Guidelines for investigating causality of sequence variants in human disease.

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Meta-analysis of genome-wide association studies identifies common variants in CTNNA2 associated with excitement-seeking.

The tendency to seek stimulating activities and intense sensations define excitement-seeking, a personality trait akin to some aspects of sensation-seeking. This trait is a central feature of extraversion and is a component of the multifaceted impulsivity construct. Those who score high on measures of excitement-seeking are more likely to smoke, use other drugs, gamble, drive recklessly, have unsafe/unprotected sex and engage in other risky behaviors of clinical and social relevance. To identify common genetic variants associated with the Excitement-Seeking scale of the Revised NEO Personality Inventory, we performed genome-wide association studies in six samples of European ancestry (N=7860), and combined the results in a meta-analysis. We identified a genome-wide significant association between the Excitement-Seeking scale and rs7600563 (P=2 × 10(-8)). This single-nucleotide polymorphism maps within the catenin cadherin-associated protein, alpha 2 (CTNNA2) gene, which encodes for a brain-expressed α-catenin critical for synaptic contact. The effect of rs7600563 was in the same direction in all six samples, but did not replicate in additional samples (N=5105). The results provide insight into the genetics of excitement-seeking and risk-taking, and are relevant to hyperactivity, substance use, antisocial and bipolar disorders.

Genome-wide association scan for five major dimensions of personality.

Personality traits are summarized by five broad dimensions with pervasive influences on major life outcomes, strong links to psychiatric disorders and clear heritable components. To identify genetic variants associated with each of the five dimensions of personality we performed a genome-wide association (GWA) scan of 3972 individuals from a genetically isolated population within Sardinia, Italy. On the basis of the analyses of 362 129 single-nucleotide polymorphisms we found several strong signals within or near genes previously implicated in psychiatric disorders. They include the association of neuroticism with SNAP25 (rs362584, P=5 x 10(-5)), extraversion with BDNF and two cadherin genes (CDH13 and CDH23; Ps<5 x 10(-5)), openness with CNTNAP2 (rs10251794, P=3 x 10(-5)), agreeableness with CLOCK (rs6832769, P=9 x 10(-6)) and conscientiousness with DYRK1A (rs2835731, P=3 x 10(-5)). Effect sizes were small (less than 1% of variance), and most failed to replicate in the follow-up independent samples (N up to 3903), though the association between agreeableness and CLOCK was supported in two of three replication samples (overall P=2 x 10(-5)). We infer that a large number of loci may influence personality traits and disorders, requiring larger sample sizes for the GWA approach to confidently identify associated genetic variants.

Meta-analysis of 32 genome-wide linkage studies of schizophrenia.

A genome scan meta-analysis (GSMA) was carried out on 32 independent genome-wide linkage scan analyses that included 3255 pedigrees with 7413 genotyped cases affected with schizophrenia (SCZ) or related disorders. The primary GSMA divided the autosomes into 120 bins, rank-ordered the bins within each study according to the most positive linkage result in each bin, summed these ranks (weighted for study size) for each bin across studies and determined the empirical probability of a given summed rank (P(SR)) by simulation. Suggestive evidence for linkage was observed in two single bins, on chromosomes 5q (142-168 Mb) and 2q (103-134 Mb). Genome-wide evidence for linkage was detected on chromosome 2q (119-152 Mb) when bin boundaries were shifted to the middle of the previous bins. The primary analysis met empirical criteria for 'aggregate' genome-wide significance, indicating that some or all of 10 bins are likely to contain loci linked to SCZ, including regions of chromosomes 1, 2q, 3q, 4q, 5q, 8p and 10q. In a secondary analysis of 22 studies of European-ancestry samples, suggestive evidence for linkage was observed on chromosome 8p (16-33 Mb). Although the newer genome-wide association methodology has greater power to detect weak associations to single common DNA sequence variants, linkage analysis can detect diverse genetic effects that segregate in families, including multiple rare variants within one locus or several weakly associated loci in the same region. Therefore, the regions supported by this meta-analysis deserve close attention in future studies.

