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Gonçalo R Abecasis

Publications and source records attributed to Gonçalo R Abecasis.

At least 19 recordsLinked to original sources
PubMed2006

CFH haplotypes without the Y402H coding variant show strong association with susceptibility to age-related macular degeneration.

In developed countries, age-related macular degeneration is a common cause of blindness in the elderly. A common polymorphism, encoding the sequence variation Y402H in complement factor H (CFH), has been strongly associated with disease susceptibility. Here, we examined 84 polymorphisms in and around CFH in 726 affected individuals (including 544 unrelated individuals) and 268 unrelated controls. In this sample, 20 of these polymorphisms showed stronger association with disease susceptibility than the Y402H variant. Further, no single polymorphism could account for the contribution of the CFH locus to disease susceptibility. Instead, multiple polymorphisms defined a set of four common haplotypes (of which two were associated with disease susceptibility and two seemed to be protective) and multiple rare haplotypes (associated with increased susceptibility in aggregate). Our results suggest that there are multiple disease susceptibility alleles in the region and that noncoding CFH variants play a role in disease susceptibility.

Aged↗
PubMed2006

In silico method for inferring genotypes in pedigrees.

Our genotype inference method combines sparse marker data from a linkage scan and high-resolution SNP genotypes for several individuals to infer genotypes for related individuals. We illustrate the method's utility by inferring over 53 million SNP genotypes for 78 children in the Centre d'Etude du Polymorphisme Humain families. The method can be used to obtain high-density genotypes in different family structures, including nuclear families commonly used in complex disease gene mapping studies.

Chi-Square Distribution↗
PubMed2006

Heritability of cardiovascular and personality traits in 6,148 Sardinians.

In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14-102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.

Adolescent↗
PubMed2006

Quantitative trait linkage analysis using Gaussian copulas.

Mapping and identifying variants that influence quantitative traits is an important problem for genetic studies. Traditional QTL mapping relies on a variance-components (VC) approach with the key assumption that the trait values in a family follow a multivariate normal distribution. Violation of this assumption can lead to inflated type I error, reduced power, and biased parameter estimates. To accommodate nonnormally distributed data, we developed and implemented a modified VC method, which we call the "copula VC method," that directly models the nonnormal distribution using Gaussian copulas. The copula VC method allows the analysis of continuous, discrete, and censored trait data, and the standard VC method is a special case when the data are distributed as multivariate normal. Through the use of link functions, the copula VC method can easily incorporate covariates. We use computer simulations to show that the proposed method yields unbiased parameter estimates, correct type I error rates, and improved power for testing linkage with a variety of nonnormal traits as compared with the standard VC and the regression-based methods.

Analysis of Variance↗
PubMed2006

Sequence and haplotype analysis supports HLA-C as the psoriasis susceptibility 1 gene.

Previous studies have narrowed the interval containing PSORS1, the psoriasis-susceptibility locus in the major histocompatibility complex (MHC), to an approximately 300-kb region containing HLA-C and at least 10 other genes. In an effort to identify the PSORS1 gene, we cloned and completely sequenced this region from both chromosomes of five individuals. Two of the sequenced haplotypes were associated with psoriasis (risk), and the other eight were clearly unassociated (nonrisk). Comparison of sequence of the two risk haplotypes identified a 298-kb region of homology, extending from just telomeric of HLA-B to the HCG22 gene, which was flanked by clearly nonhomologous regions. Similar haplotypes cloned from unrelated individuals had nearly identical sequence. Combinatorial analysis of exonic variations in the known genes of the candidate interval revealed that HCG27, PSORS1C3, OTF3, TCF19, HCR, STG, and HCG22 bore no alleles unique to risk haplotypes among the 10 sequenced haplotypes. SPR1 and SEEK1 both had messenger RNA alleles specific to risk haplotypes, but only HLA-C and CDSN yielded protein alleles unique to risk. The risk alleles of HLA-C and CDSN (HLA-Cw6 and CDSN*TTC) were genotyped in 678 families with early-onset psoriasis; 620 of these families were also typed for 34 microsatellite markers spanning the PSORS1 interval. Recombinant haplotypes retaining HLA-Cw6 but lacking CDSN*TTC were significantly associated with psoriasis, whereas recombinants retaining CDSN*TTC but lacking HLA-Cw6 were not associated, despite good statistical power. By grouping recombinants with similar breakpoints, the most telomeric quarter of the 298-kb candidate interval could be excluded with high confidence. These results strongly suggest that HLA-Cw6 is the PSORS1 risk allele that confers susceptibility to early-onset psoriasis.

Adult↗
PubMed2006

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls.

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.

Case-Control Studies↗
PubMed2006

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).

Alleles↗
PubMed2006

An evaluation of the replicate pool method: quick estimation of genome-wide linkage peak p-values.

