Genomic DNA pooling for whole-genome association scans in complex disease: empirical demonstration of efficacy in rheumatoid arthritis

A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder

The genetic basis of bipolar disorder has long been thought to be complex, with the potential involvement of multiple genes, but methods to analyze populations with respect to this complexity have only recently become available. We have carried out a genome-wide association study of bipolar disorder by genotyping over 550,000 single-nucleotide polymorphisms (SNPs) in two independent case-control samples of European origin. The initial association screen was performed using pooled DNA, and selected SNPs were confirmed by individual genotyping. While DNA pooling reduces power to detect genetic associations, there is a substantial cost saving and gain in efficiency. A total of 88 SNPs, representing 80 different genes, met the prior criteria for replication in both samples. Effect sizes were modest: no single SNP of large effect was detected. Of 37 SNPs selected for individual genotyping, the strongest association signal was detected at a marker within the first intron of diacylglycerol kinase eta (DGKH; P=1.5 x 10(-8), experiment-wide P<0.01, OR=1.59). This gene encodes DGKH, a key protein in the lithium-sensitive phosphatidyl inositol pathway. This first genome-wide association study of bipolar disorder shows that several genes, each of modest effect, reproducibly influence disease risk. Bipolar disorder may be a polygenic disease.

A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples

BACKGROUND

One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations.

RESULTS

We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis.

CONCLUSION

Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects.

Insights and applications from sequencing the bovine genome

Humans have sought to improve/tailor cattle since their domestication a few thousand years ago. Up until the last 40–50 years, consistent genetic improvement of cattle was a hit or miss proposition. Recent progress has been more rapid, thanks to applications of quantitative genetics to breeding schemes. With the availability of the bovine genome sequence, genetic selection and on-farm management are likely to be revolutionised yet again. Genetic association studies that were previously impossible to carry out due to a lack of markers are now possible. In addition to improved genetic mapping of economic traits, the bovine genome sequence allows us to create a common context for genetic and physiological data, which will provide novel insights into gene regulation and function.

Genetic study of the melanin-concentrating hormone receptor 2 in childhood and adulthood severe obesity

CONTEXT

The melanin-concentrating hormone receptor 2 (MCHR2) is a G protein-coupled receptor for melanin-concentrating hormone, a neuropeptide that plays an important role in feeding behaviors. MCHR2 maps on chromosome 6q16.3, in a susceptibility locus for childhood obesity.

OBJECTIVE

The aim of this study was to investigate the association between MCHR2 variation and human obesity.

DESIGN

Case control and family-based studies were performed.

PARTICIPANTS

A total of 141 obese children and 24 nonobese adult subjects was sequenced, and case-control analyses were conducted using 628 severely obese children and 1,401 controls.

RESULTS

There were 11 single nucleotide polymorphisms (SNPs) identified. We showed nominal association among -38,245 ATG A/G SNP (P = 0.03; 95% confidence interval 1.02-1.34; odds ratio 1.17), A76A T/C SNP (P = 0.03; 95% confidence interval 0.58-0.97; odds ratio 0.75), and childhood obesity. Analysis of 645 trios with childhood obesity supported further the A76A T/C association, showing an overtransmission to obese children of the at risk T allele (59.0%; P = 0.01), especially in children with most severe forms of obesity (Z score of body mass index > 4) (67.0%; P = 0.003). The A76A at risk T allele was also associated with overeating during meals (P = 0.02) in an additional group of 102 nonobese children. None of the MCHR2 variants, including the A76A SNP, showed association with adult severe obesity, although a trend for association of the T allele of this variant with food disinhibition (P = 0.06) and higher hunger (P = 0.09) was found. This variant was not associated with childhood obesity in an independent case-control study, including 1,573 subjects (P = 0.98). Moreover, the A76A SNP did not explain the linkage on the 6q locus.

CONCLUSION

Our results altogether suggest that MCHR2 is not a major contributor to polygenic obesity and support a modest effect of the A76A SNP on food intake abnormalities in childhood.

Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies

Background

Genetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip^® Mapping 500 K Array.

Results

In comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip^® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects.

Conclusion

We conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip^® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

A DArT platform for quantitative bulked segregant analysis

BACKGROUND

Bulked segregant analysis (BSA) identifies molecular markers associated with a phenotype by screening two DNA pools of phenotypically distinct plants for markers with skewed allele frequencies. In contrast to gel-based markers, hybridization-based markers such as SFP, DArT or SNP generate quantitative allele-frequency estimates. Only DArT, however, combines this advantage with low development and assay costs and the ability to be deployed for any plant species irrespective of its ploidy level. Here we investigate the suitability of DArT for BSA applications using a barley array as an example.

RESULTS

In a first test experiment, we compared two bulks of 40 Steptoe/Morex DH plants with contrasting pubescent leaves (mPub) alleles on chromosome 3H. At optimized levels of experimental replication and marker-selection threshold, the BSA scan identified 433 polymorphic markers. The relative hybridization contrast between bulks accurately reflected the between-bulk difference in the frequency of the mPub allele (r = 0.96). The 'platform noise' of DArT assays, estimated by comparing two identical aliquots of a DNA mixture, was significantly lower than the 'pooling noise' reflecting the binomial sampling variance of the bulking process. The allele-frequency difference on chromosome 3H increased in the vicinity of mPub and peaked at the marker with the smallest distance from mPub (4.6 cM). In a validation experiment with only 20 plants per bulk we identified an aluminum (Al) tolerance locus in a Dayton/Zhepi2 DH population on chromosome 4H with < 0.8 cM precision, the same Al-tolerance locus that had been mapped before in other barley populations.

CONCLUSION

DArT-BSA identifies genetic loci that influence phenotypic characters in barley with at least 5 cM accuracy and should prove useful as a generic tool for high-throughput, quantitative BSA in plants irrespective of their ploidy level.

Sequence variants in the genes for the interleukin-23 receptor (IL23R) and its ligand (IL12B) confer protection against psoriasis

Psoriasis is an inflammatory skin disorder that is inherited as a multifactorial trait. Genetic analyses have repeatedly identified a primary disease susceptibility locus lying within the major histocompatibility complex (MHC), on chromosome 6p21. A small number of non-MHC susceptibility loci have also been identified. These regions tend to overlap with susceptibility intervals for Crohn's disease and atopic dermatitis, suggesting the possibility that genetic variants affecting inflammatory pathways may contribute to the pathogenesis of multiple disorders. Here, we report a genetic analysis of the interleukin 23 receptor gene (IL23R), which was recently identified as a susceptibility determinant for Crohn's disease. We initially examined the results of a whole-genome association scan, carried out on 318 cases and 288 controls. We observed a significant increase of a non-synonymous substitution (p.Arg381Gln) among controls (P = 0.00036). We validated this finding by extending our cohort to include a further 519 cases and 528 controls. In the overall sample, the frequency of the 381Gln allele was 3.6% in cases and 7% in controls, yielding a P value of 0.00014. Next, we examined genetic variation at the IL12RB1, IL23A and IL12B genes, respectively, encoding the second subunit of the IL23R receptor and the two subunits of its ligand. This analysis identified independent associations for IL12B SNPs rs10045431 (P value for the extended dataset = 0.0001) and rs3212227 (P = 0.036). Altogether, these findings indicate that genes participating in IL23 signalling play a significant role in the pathogenesis of chronic epithelial inflammation.