Analysis of RUNX1 binding site and RAPTOR polymorphisms in psoriasis: no evidence for association despite adequate power and evidence for linkage.

BACKGROUND: A previous study identified two peaks of allelic association between psoriasis and single nucleotide polymorphisms (SNPs) mapping to distal chromosome 17q, including a disease associated SNP that leads to loss of a RUNX1 transcription factor binding site, and additional SNPs in the third intron of the RAPTOR gene. Another study found an association with SNPs in the RAPTOR gene, but not with the RUNX1 binding site polymorphism. METHODS: In an effort to confirm these observations, we genotyped 579 pedigrees containing 1285 affected individuals for three SNPs immediately flanking and including the RUNX1 binding site, and for three SNPs in the RAPTOR gene. RESULTS: Here we report further evidence for linkage to distal chromosome 17q, with a linkage peak mapping 1.7 cM distal to the RUNX1 binding site (logarithm of the odds 2.26 to 2.73, depending upon statistic used). However, we found no evidence for association to individual SNPs or haplotypes in either of the previously identified peaks of association. Power analysis demonstrated 80% power to detect significant association at genotype relative risks of 1.2 (additive and multiplicative models) to 1.5 (dominant and recessive models) for the RUNX1 binding site, and 1.3 to 1.4 for the RAPTOR locus under all models except dominant. CONCLUSIONS: Our data provide no support for the previously identified RUNX1 binding site or for the RAPTOR locus as genetic determinants of psoriasis, despite evidence for linkage of psoriasis to distal chromosome 17q.

Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots.

Recent studies of human populations suggest that the genome consists of chromosome segments that are ancestrally conserved ('haplotype blocks'; refs. 1-3) and have discrete boundaries defined by recombination hot spots. Using publicly available genetic markers, we have constructed a first-generation haplotype map of chromosome 19. As expected for this marker density, approximately one-third of the chromosome is encompassed within haplotype blocks. Evolutionary modeling of the data indicates that recombination hot spots are not required to explain most of the observed blocks, providing that marker ascertainment and the observed marker spacing are considered. In contrast, several long blocks are inconsistent with our evolutionary models, and different mechanisms could explain their origins.

Mapping quantitative effects of oligogenes by allelic association.

Regression analysis of a quantitative trait as a function of a single diallelic polymorphism has been extended to allelic association by composite likelihood under the Malecot model for multiple markers. We applied the method to 10 single nucleotide polymorphisms (SNPs) spanning 27 kb of the angiotensin-I converting enzyme (ACE) gene in British families, localising a causal SNP between G2530A and 4656(CT)3/2 in the 3' region, at a distance of 21.6+/-0.9 kb from the most proximal SNP T-5491C. Neither they nor the I/D polymorphism is causal. To clarify genetic parameters we applied combined segregation, linkage and association analysis. Stronger evidence for the 3' region was obtained, with significant evidence of a lesser 5' effect as reported in French and Nigerian families. However, rigorous confirmation requires that the causal SNPs be identified. Both Malecot and parametric analysis appear to have high power by comparison with alternative methods for localizing oligogenes and their causal polymorphisms.

Gene polymorphism in Netherton and common atopic disease.

Atopic dermatitis (AD) and asthma are characterized by IgE-mediated atopic (allergic) responses to common proteins (allergens), many of which are proteinases. Loci influencing atopy have been localized to a number of chromosomal regions, including the chromosome 5q31 cytokine cluster. Netherton disease is a rare recessive skin disorder in which atopy is a universal accompaniment. The gene underlying Netherton disease (SPINK5) encodes a 15-domain serine proteinase inhibitor (LEKTI) which is expressed in epithelial and mucosal surfaces and in the thymus. We have identified six coding polymorphisms in SPINK5 (Table 1) and found that a Glu420-->Lys variant shows significant association with atopy and AD in two independent panels of families. Our results implicate a previously unrecognized pathway for the development of common allergic illnesses.

