Mapping Of Disease Loci Research Articles

Genetic diagnosis of facioscapulohumeral muscular dystrophy type 1 using rare-variant linkage analysis and long-read genome sequencing

Facioscapulohumeral dystrophy type 1 (FSHD1) is a progressive, debilitating skeletal myopathy that requires a multimodal approach for complete molecular characterization of pathogenic genotypes. Here, we report genomic analyses of a family with suspected FSHD1. We first performed short-read genome sequencing, followed by parametric linkage analysis using rare variants to map the disease locus to a single 1.7 Mb interval on chromosome 4q35.2 with a logarithm of the odds score of 3.2. We then used ultra-long-read genome sequencing as a single molecular test to genotype a pathogenic FSHD allele containing a 4qA permissive haplotype and 5 KpnI repeat units at the D4Z4 locus. These results demonstrate that genome-wide rare variant–based linkage analysis is a powerful tool for mapping disease loci in families, and ultra-long-read genome sequencing is capable of genotyping pathogenic FSHD1 alleles.

Phenotypic spectrum and genomics of undiagnosed arthrogryposis multiplex congenita

BackgroundArthrogryposis multiplex congenita (AMC) is characterised by congenital joint contractures in two or more body areas. AMC exhibits wide phenotypic and genetic heterogeneity. Our goals were to improve the genetic...

Refined subchromosomal location of 21 expressed sequence tags from unknown genes at region 11p15.

Twenty-one expressed sequence tags (ESTs) belonging to unidentified transcripts were mapped to 9 intervals at 11p 15.5-p15.3. Thirteen were mapped to a region of an estimated size of 7 Mb with the help of a YAC contig. The remaining eight were mapped outside this region, and the centromeric or telomeric position of the ESTs relative to the YAC contig was defined with the help of a cellular hybrid containing a derivative chromosome 11 with a break-point within the region covered by the contig. The average size of the intervals, where the ESTs were mapped, was estimated to be 0.3-0.4 Mb. The refinement of the chromosomal location of these ESTs could be of great help in the identification of potential candidate genes for disease locus mapping at 11p15.

SPECIAL SECTION: Dosage compensation, epigenetic mechanisms, and mapping disease loci

SPECIAL SECTION: Dosage compensation, epigenetic mechanisms, and mapping disease loci

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type–specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci.

Biallelic mutation of UNC50, encoding a protein involved in AChR trafficking, is responsible for arthrogryposis.

Arthrogryposis multiplex congenita (AMC) is a developmental condition characterized by multiple joint contractures resulting from reduced or absent fetal movements. Homozygosity mapping of disease loci combined with whole exome sequencing in a consanguineous family presenting with lethal AMC allowed the identification of a homozygous frameshift deletion in UNC50 gene (c.750_751del:p.Cys251Phefs*4) in the index case. To assess the effect of the mutation, an equivalent mutation in the Caenorhabditis elegans orthologous gene was created using CRISPR/Cas9. We demonstrated that unc-50(kr331) modification caused the loss of acetylcholine receptor (AChR) expression in C. elegans muscle. unc-50(kr331) animals were as resistant to the cholinergic agonist levamisole as unc-50 null mutants suggesting that AChRs were no longer expressed in this animal model. This was confirmed by using a knock-in strain in which a red fluorescent protein was inserted into the AChR locus: no signal was detected in unc-50(kr331) background, suggesting that UNC-50, a protein known to be involved in AChR trafficking, was no longer functional. These data indicate that biallelic mutation in the UNC50 gene underlies AMC through a probable loss of AChR expression at the neuromuscular junction which is essential for the cholinergic transmission during human muscle development.

Loss-of-Function Mutations in LGI4, a Secreted Ligand Involved in Schwann Cell Myelination, Are Responsible for Arthrogryposis Multiplex Congenita

Arthrogryposis multiplex congenita (AMC) is a developmental condition characterized by multiple joint contractures resulting from reduced or absent fetal movements. Through genetic mapping of disease loci and whole-exome sequencing in four unrelated multiplex families presenting with severe AMC, we identified biallelic loss-of-function mutations in LGI4 (leucine-rich glioma-inactivated 4). LGI4 is a ligand secreted by Schwann cells that regulates peripheral nerve myelination via its cognate receptor ADAM22 expressed by neurons. Immunolabeling experiments and transmission electron microscopy of the sciatic nerve from one of the affected individuals revealed a lack of myelin. Functional tests using affected individual-derived iPSCs showed that these germline mutations caused aberrant splicing of the endogenous LGI4 transcript and in a cell-based assay impaired the secretion of truncated LGI4 protein. This is consistent with previous studies reporting arthrogryposis in Lgi4-deficient mice due to peripheral hypomyelination. This study adds to the recent reports implicating defective axoglial function as a key cause of AMC.

