Linkage analysis with dense SNP maps in isolated populations

Determinants of Human Asymmetry: Does Asymmetrical Retinal Vasculature Predict Asymmetry Elsewhere in the Body?

The aim of this study was to explore retinal vasculature asymmetry (ReVA) patterns in subjects from the islands of Vis and Korcula and the city of Split, Croatia. Asymmetry estimates were based on topographic image analysis of non-mydriatic retinal fundus photographs and compared with nine ophthalmic measurements, three Doppler-based pressure indices and eight frequencies of audiometry. ReVA was also correlated to the genomic runs of homozygosity (ROHs) and used in a Cox regression survival model, where we adjusted for the effects of sex, age and comorbidity. In 1873 subjects, ReVA estimates were significantly correlated with most ophthalmic asymmetry measures, less strongly with the ankle-brachial pressure index and only modestly with higher-amplitude audiometry asymmetries (lowest p = 0.020). ReVA was significantly correlated with the number of ROHs (r = 0.229, p < 0.001) but less strongly with the ROH length (r = 0.101, p < 0.001). The overlap of asymmetries was low, with only 107 subjects (5.7% of the total sample) who had two or more instances in which they were among the top 10%. Multiple asymmetries did not affect survival (HR = 0.74, 95% confidence intervals 0.45-1.22). Retinal vasculature asymmetry is a poor predictor of asymmetry elsewhere in the body. Despite its existence and apparent association with comorbidities, the observed extent of retinal vasculature asymmetry did not affect the lifespan in this population.

PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers

MOTIVATION

Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility.

RESULTS

We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets.

AVAILABILITY AND IMPLEMENTATION

PBAP scripts may be downloaded from http://faculty.washington.edu/wijsman/software.shtml.

CONTACT

wijsman@uw.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Can whole-exome sequencing data be used for linkage analysis?

Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage of the genome and the lack of knowledge on the power of this strategy have largely precluded its use so far. To compare the performance of linkage analysis conducted with WES and SNP chip data in different situations, we performed simulations on two pedigree structures with, respectively, a dominant and a recessive trait segregating. We found that the performance of the two sets of markers at excluding regions of the genome were very similar, and there was no real gain at using SNP chip data compared with using the common SNPs extracted from WES data. When analyzing the real WES data available for these two pedigrees, we found that the linkage information derived from the WES common polymorphisms was able to reduce by half the list of candidate variants identified by a simple filtering approach. Conducting linkage analysis with WES data available on pedigrees and excluding among the candidate variants those that fall in excluded linkage regions is thus a powerful and cost-effective strategy to reduce the number of false-positive candidate variants.

A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees

MOTIVATION

The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes.

RESULTS

Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome.

AVAILABILITY

Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website.

CONTACT

omerw@cs.technion.ac.il

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Obtaining accurate p values from a dense SNP linkage scan

A major concern of resequencing studies is that the pathogenicity of most mutations is difficult to predict. To address this concern, linkage (i.e. co-segregation) analysis is often used to exclude neutral mutations and to better predict pathogenicity among the candidate mutations that remain. However, when linkage disequilibrium (LD) is present in the population but ignored in the analysis, unlinked regions with high LD can inflate the type 1 error and thousands of neutral mutations may be mistakenly included in a follow-up resequencing study, which could dramatically reduce the power to identify causal variants. To illustrate the need for concern, we simulated data on a sparsely spaced panel of single nucleotide polymorphisms (average spacing 1.27 cM) using an LD pattern estimated from real data. In our simulations, we find that the type 1 error of the maximum LOD can be as high as 14%. Therefore, to control the type 1 error of linkage tests we created Haplodrop - a fast and flexible simulation program that generates the haplotypes of founders with LD and then 'drops' these haplotypes with recombination to all non-founders in the pedigree. Haplodrop can be used to control the type 1 error of any linkage test, agrees well with existing software, accommodates arbitrary pedigree structures, and scales easily to the whole genome. Moreover, by correctly excluding mutations that lie in unlinked regions with high LD, Haplodrop should aid significantly in reducing the multiple testing burden of follow-up resequencing studies.

Small effective population size and genetic homogeneity in the Val Borbera isolate

Population isolates are a valuable resource for medical genetics because of their reduced genetic, phenotypic and environmental heterogeneity. Further, extended linkage disequilibrium (LD) allows accurate haplotyping and imputation. In this study, we use nuclear and mitochondrial DNA data to determine to what extent the geographically isolated population of the Val Borbera valley also presents features of genetic isolation. We performed a comparative analysis of population structure and estimated effective population size exploiting LD data. We also evaluated haplotype sharing through the analysis of segments of autozygosity. Our findings reveal that the valley has features characteristic of a genetic isolate, including reduced genetic heterogeneity and reduced effective population size. We show that this population has been subject to prolonged genetic drift and thus we expect many variants that are rare in the general population to reach significant frequency values in the valley, making this population suitable for the identification of rare variants underlying complex traits.