The calculation of empirical p-values for genome-wide non-parametric linkage tests continues to present significant computational challenges for many complex disease mapping studies. The gold standard approach is to use gene dropping to simulate null genome scans. Unfortunately, this approach is too computationally expensive for many data sets of interest. An alternative, more efficient method for sampling null genome scans is to pre-calculate pools of family-specific statistics and then resample from these replicate pools to generate "pseudo-replicate" genome scans. In this study, we use simulations to explore properties of the replicate pool p-value estimator pRP and show that it provides an excellent approximation to the traditional gene-dropping estimator for significantly less computational effort. While the computational efficiency of the replicate pool estimator is noticeable in almost all data sets, by applying the replicate pool method to several previously characterized data sets we show that savings in computational effort can be especially significant (on the order of 10,000-fold or more) when one or more large families are analyzed. We also estimate replicate pool p-values for the schizophrenia data described by Abecasis et al. and show that pRP closely approximates gene-drop p-values for all linkage peaks reported for this study. Lastly, we expand upon Song et al.'s previous work by deriving a conservative estimator of the variance for PRP that can easily be computed in practical settings. We have implemented the replicate pool method along with our variance estimator in a new program called Pseudo, which is the first widely available automated implementation of the replicate pool method.

Analysis of Variance↗
PubMed2006

Using sex-averaged genetic maps in multipoint linkage analysis when identity-by-descent status is incompletely known.

The ratio of male and female genetic map distances varies dramatically across the human genome. Despite these sex differences in genetic map distances, most multipoint linkage analyses use sex-averaged genetic maps. We investigated the impact of using a sex-averaged genetic map instead of sex-specific maps for multipoint linkage analysis of affected sibling pairs when identity-by-descent states are incompletely known due to missing parental genotypes and incomplete marker heterozygosity. If either all or no parental genotypes were available, for intermarker distances of 10, 5, and 1 cM, we found no important differences in the expected maximum lod score (EMLOD) or location estimates of the disease locus between analyses that used the sex-averaged map and those that used the true sex-specific maps for female:male genetic map distance ratios 1:10 and 10:1. However, when genotypes for only one parent were available and the recombination rate was higher in females, the EMLOD using the sex-averaged map was inflated compared to the sex-specific map analysis if only mothers were genotyped and deflated if only fathers were genotyped. The inflation of the lod score when only mothers were genotyped led to markedly increased false-positive rates in some cases. The opposite was true when the recombination rate was higher in males; the EMLOD was inflated if only fathers were genotyped, and deflated if only mothers were genotyped. While the effects of missing parental genotypes were mitigated for less extreme cases of missingness, our results suggest that when possible, sex-specific maps should be used in linkage analyses.

Chromosome Mapping↗
PubMed2006

Estimating the power of variance component linkage analysis in large pedigrees.

Variance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2-5 generations and 2-8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 (1.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with >1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY.

Chromosome Mapping↗
PubMed2005

An efficient comprehensive search algorithm for tagSNP selection using linkage disequilibrium criteria.

MOTIVATION: Selecting SNP markers for genome-wide association studies is an important and challenging task. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously maximizing the information content provided by selected markers. RESULTS: We devised an improved algorithm for tagSNP selection using the pairwise r(2) criterion. We first break down large marker sets into disjoint pieces, where more exhaustive searches can replace the greedy algorithm for tagSNP selection. These exhaustive searches lead to smaller tagSNP sets being generated. In addition, our method evaluates multiple solutions that are equivalent according to the linkage disequilibrium criteria to accommodate additional constraints. Its performance was assessed using HapMap data. AVAILABILITY: A computer program named FESTA has been developed based on this algorithm. The program is freely available and can be downloaded at http://www.sph.umich.edu/csg/qin/FESTA/

Algorithms↗
PubMed2005

Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers.

Single-nucleotide polymorphisms (SNPs) are rapidly replacing microsatellites as the markers of choice for genetic linkage studies and many other studies of human pedigrees. Here, we describe an efficient approach for modeling linkage disequilibrium (LD) between markers during multipoint analysis of human pedigrees. Using a gene-counting algorithm suitable for pedigree data, our approach enables rapid estimation of allele and haplotype frequencies within clusters of tightly linked markers. In addition, with the use of a hidden Markov model, our approach allows for multipoint pedigree analysis with large numbers of SNP markers organized into clusters of markers in LD. Simulation results show that our approach resolves previously described biases in multipoint linkage analysis with SNPs that are in LD. An updated version of the freely available Merlin software package uses the approach described here to perform many common pedigree analyses, including haplotyping and haplotype frequency estimation, parametric and nonparametric multipoint linkage analysis of discrete traits, variance-components and regression-based analysis of quantitative traits, calculation of identity-by-descent or kinship coefficients, and case selection for follow-up association studies. To illustrate the possibilities, we examine a data set that provides evidence of linkage of psoriasis to chromosome 17.

