• cites [105] Jelenkovic, A., Poveda, A,, Susanne, C. & Rebato, E. Contribution of genetics and environment to craniofacial anthropometric phenotypes in Belgian nuclear families. Hum. Biol. 80, 637–654 (2008). sourceNot checked yet

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Various blood disorders correlate with extreme values for the major vectors of modules of gene expression.

“By contrast, various blood disorders have been shown to correlate with extreme values for the major vectors of modules of gene expression92.”

• cites [92] Chaussabel, D. et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29, 150–164 (2008). sourceNot checked yet

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Infinitesimal effects are inconsistent with many demographic patterns that indicate gene-environment and complex genetic interactions.

“As with the rare allele model, infinitesimal effects are also not consistent with a wide range of demographic effects that are indicative of GxE interactions and complex genetic interactions.”

The simplest explanation for GWAS failing to find missing heritability is that expectations were based on unrealistic prior assumptions about effect sizes.

“While there has been a very public focus on the failure of GWAS to find the missing heritability5,6, the simplest explanation for this is that the expectations were based on unrealistic prior assumption of effect sizes.”

• cites [5] Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008). sourceNot checked yet

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• cites [6] Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). This paper provides a compendium of arguments, which were assembled by participants in a US National Institutes of Health (NIH) workshop, relating to the possible sources of missing heritability. sourceNot checked yet

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Sample sizes can be extrapolated from the range of variant effects in initial discovery samples.

“In fact, sample sizes can be extrapolated from the range of variant effects in initial discovery samples12”

• cites [12] Park, J. H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nature Genet. 42, 570–575 (2010). This paper discusses how the true number of associations and their effect sizes can be inferred from observed GWAS results. sourceNot checked yet

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With more alleles influencing a trait, random mating should produce lower among-individual variance because most people carry similar counts of risk alleles.

“The larger the number of alleles affecting a trait, the lower the among-individual variance should be under random mating since most individuals will share similar numbers of risk alleles.”

In height, the GIANT consortium confirmed inferences from 30,000 individuals when it expanded to 180,000, finding an additional 180 or so loci.

“in the case of height, the GIANT consortium (http://www.broadinstitute.org/collaboration/giant/) confirmed the inferences from 30,000 individuals100 when they jumped to 180,000, finding an additional 180 or so loci14.”

• cites [100] Weedon, M. N. et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nature Genet. 40, 575–583 (2008). sourceNot checked yet

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• cites [14] Lango-Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010). sourceNot checked yet

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GWAS has successfully identified thousands of common variants.

“GWAS has successfully identified thousands of common variants99”

• cites [99] Manolio, T. A., Brooks, L. D. & Collins, F. S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008). sourceNot checked yet

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Re-sequencing of evolved outbred Drosophila melanogaster populations provides strong support for selection on thousands of variants being responsible for changes in body size and fecundity.

“re-sequencing of evolved outbred populations of Drosophila melanogaster has provided strong support for selection on thousands of variants being responsible for changes in the highly complex traits of body size and fecundity97,98.”

• cites [97] Burke, M. K. et al. Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature 467, 587–590 (2010). This paper uses an 'evolve-and-resequence' strategy to demonstrate the pervasive polygenic basis of complex traits. sourceNot checked yet

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• cites [98] Turner, T. L. et al. Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster. PLoS Genet. 7, e1001336 (2011). sourceNot checked yet

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Risk for one disease is conditioned on another disease in the same person.

“(iii) conditioning of the risk for one disease on another disease in the same individual106,107.”

• cites [106] Rzhetsky, A., Wajngurt, D., Park, N. & Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA 104, 11694–11699 (2007). sourceNot checked yet

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• cites [107] Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010). This study develops a strategy that integrates whole-genome sequence and environmental exposure information to assess personal risk of disease. sourceNot checked yet

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Once most alleles are accepted to have relative risks below 1.2, hundreds of thousands of individuals are required to identify more than a few dozen loci explaining more than 20% of variance.

“Once it is accepted that most alleles are associated with relative risks less than 1.2, it becomes clear that hundreds of thousands of individuals are required to identify more than a few dozen loci than explain more than 20% of the variance.”

In many crosses, the estimated effect size is inflated by the Beavis effect or winner's curse.

“In many cases as well, the effect size estimated in the cross will be an overestimate due to the Beavis effect/winner’s curse101,102”

• cites [101] Xu, S. Theoretical basis of the Beavis effect. Genetics 165, 2259–2268 (2003). sourceNot checked yet

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• cites [102] Zhong, R. & Prentice, R. L. Correcting “winner's curse” in odds ratios from genome-wide association findings for major complex human diseases. Genet. Epidemiol. 34, 78–91 (2010). sourceNot checked yet

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This is true of threshold-dependent characters and cryptic variation as well as continuous variation.

“This is as true of threshold-dependent characters and cryptic variation93 as it is of continuous variation.”

