The allelic spectra of common diseases may resemble the allelic spectrum of the full genome

Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases

Genome-wide association studies (GWAS) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs. Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor (<50%) alleles. Indeed, we found that the overall proportion of risk alleles was higher among alleles with frequency <50% (minor alleles) compared to that in the group of major alleles. We hypothesized that negative selection may have different effects on environment (or lifestyle)-dependent versus environment (or lifestyle)-independent diseases. We used an environment/lifestyle index (ELI) to assess influence of environmental/lifestyle factors on disease etiology. ELI was defined as the number of publications mentioning "environment" or "lifestyle" AND disease per 1,000 disease-mentioning publications. We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model, while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection. We hypothesized that previously selectively neutral variants become risk alleles when environment changes. The hypothesis of ancestrally neutral, currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies. The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases.

Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations

Background

Genome-wide association studies give insight into the genetic basis of common diseases. An open question is whether the allele frequency distributions and ancestral vs. derived states of disease-associated alleles differ from the rest of the genome. Characteristics of disease-associated alleles can be used to increase the yield of future studies.

Methods

The set of all common disease-associated alleles found in genome-wide association studies prior to January 2010 was analyzed and compared with HapMap and theoretical null expectations. In addition, allele frequency distributions of different disease classes were assessed. Ages of HapMap and disease-associated alleles were also estimated.

Results

The allele frequency distribution of HapMap alleles was qualitatively similar to neutral expectations. However, disease-associated alleles were more likely to be low frequency derived alleles relative to null expectations. 43.7% of disease-associated alleles were ancestral alleles. The mean frequency of disease-associated alleles was less than randomly chosen CEU HapMap alleles (0.394 vs. 0.610, after accounting for probability of detection). Similar patterns were observed for the subset of disease-associated alleles that have been verified in multiple studies. SNPs implicated in genome-wide association studies were enriched for young SNPs compared to randomly selected HapMap loci. Odds ratios of disease-associated alleles tended to be less than 1.5 and varied by frequency, confirming previous studies.

Conclusions

Alleles associated with genetic disease differ from randomly selected HapMap alleles and neutral expectations. The evolutionary history of alleles (frequency and ancestral vs. derived state) influences whether they are implicated in genome-wide assocation studies.

Appropriate Use of Information on Family History of Disease in Recruitment for Linkage Analysis Studies

When conducting genetic studies for complex traits, large samples are commonly required to detect any of the number of genes with relatively low effect thought to underly such traits. This is because, in contrast to monogenic diseases, complex traits typically result from a number of different genetic pathways (genetic heterogeneity) and any sample is likely to contain a considerable fraction of sporadic cases (phenocopies). Such samples are time-consuming and costly to recruit and analyse. Methods which might be used to decrease sample size include attempting to select families, with the aim of reducing genetic heterogeneity or phenocopy rate within the sample. Selecting cases with positive family history of disease should reduce the phenocopy rate, and this strategy has been employed in linkage studies of complex disease, although evaluations of such a strategy have been equivocal. This paper shows how identity by descent (IBD) distributions may be calculated for affected relative pairs recruited conditional on the affection status of a third relative. These distributions are then used to calculate expected power in affected sib and half-sib linkage studies when recruitment is conditional on family history of disease. We consider the proxy conditions of recruitment conditional on disease in an affected parent or third sibling with single-locus and additive multilocus genetic models. We show that while such selection strategies can reduce power if disease risk alleles are common and environmental heterogeneity low, under models more likely to underly common complex diseases power will generally be increased, and that this effect is greater as more loci are involved. Though the proxy cases studied are more extreme than a general strategy of asking potential recruits whether they have any family history of disease, these results suggest that conditional recruitment is more generally useful than previous studies have suggested.

Multiple rare variants in NPC1L1 associated with reduced sterol absorption and plasma low-density lipoprotein levels

An approach to understand quantitative traits was recently proposed based on the finding that nonsynonymous (NS) sequence variants in certain genes are preferentially enriched at one extreme of the population distribution. The NS variants, although individually rare, are cumulatively frequent and influence quantitative traits, such as plasma lipoprotein levels. Here, we use the NS variant technique to demonstrate that genetic variation in NPC1L1 contributes to variability in cholesterol absorption and plasma levels of low-density lipoproteins (LDLs). The ratio of plasma campesterol (a plant sterol) to lathosterol (a cholesterol precursor) was used to estimate relative cholesterol absorption in a population-based study. Nonsynonymous sequence variations in NPC1L1 were five times more common in low absorbers (n = 26 of 256) than in high absorbers (n = 5 of 256) (P < 0.001). The rare variants identified in low absorbers were found in 6% of 1,832 African-Americans and were associated with lower plasma levels of LDL cholesterol (LDL-C) (96 +/- 36 mg/dl vs. 105 +/- 36 mg/dl; P = 0.005). These data, together with prior findings, reveal a genetic architecture for LDL-C levels that does not conform to current models for quantitative traits and indicate that a significant fraction of genetic variance in LDL-C is due to multiple alleles with modest effects that are present at low frequencies in the population.

Genome-wide association studies: theoretical and practical concerns

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.