GRR: graphical representation of relationship errors.

SUMMARY: A graphical tool for verifying assumed relationships between individuals in genetic studies is described. GRR can detect many common errors using genotypes from many markers. AVAILABILITY: GRR is available at http://bioinformatics.well.ox.ac.uk/GRR.

The power to detect linkage disequilibrium with quantitative traits in selected samples.

Results from power studies for linkage detection have led to many ongoing and planned collections of phenotypically extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However, optimal selection strategies for linkage may not be equally powerful for association. We examine the power to detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele frequencies within each selection design indicates that common trait alleles generally offer the most power, but similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and the extreme-proband design provide power for the broadest range of QTL-marker-allele frequency differences. Overall, proband selection from either tail provides the best balance of power, robustness, and simplicity of ascertainment for family-based association analysis.

Trans-ethnic fine mapping of a quantitative trait locus for circulating angiotensin I-converting enzyme (ACE).

Circulating angiotensin I-converting enzyme (ACE) levels are influenced by a major quantitative trait locus (QTL) that maps to the ACE gene. Phylogenetic and measured haplotype analyses have suggested that the ACE-linked QTL lies downstream of a putative ancestral breakpoint located near to position 6435. However, strong linkage disequilibrium between markers in the 3' portion of the gene has prevented further resolution of the QTL in Caucasian subjects. We have examined 10 ACE gene polymorphisms in Afro-Caribbean families recruited in JAMAICA: Variance components analyses showed strong evidence of linkage and association to circulating ACE levels. When the linkage results were contrasted with those from a set of British Caucasian families, there was no evidence for heterogeneity between the samples. However, patterns of allelic association between the markers and circulating ACE levels differed significantly in the two data sets. In the British families, three markers [G2215A, Alu insertion/deletion and G2350A] were in complete disequilibrium with the ACE-linked QTL. In the Jamaican families, only marker G2350A showed strong but incomplete disequilibrium with the ACE-linked QTL. These results suggest that additional unobserved polymorphisms have an effect on circulating ACE levels in Jamaican families. Furthermore, our results show that a variance components approach combined with structured, quantitative comparisons between families from different ethnic groups may be a useful strategy for helping to determine which, if any, variants in a small genomic region directly influence a quantitative trait.

Association between quantitative traits underlying asthma and the HLA-DRB1 locus in a family-based population sample.

The region of human chromosome 6 containing the MHC has been identified as influencing asthma and atopy (allergy) by several genome-wide searches. The MHC contains many genes with potential effects on innate and specific immunity. As a first step in dissecting MHC influences on asthma and its underlying quantitative phenotypes, we have examined the HLA-DRB1 locus in a population sample consisting of 1004 individuals from 230 families from the rural Australian town of Busselton. The locus was strongly associated with the (log(e)) total serum IgE concentration, accounting for 4.0% of the sigma(2) (variance) in that trait (multi-allelic test, P=0.00001). The locus also influenced specific IgE titres to common allergens (multi-allelic tests, 2.8% sigma(2) for the house dust mite allergen Der p I, P=0.0013; 3.0% of sigma(2) for Der p II, P=0.0007; and 2.1% of sigma(2) for the cat allergen Fel d I, P=0.014). No associations were found to the categorical phenotype of asthma, or to the quantitative traits of peripheral blood eosinophil counts and bronchial hyper-responsiveness. Transmission disequilibrium tests excluded genetic admixture as a cause of false-positive findings. The results indicate that HLA-DRB1 alleles modulate the total serum IgE concentration and IgE responses to allergens, but do not account for the previous observations of linkage of asthma to the MHC.

The impact of genotyping error on family-based analysis of quantitative traits.

Errors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.

Genetic linkage of childhood atopic dermatitis to psoriasis susceptibility loci.

We have carried out a genome screen for atopic dermatitis (AD) and have identified linkage to AD on chromosomes 1q21, 17q25 and 20p. These regions correspond closely with known psoriasis loci, as does a previously identified AD locus on chromosome 3q21. The results indicate that AD is influenced by genes with general effects on dermal inflammation and immunity.