XIBD: software for inferring pairwise identity by descent on the X chromosome.

XIBD performs pairwise relatedness mapping on the X chromosome using dense single nucleotide polymorphism (SNP) data from either SNP chips or next generation sequencing data. It correctly accounts for the difference in chromosomal numbers between males and females and estimates global relatedness as well as regions of the genome that are identical by descent (IBD). XIBD also generates novel graphical summaries of all pairwise IBD tracts for a cohort making it very useful for disease locus mapping. XIBD is written in R/Rcpp and executed from shell scripts that are freely available from http://bioinf.wehi.edu.au/software/XIBD along with accompanying reference datasets. henden.l@wehi.edu.au Supplementary data are available at Bioinformatics online.

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data.

ABSTRACTAutozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.

Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3′ UTR regulation. We also showed that (1) 15%–36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling.

Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.We develop a hybrid approach (PIGS), which combines the computational efficiency of pairwise methods with the power of multiway methods. It leverages the IBD graph structure to compute the probability of IBD conditional on all pairwise estimates simultaneously. We show via extensive simulations and analysis of real data that our method produces a substantial increase in the number of identified small IBD segments.

Linkage disequilibrium measures for fine-scale mapping of disease loci are revisited

Genome-wide association (GWA) studies for fine-scale mapping of disease loci are conceptually based on the occurrence and history of non-random association of alleles at different loci in populations (or linkage disequilibrium). Thus, a new disease mutation arises on a single ancestral haplotype of a given population in complete linkage disequilibrium with syntenic polymorphic markers. Owing to historical recombination events, markers close to the disease locus will tend to be in strongest disequilibrium than distant markers when the presence of evolutionary mechanisms generating non-uniform disequilibrium patterns is ignored. Therefore, the use of optimal measures to assess gradients of the strength of disequilibrium along chromosomes is crucial for fine mapping of disease loci. In practice, an ideal disequilibrium measure for fine-scale mapping should be a monotone function of the recombination fraction between the marker and the disease locus. In an influential paper, Devlin and Risch (1995, henceforth referred to as “DR also described in the literature as r), Yule's Q (Nei and Li, 1980), and Kaplan and Weir's d (Kaplan and Weir, 1992). It was concluded that the measures δ and D' outperform all other measures for fine-scale mapping. In particular, D&R established analytically that δ is the ideal measure for fine mapping because changes of δ over generations are directly related to the recombination fraction (θ) between the disease and the marker locus. In contrast, they found that D' not only depends on θ, but also on the haplotype frequencies. This supposed advantage of δ over D' for fine-scale mapping of disease loci has become a classical paradigm in the literature on disequilibrium. Nevertheless, D&R's conclusion about the relative merits of δ and D' for fine-scale mapping based on analytic results is erroneous. As is shown below, the source of the error lies in a misinterpretation of the formulas used by D&R to assess the relationship between the decay of disequilibrium by recombination and the measures δ and D'. Despite the time elapsed, this misconception remains uncorrected. Following DR where D = π11π22 − π12π21 and Dmax is the lesser of π1+π+2 and π+1π2+ when D > 0, or the lesser of π1+π+1 and π2+π+2 when D 0 at generation 0; and θ between the disease and the marker locus is constant along generations. After n generations, the decay of the initial disequilibrium (D0) can be obtained as a function of θ by means of the expression Dn = (1 − θ)nD0, where Dn is the value of disequilibrium in the nth generation (Hedrick, 2005). Expressing the measures δ and D' in terms of (1 − θ)n, D&R obtained then the following mathematical relationship: (1−θ)n=Dn/D0 =⌊π11π22−π12π21⌋/π+1π22=δ (1) where π+1π22 was considered to be the best estimate of D0, the initial amount of linkage disequilibrium, given that π21 = 0 at generation 0 and hence π11 = π+1. In contrast: (1−θ)n=D′[1+(π21/π22)] (2) DR the formula (1) can be then rewritten as follows: (1−θ)n=Dn/D0 =⌊π11(n)π22(n)−π12(n)π21(n)⌋/π+1π22(0)=δ[π22(n)/π22(0)] Therefore, the relationship between δ and θ depends on the frequencies of the haplotype π22 at generations n and 0. In terms of D', the formula (1) can be rewritten as: (1−θ)n=Dn/D0 =Dn/π+1π22(0) =Dn/π+1π2+ =Dn/Dmax=D′n given that π22(0) = π2+ and Dmax = π+1π2+ when D > 0 and π+1π2+ < π1+π+2. It is shown, therefore, that D' is directly related to the recombination fraction. Likewise, the formula (2) can be rewritten as: (1−θ)n=D′n{1+[π21(0)/π22(0)]}=D′n given that π21(0) = 0. D&R studied the relationship between D' and θ when π1+ < π+1. Note, however, that π1+ = [π11(0) + π12(0)] cannot be lower than π+1 = [π11(0) + π21(0)] = π11 (0). Table 1 Layout and notation for population haplotypes, marker alleles, and disease allele frequencies in a 2 × 2 table. Our reanalysis on the relationship of the recombination fraction with the measures of disequilibrium δ and D' thus, contradicts the contention by D&R that δ outperforms D' for fine-scale mapping because changes of δ over generations are directly related to the recombination fraction. In fact, we reached the opposite conclusion to that of D&R. This finding reinforces the view that D' exhibits better statistical properties as a general measure of linkage desequilibrium than other commonly used measures (Zapata, 2011). In particular, D' seems to be an optimal measure for mapping of marker association and localization of disease loci sensu D&R.