The combination of a genome-wide association study of lymphocyte count and analysis of gene expression data reveals novel asthma candidate genes

Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the ‘missing heritability problem’. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.

Detection of novel SNPs and mapping of the fatness QTL on pig chromosome 7q1.1-1.4 region

Many QTLs for fatness traits have been mapped on pig chromosome 7q1.1-1.4 in various pig resource populations. Eight novel markers, including seven SNPs and one insertion or deletion within BTNL1, COL21A1, PPARD, GLP1R, MDFI, GNMT, ABCC10, and PLA2G7 genes, as well as two previously reported SNPs in SLC39A7 and HMGA1 genes, were genotyped in Large White and Meishan pig breeds. Except for two SNPs in HMGA1 and ABCC10 genes, allele frequencies of the other eight markers are highly significant different between Chinese indigenous Meishan breeds and Large White pig breeds. Eight polymorphic sites were then used for linkage and QTL mapping to refine the fatness QTL in a Large White × Meishan F(2) resource population. Five chromosome-wise significant QTLs were detected, of which the QTLs for leaf fat weight, backfat thickness at 6-7th rib and rump, and mean backfat thickness were narrowed to the interval between PPARD and GLP1R genes and the QTL for backfat thickness at thorax-waist between GNMT and PLA2G7 genes on SSC7p1.1-q1.4.

Identity by descent estimation with dense genome-wide genotype data

We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of accommodating the background linkage disequilibrium (LD) that is present in high-density genotype data. We show that IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate the probability for each possible IBD sharing state at every SNP from a high-density genotyping array for hundreds of thousands of pairs of individuals. We use it to estimate point-wise and genomewide IBD sharing between 185,745 pairs of subjects all of whom are related through a single, large and complex 13-generation pedigree and genotyped with the Affymetrix 500 k chip. We find that we are able to identify the true pedigree relationship for individuals who were misidentified in the collected data and estimate empirical kinship coefficients that can be used in follow-up QTL mapping studies. IBDLD is implemented as an open source software package and is freely available.

Finding disease genes: a fast and flexible approach for analyzing high-throughput data

Linkage disequilibrium (LD) is the non-random distribution of alleles across the genome, and it can create serious problems for modern linkage studies. In particular, computational feasibility is often obtained at the expense of power, precision, and/or accuracy. In our new approach, we combine linkage results over multiple marker subsets to provide fast, efficient, and robust analyses, without compromising power, precision, or accuracy. Allele frequencies and LD in the densely spaced markers are used to construct subsamples that are highly informative for linkage. We have tested our approach extensively, and implemented it in the software package EAGLET (Efficient Analysis of Genetic Linkage: Estimation and Testing). Relative to several commonly used methods we show that EAGLET has increased power to detect disease genes across a range of trait models, LD patterns, and family structures using both simulated and real data. In particular, when the underlying LD pattern is derived from real data, we find that EAGLET outperforms several commonly used linkage methods. In-depth analysis of family data, simulated with linkage and under the real-data derived LD pattern, showed that EAGLET had 78.1% power to detect a dominant disease with incomplete penetrance, whereas the method that uses one marker per cM had 69.7% power, and the cluster-based approach implemented in MERLIN had 76.7% power. In this same setting, EAGLET was three times faster than MERLIN, and it narrowed the MERLIN-based confidence interval for trait location by 29%. Overall, EAGLET gives researchers a fast, accurate, and powerful new tool for analyzing high-throughput linkage data, and large extended families are easily accommodated.

Does inbreeding affect N-glycosylation of human plasma proteins?

Inbreeding depression and heterosis are the two ends of phenotypic changes defined by the genome-wide homozygosity. The aim of this study was to investigate the association of genetic marker-based homozygosity estimates with 46 N-glycan features measured in human plasma. The study was based on a total of 2,341 subjects, originating from three isolated island communities in Croatia (Vis and Korcula islands) and Scotland (Orkney Islands). Inbreeding estimates were associated with an increase in tetrantennary and tetrasialylated glycans, and a decrease in digalactosylated glycans (P < 0.001). The strength of this association was proportional to the mean cohort-based inbreeding coefficient. Increase in tetraantennary glycans is known to be associated with various tumours and their association with inbreeding might be one of the mechanisms underlying the increased prevalence of tumours reported in some human isolated populations. Further studies are thus merited in order to confirm the association of inbreeding with changes in glycan profiles in other plant and animal populations, thus attempting to establish if glycosylation could indeed be involved in mediation of some phenotypic changes described in inbred and outbred organisms.