Algorithms↗
PubMed2005

Strong association of the Y402H variant in complement factor H at 1q32 with susceptibility to age-related macular degeneration.

Using a large sample of cases and controls from a single center, we show that a T-->C substitution in exon 9 (Y402H) of the complement factor H gene is strongly associated with susceptibility to age-related macular degeneration, the most common cause of blindness in the elderly. Frequency of the C allele was 0.61 in cases, versus 0.34 in age-matched controls (P<1x10(-24)). Genotype frequencies also differ markedly between cases and controls (chi2=112.68 [2 degrees of freedom]; P<1x10(-24)). A multiplicative model fits the data well, and we estimate the population frequency of the high-risk C allele to be 0.39 (95% confidence interval 0.36-0.42) and the genotype relative risk to be 2.44 (95% confidence interval 2.08-2.83) for TC heterozygotes and 5.93 (95% confidence interval 4.33-8.02) for CC homozygotes.

Aged↗
PubMed2005

Linkage disequilibrium: ancient history drives the new genetics.

This brief review provides a summary of the biological causes of genetic association between tightly linked markers--termed linkage disequilibrium--and unlinked markers--termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.

Chromosome Mapping↗
PubMed2005

Toll-like receptor 4 variant D299G is associated with susceptibility to age-related macular degeneration.

Age-related macular degeneration (AMD) is a genetically heterogeneous disease that leads to progressive and irreversible vision loss among the elderly. Inflammation, oxidative damage, cholesterol metabolism and/or impaired function of retinal pigment epithelium (RPE) have been implicated in AMD pathogenesis. We examined toll-like receptor 4 (TLR4) as a candidate gene for AMD susceptibility because: (i) the TLR4 gene is located on chromosome 9q32-33, a region exhibiting evidence of linkage to AMD in three independent reports; (ii) the TLR4-D299G variant is associated with reduced risk of atherosclerosis, a chronic inflammatory disease with subendothelial accumulation; (iii) the TLR4 is not only a key mediator of proinflammatory signaling pathways but also linked to regulation of cholesterol efflux and (iv) the TLR4 participates in phagocytosis of photoreceptor outer segments by the RPE. We examined D299G and T399I variants of TLR4 in a sample of 667 unrelated AMD patients and 439 unrelated controls, all of Caucasian ancestry. Multiple logistic regression demonstrated an increased risk of AMD in carriers of the G allele at TLR4 residue 299 (odds ratio=2.65, P=0.025), but lack of an independent effect by T399I variant. TLR4-D299G showed an additive effect on AMD risk (odds ratio=4.13, P=0.002) with allelic variants of apolipoprotein E (APOE) and ATP-binding cassette transporter-1 (ABCA1), two genes involved in cholesterol efflux. Interestingly, the effect of TLR4, APOE and ABCA1 variants on AMD susceptibility was opposite to that of association with atherosclerosis risk. Our data provide evidence of a link between multiple diverse mechanisms underlying AMD pathogenesis.

Aged↗
PubMed2005

Sequence features in regions of weak and strong linkage disequilibrium.

We use genotype data generated by the International HapMap Project to dissect the relationship between sequence features and the degree of linkage disequilibrium in the genome. We show that variation in linkage disequilibrium is broadly similar across populations and examine sequence landscape in regions of strong and weak disequilibrium. Linkage disequilibrium is generally low within approximately 15 Mb of the telomeres of each chromosome and noticeably elevated in large, duplicated regions of the genome as well as within approximately 5 Mb of centromeres and other heterochromatic regions. At a broad scale (100-1000 kb resolution), our results show that regions of strong linkage disequilibrium are typically GC poor and have reduced polymorphism. In addition, these regions are enriched for LINE repeats, but have fewer SINE, DNA, and simple repeats than the rest of the genome. At a fine scale, we examine the sequence composition of "hotspots" for the rapid breakdown of linkage disequilibrium and show that they are enriched in SINEs, in simple repeats, and in sequences that are conserved between species. Regions of high and low linkage disequilibrium (the top and bottom quartiles of the genome) have a higher density of genes and coding bases than the rest of the genome. Closer examination of the data shows that whereas some types of genes (including genes involved in immune response and sensory perception) are typically located in regions of low linkage disequilibrium, other genes (including those involved in DNA and RNA metabolism, response to DNA damage, and the cell cycle) are preferentially located in regions of strong linkage disequilibrium. Our results provide a detailed analysis of the relationship between sequence features and linkage disequilibrium and suggest an evolutionary justification for the heterogeneity in linkage disequilibrium in the genome.

Base Composition↗