DominantMapper: Rule-based analysis of SNP data for rapid mapping of dominant diseases in related nuclear families

With the advent of cheap rapid methods for whole-genome SNP genotyping and the completion of the Human Genome Project, mapping disease loci has become primarily a bioinformatic rather than a laboratory problem. Here, we describe DominantMapper, a computer program that implements a rule-based analysis algorithm for the detection of dominant disease loci in either a small number of nuclear families or a single large nuclear family. To demonstrate its utility, we present the successful analysis of two pedigrees in which the affected individuals carry either APC or TSPAN12 mutations.

Accelerating Haplotype-Based Genome-Wide Association Study Using Perfect Phylogeny and Phase-Known Reference Data

The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype inference, also known as phasing. In this study, we developed an efficient approach to haplotype-based analysis in GWAS. By using a reference panel, our method accelerated the phasing process and reduced the potential bias generated by unrealistic assumptions in phasing process. The haplotype-based approach delivers great power and no type I error inflation for association studies. With only a medium-size reference panel, phasing error in our method is comparable to the genotyping error afforded by commercial genotyping solutions.

Recent Admixture in an Indian Population of African Ancestry

Identification and study of genetic variation in recently admixed populations not only provides insight into historical population events but also is a powerful approach for mapping disease loci. We studied a population (OG-W-IP) that is of African-Indian origin and has resided in the western part of India for 500 years; members of this population are believed to be descendants of the Bantu-speaking population of Africa. We have carried out this study by using a set of 18,534 autosomal markers common between Indian, CEPH-HGDP, and HapMap populations. Principal-components analysis clearly revealed that the African-Indian population derives its ancestry from Bantu-speaking west-African as well as Indo-European-speaking north and northwest Indian population(s). STRUCTURE and ADMIXTURE analyses show that, overall, the OG-W-IPs derive 58.7% of their genomic ancestry from their African past and have very little inter-individual ancestry variation (8.4%). The extent of linkage disequilibrium also reveals that the admixture event has been recent. Functional annotation of genes encompassing the ancestry-informative markers that are closer in allele frequency to the Indian ancestral population revealed significant enrichment of biological processes, such as ion-channel activity, and cadherins. We briefly examine the implications of determining the genetic diversity of this population, which could provide opportunities for studies involving admixture mapping.

Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.

While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.

An Investigation of Genome-Wide Studies Reported Susceptibility Loci for Ulcerative Colitis Shows Limited Replication in North Indians

Genome-Wide Association studies (GWAS) of both Crohn's Disease (CD) and Ulcerative Colitis (UC) have unearthed over 40 risk conferring variants. Recently, a meta-analysis on UC revealed several loci, most of which were either previously associated with UC or CD susceptibility in populations of European origin. In this study, we attempted to replicate these findings in an ethnically distinct north Indian UC cohort. 648 UC cases and 850 controls were genotyped using Infinium Human 660W-quad. Out of 59 meta-analysis index SNPs, six were not in the SNP array used in the study. Of the remaining 53 SNPs, four were found monomorphic. Association (p<0.05) at 25 SNPs was observed, of which 15 were CD specific. Only five SNPs namely rs2395185 (HLA-DRA), rs3024505 (IL10), rs6426833 (RNF186), rs3763313 (BTNL2) and rs2066843 (NOD2) retained significance after Bonferroni correction. These results (i) reveal limited replication of Caucasian based meta-analysis results; (ii) reiterate overlapping molecular mechanism(s) in UC and CD; (iii) indicate differences in genetic architecture between populations; and (iv) suggest that resources such as HapMap need to be extended to cover diverse ethnic populations. They also suggest a systematic GWAS in this terrain may be insightful for identifying population specific IBD risk conferring loci and thus enable cross-ethnicity fine mapping of disease loci.