Extent and distribution of linkage disequilibrium in three genomic regions.

The positional cloning of genes underlying common complex diseases relies on the identification of linkage disequilibrium (LD) between genetic markers and disease. We have examined 127 polymorphisms in three genomic regions in a sample of 575 chromosomes from unrelated individuals of British ancestry. To establish phase, 800 individuals were genotyped in 160 families. The fine structure of LD was found to be highly irregular. Forty-five percent of the variation in disequilibrium measures could be explained by physical distance. Additional factors, such as allele frequency, type of polymorphism, and genomic location, explained <5% of the variation. Nevertheless, disequilibrium was occasionally detectable at 500 kb and was present for over one-half of marker pairs separated by <50 kb. Although these findings are encouraging for the prospects of a genomewide LD map, they suggest caution in interpreting localization due to allelic association.

Oxford genome screen for asthma-associated traits.

A genome screen for linkage of quantitative traits underlying asthma has been carried out previously by our group on 80 families sub-selected for discordant phenotypes from a general population sample. The families contained a total of 203 offspring forming 172 sib-pairs. Genotypic data for at a total of 296 markers were available. This paper describes the ascertainment, phenotypic data, and genotypic data made available for Genetic Analysis Workshop 12.

The effect of genotype and pedigree error on linkage analysis: analysis of three asthma genome scans.

The effects of genotype and relationship errors on linkage results are evaluated in three of the Genetic Analysis Workshop 12 asthma genome scans. A number of errors are detected in the samples. While the evidence for linkage is not striking in any data set with or without error, in some cases the difference in test statistic could support different conclusions. The results provide empirical evidence for the predicted effects of genotype and relationship error and highlight the need for rigorous detection and elimination of data error in complex trait studies.

Association analysis in a variance components framework.

Association analyses conducted in a variance components framework can include information from all available individuals but remain unbiased in the presence of familiality or linkage. Models that include both linkage and association parameters provide different estimates of the effect of a single locus and can be used to distinguish causal polymorphisms from other types of variation. We examine some of these models and their properties in a blind analysis of the simulated Genetic Analysis Workshop 12 data sets.

Some properties of a variance components model for fine-mapping quantitative trait loci.

Identifying etiological variants for multifactorial traits by allelic association holds promise when many markers are available in close proximity. However, evidence for or against association at any particular marker does not provide any direct information about the influence of causal variants or the frequency of the etiologic allele(s). Recently, a variance components model of linkage and association was developed for quantitative traits which is sufficiently flexible to provide some insights into these issues. We show that this combined linkage/association model provides an estimate of the additive genetic variance of a trait that is attributable to disequilibrium between the marker and QTL. We use this estimate to construct approximate boundaries of the minimum level of disequilibrium between an observed marker and unobserved QTL and to delimit the permissible range of allele frequencies at the QTL based on available data at nearby markers. This information may facilitate fine-mapping studies of complex traits that aim to localize QTLs by assessment of association with many markers in a candidate region of interest.

Pedigree tests of transmission disequilibrium.

High-resolution mapping is essential for the positional cloning of complex disease genes. In outbred populations, linkage disequilibrium is expected to extend for short distances and could provide a powerful fine-mapping tool. Current family-based association tests use nuclear family members to define allelic transmission and controls, but ignore other types of relatives. Here we construct a general approach for scoring allelic transmission that accommodates families of any size and uses all available genotypic information. Family data allows for the construction of an expected genotype for every non-founder, and orthogonal deviates from this expectation are a measure of allelic transmission. These allelic transmission scores can be used to extend previously described tests of linkage disequilibrium for dichotomous or quantitative traits. Some of these tests are illustrated, together with a permutation framework for estimating exact significance levels. Simulation studies are used to investigate power and error rates of the approach. As a practical application, the method is used to investigate the relationship between circulating angiotensin-1 converting enzyme (ACE) levels and polymorphisms in the ACE gene using previously published data.