• cites [93] Gibson, G. & Dworkin, I. M. Uncovering cryptic genetic variation. Nature Rev. Genet. 5, 681–690 (2004). sourceNot checked yet

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In model organism research, pedigree analyses and genetic crosses using linkage mapping almost always identify multiple variants influencing the quantitative trait.

“In model organism research, both pedigree analyses and genetic crosses in which linkage mapping is used to localize QTL almost always lead to the identification of multiple variants influencing the quantitative trait of interest20.”

• cites [20] Mackay, T. F. C. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35, 303–339 (2001). sourceNot checked yet

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In some cases, associations between common variants and endophenotypes have been confirmed by in vitro biochemical assays for structural and regulatory effects.

“in some cases these have been confirmed by in vitro biochemical assays for structural and regulatory effects [eg83–85].”

• cites [83] Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010). This was an important case study showing how to go from association study to molecular function of a specific variant. sourceNot checked yet

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• cites [84] Wang, Y. et al. Whole-genome association study identifies STK39 as a hypertension susceptibility gene. Proc. Natl Acad. Sci. USA 106, 226–231 (2009). sourceNot checked yet

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• cites [85] Bertram, L., Lill, C. M. & Tanzi, R. E. The genetics of Alzheimer disease: back to the future. Neuron 68, 270–281 (2010). sourceNot checked yet

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Disease is generally a threshold-dependent response over a continuous liability.

“The typical resolution of the observation that disease is categorical (people are either cases or controls) yet genetic contributions are complex, is that disease is generally a threshold-dependent response superimposed over a continuous liability120.”

• cites [120] Rendel, J. M. Canalization and Gene Control (Academic Press, New York, 1967).Not checked yet

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MutDB holds a much larger set of rare coding variants linked to disease or predicted damaging.

“MutDB112(http://mutdb.org/) contains a much larger number of rare coding variants that are either associated with disease or predicted to be damaging.”

• cites [112] Stenson, P. D. et al. The Human Gene Mutation Database: 2008 update. Genome Med. 1, 13 (2009). sourceNot checked yet

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Missing heritability is inferred against heritability estimates that usually come from family studies, which themselves lack direct estimates of genetic contribution.

“Missing heritability is inferred with respect to heritability estimates that generally derive from family studies, where ironically direct estimates of the genetic contribution are lacking.”

Even alleles with large effects are subject to background modification and incomplete penetrance.

“but it must be recognized that even large effect alleles are subject to background modification and incomplete penetrance.”

It is difficult to estimate how much variance GWAS-captured variants should explain in outbred populations.

“Consequently, it is difficult to actually estimate how much of the variance GWAS-captured variants should explain in outbred populations.”

Liability is likely established by additive and infinitesimal contributions of hundreds of polymorphisms, each regulating biochemical traits that affect the phenotype.

“Liability is quite likely to be established by the additive and infinitesimal contributions of hundreds of polymorphisms, each regulating a series of biochemical traits that impinge on the phenotype: metabolite abundance, gene expression, and hormone levels, for example.”

Height has a heritability as high as 80%.

“However, in the case of height, where heritability is as high as 80%, over 50% of the phenotype can be attributed to common variants using the mixed linear modeling approach71”

• cites [71] Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010). This paper introduces a multivariate approach for capturing the effects of common variant associations genome-wide. sourceNot checked yet

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Once allele-frequency skews and incomplete LD are corrected, nearly all of height's genetic contribution is assigned to genotypic variation.

“and after adjustment for allele frequency skews and incomplete LD, essentially the entire genetic contribution has been ascribed to genotypic variation.”

• cites [71] Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010). This paper introduces a multivariate approach for capturing the effects of common variant associations genome-wide. sourceNot checked yet

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For BMI, those methods recover only about half or slightly more of the expected genotypic variance.

“Yet the same methods applied to BMI do not capture much more than half of the expected genotypic variance, and it is not clear that they are as efficient for schizophrenia, arthritis or intelligence75,76.”

• cites [75] Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genet. 43, 519–525 (2011). sourceNot checked yet

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• cites [76] Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011). sourceNot checked yet

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Mixed linear modeling attributes over half of height phenotypic variation to common variants.

“over 50% of the phenotype can be attributed to common variants using the mixed linear modeling approach71”

• cites [71] Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010). This paper introduces a multivariate approach for capturing the effects of common variant associations genome-wide. sourceNot checked yet

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In chronic hepatitis C, the initial GWAS signal for protection against anemia captures two less common causal coding variants at ITPA.

“The case of protection against anemia in chronic hepatitis C, where the initial GWAS discovery clearly captures two less common causal coding variants at the ITPA locus, is a good example110.”

• cites [110] Fellay, J. et al. ITPA gene variants protect against anemia in patients treated for chronic hepatitis C. Nature 464, 405–408 (2010). sourceNot checked yet

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Because even rare variants have variable penetrance, the line between infinitesimal and major-effect models of disease susceptibility becomes blurred.

“The notion that even rare variants have variable penetrance thus blurs the distinction between infinitesimal and major-effect models of disease susceptibility.”