Assessment of the functionality of genome-wide canine SNP arrays and implications for canine disease association studies

Domestic dogs share a wide range of important disease conditions with humans, including cancers, diabetes and epilepsy. Many of these conditions have similar or identical underlying pathologies to their human counterparts and thus dogs represent physiologically relevant natural models of human disorders. Comparative genomic approaches whereby disease genes can be identified in dog diseases and then mapped onto the human genome are now recognized as a valid method and are increasing in popularity. The majority of dog breeds have been created over the past few hundred years and, as a consequence, the dog genome is characterized by extensive linkage disequilibrium (LD), extending usually from hundreds of kilobases to several megabases within a breed, rather than tens of kilobases observed in the human genome. Genome-wide canine SNP arrays have been developed, and increasing success of using these arrays to map disease loci in dogs is emerging. No equivalent of the human HapMap currently exists for different canine breeds, and the LD structure for such breeds is far less understood than for humans. This study is a dedicated large-scale assessment of the functionalities (LD and SNP tagging performance) of canine genome-wide SNP arrays in multiple domestic dog breeds. We have used genotype data from 18 breeds as well as wolves and coyotes genotyped by the Illumina 22K canine SNP array and Affymetrix 50K canine SNP array. As expected, high tagging performance was observed with most of the breeds using both Illumina and Affymetrix arrays when multi-marker tagging was applied. In contrast, however, large differences in population structure, LD coverage and pairwise tagging performance were found between breeds, suggesting that study designs should be carefully assessed for individual breeds before undertaking genome-wide association studies (GWAS).

Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations

The development of the human cerebral cortex is an orchestrated process involving the birth of neural progenitors in the peri-ventricular germinal zones, cell proliferation characterized by both symmetric and asymmetric mitoses, followed by migration of post-mitotic neurons to their final destinations in 6 highly ordered, functionally-specialized layers1,2. An understanding of the molecular mechanisms guiding these intricate processes is in its infancy, substantially driven by the discovery of rare mutations that cause malformations of cortical development (MCD)3-6. Mapping of disease loci in putative Mendelian forms of MCD has been hindered by marked locus heterogeneity, small kindred sizes and diagnostic classifications that may not reflect molecular pathogenesis. Here we demonstrate the use of whole-exome sequencing to overcome these obstacles by identifying recessive mutations in WDR62 as the cause of a wide spectrum of severe cerebral cortical malformations including microcephaly, pachygria with cortical thickening as well as hypoplasia of the corpus callosum. Some patients with WDR62 mutations had evidence of additional abnormalities including lissencephaly, schizencephaly, polymicrogyria and, in one instance, cerebellar hypoplasia, all traits traditionally regarded as distinct entities. In mouse and humans, WDR62 transcripts and protein are enriched in neural progenitors within the ventricular and subventricular zones. WDR62 expression in the neocortex is transient, spanning the period of embryonic neurogenesis. Unlike other known microcephaly genes, WDR62 does not apparently associate with centrosomes and is predominantly nuclear in localization. These findings unify previously disparate aspects of cerebral cortical development and highlight the utility of whole-exome sequencing to identify disease loci in settings in which traditional methods have proved challenging.

High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays

BackgroundSingle nucleotide polymorphisms (SNPs) have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. There is a need for systems allowing medium multiplexing (ten to hundreds of SNPs) with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set (thousands of individuals). Methods that are flexible, fast, accurate and cost-effective are urgently needed. This is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.ResultsWe demonstrate the use of a nanofluidic Integrated Fluidic Circuit (IFC) - based genotyping system for medium-throughput multiplexing known as the Dynamic Array, by genotyping 994 individual human DNA samples on 47 different SNP assays, using nanoliter volumes of reagents. Call rates of greater than 99.5% and call accuracies of greater than 99.8% were achieved from our study, which demonstrates that this is a formidable genotyping platform. The experimental set up is very simple, with a time-to-result for each sample of about 3 hours.ConclusionOur results demonstrate that the Dynamic Array is an excellent genotyping system for medium-throughput multiplexing (30-300 SNPs), which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. The exceptional call rates and call accuracy obtained may be of particular interest to those working on validation and replication of genome- wide- association (GWA) studies.