Consanguinity around the world: what do the genomic data of the HGDP-CEPH diversity panel tell us?

Inbreeding coefficients and consanguineous mating types are usually inferred from population surveys or pedigree studies. Here, we present a method to estimate them from dense genome-wide single-nucleotide polymorphism genotypes and apply it to 940 unrelated individuals from the Human Genome Diversity Panel (HGDP-CEPH). Inbreeding is observed in almost all populations of the panel, and the highest inbreeding levels and frequencies of inbred individuals are found in populations of the Middle East, Central South Asia and the Americas. In these regions, first cousin (1C) marriages are the most frequent, but we also observed marriages between double first cousins (2 × 1C) and between avuncular (AV) pairs. Interestingly, if 2 × 1C marriages are preferred to AV marriages in Central South Asia and the Middle East, the contrary is found in the Americas. There are thus some regional trends but there are also some important differences between populations within a region. Individual results can be found on the CEPH website at ftp://ftp.cephb.fr/hgdp_hbd/.

Genome wide linkage study, using a 250K SNP map, of Plasmodium falciparum infection and mild malaria attack in a Senegalese population

Multiple factors are involved in the variability of host's response to P. falciparum infection, like the intensity and seasonality of malaria transmission, the virulence of parasite and host characteristics like age or genetic make-up. Although admitted nowadays, the involvement of host genetic factors remains unclear. Discordant results exist, even concerning the best-known malaria resistance genes that determine the structure or function of red blood cells. Here we report on a genome-wide linkage and association study for P. falciparum infection intensity and mild malaria attack among a Senegalese population of children and young adults from 2 to 18 years old. A high density single nucleotide polymorphisms (SNP) genome scan (Affimetrix GeneChip Human Mapping 250K-nsp) was performed for 626 individuals: i.e. 249 parents and 377 children out of the 504 ones included in the follow-up. The population belongs to a unique ethnic group and was closely followed-up during 3 years. Genome-wide linkage analyses were performed on four clinical and parasitological phenotypes and association analyses using the family based association tests (FBAT) method were carried out in regions previously linked to malaria phenotypes in literature and in the regions for which we identified a linkage peak. Analyses revealed three strongly suggestive evidences for linkage: between mild malaria attack and both the 6p25.1 and the 12q22 regions (empirical p-value = 5×10⁻⁵ and 9×10⁻⁵ respectively), and between the 20p11q11 region and the prevalence of parasite density in asymptomatic children (empirical p-value = 1.5×10⁻⁴). Family based association analysis pointed out one significant association between the intensity of plasmodial infection and a polymorphism located in ARHGAP26 gene in the 5q31–q33 region (p-value = 3.7×10⁻⁵). This study identified three candidate regions, two of them containing genes that could point out new pathways implicated in the response to malaria infection. Furthermore, we detected one gene associated with malaria infection in the 5q31–q33 region.

Comparative assessment of methods for estimating individual genome-wide homozygosity-by-descent from human genomic data

Background

Genome-wide homozygosity estimation from genomic data is becoming an increasingly interesting research topic. The aim of this study was to compare different methods for estimating individual homozygosity-by-descent based on the information from human genome-wide scans rather than genealogies. We considered the four most commonly used methods and investigated their applicability to single-nucleotide polymorphism (SNP) data in both a simulation study and by using the human genotyped data. A total of 986 inhabitants from the isolated Island of Vis, Croatia (where inbreeding is present, but no pedigree-based inbreeding was observed at the level of F > 0.0625) were included in this study. All individuals were genotyped with the Illumina HumanHap300 array with 317,503 SNP markers.

Results

Simulation data suggested that multi-point FEstim is the method most strongly correlated to true homozygosity-by-descent. Correlation coefficients between the homozygosity-by-descent estimates were high but only for inbred individuals, with nearly absolute correlation between single-point measures.

Conclusions

Deciding who is really inbred is a methodological challenge where multi-point approaches can be very helpful once the set of SNP markers is filtered to remove linkage disequilibrium. The use of several different methodological approaches and hence different homozygosity measures can help to distinguish between homozygosity-by-state and homozygosity-by-descent in studies investigating the effects of genomic autozygosity on human health.