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Reducing the exome search space for Mendelian diseases using genetic linkage analysis of exome genotypes
Katherine R Smith, Catherine J Bromhead, Michael S Hildebrand, A Eliot Shearer, Paul J Lockhart, Hossein Najmabadi, Richard J Leventer, George McGillivray, David J Amor, Richard J Smith, Melanie Bahlo
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-9-r85
Abstract: Whole exome sequencing (WES) has recently become a popular strategy for discovering potential causal variants in individuals with inherited Mendelian disorders, providing a cost- effective, fast-track approach to variant discovery. However, a typical human genome differs from the reference genome at over 10,000 potentially functional sites [1]; identifying the disease-causing mutation among this plethora of variants can be a significant challenge. For this reason, exome sequencing is often preceded by genetic linkage analysis, which allows variants outside of linkage peaks to be excluded. The linkage peaks delineate tracts of identity by descent sharing that match the proposed genetic model. This combination strategy has been successfully used to identify variants causing autosomal dominant [2-4] and recessive [5-11] diseases, as well as those affecting quantitative traits [12-14]. Linkage analysis has also been used in conjunction with whole genome sequencing (WGS) [15].Other WES studies have not performed formal linkage analysis, but have nonetheless considered inheritance information, such as searching for large regions of homozygosity shared by affected family members using genotypes obtained from genotyping arrays [16-18] or exome data [19,20]. This method does not incorporate genetic map or allele frequency information, which could help to eliminate regions from consideration, and is applicable only to recessive diseases resulting from consanguinity. Recently, it has been suggested that identity by descent regions be identified from exome data using a non-homogeneous hidden Markov model (HMM), allowing variants outside these regions to be eliminated [21,22]. This method incorporates genetic map information but not allele frequency information and requires a strict genetic model (recessive and fully penetrant) and sampling scheme (exomes of two or more affected siblings must be sequenced). It would be suboptimal for use with diseases resulting from consanguinit
Unlocking Mendelian disease using exome sequencing
Christian Gilissen, Alexander Hoischen, Han G Brunner, Joris A Veltman
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-9-228
Abstract: The identification of the causative mutation for a Mendelian disease enables molecular diagnosis and carrier testing in the patient and his or her family. This is of great importance for patient management and family counseling, and serves as a starting point for therapeutic interventions [1]. Furthermore, the identification of Mendelian disease genes contributes to our understanding of gene functions and biological pathways underlying health and disease in general [2], and lessons learned from rare diseases are often also relevant to common disease [3]. Research aimed at the identification of genes that cause Mendelian disease has received a boost over the past couple of years by the introduction of new technologies that enable the sequencing of DNA at a much higher throughput and at much lower costs than previously possible [4].Although traditional gene mapping approaches (such as karyotyping [5], linkage analysis [6] homozygosity mapping [7] and copy number variation (CNV) analysis [8]) have led to great insights into Mendelian disease over the past few decades (Figure 1), they are unable to detect all forms of genomic variation (Table 1). The approach applied is dependent on whether the disease is, for example, caused by single nucleotide mutations or by CNVs, which is difficult to predict in advance. In addition, mapping approaches would often not reduce the number of candidate genes sufficiently for straightforward follow-up by Sanger sequencing [9]. For example, genome-wide single nucleotide polymorphism analysis in a large Dutch pedigree with autosomal-dominant familial exudative vitreoretinopathy (FEVR, MIM 613310), a retinal disorder, identified a linkage peak of about 40 Mb on chromosome 7, containing more than 300 genes [10]. Even after adding linkage data from a second FEVR family the region was still too large for straightforward disease-gene identification, and Sanger sequencing of a few candidate genes did not identify causative mutations. Next gener
Mendelian and Non-Mendelian Quadratic Operators  [PDF]
Nasir Ganikhodjaev,Mansoor Saburov,Uygun Jamilov
Quantitative Biology , 2013,
Abstract: In this paper, we attempt to provide mathematical models of Mendelian and Non-Mendelian inheritances of the bisexual population system having Fisher's {\textbf{1:1}} principle. In our model, we always assume that distributions of the same phenotype of female and male populations are equal. We study the evolution of a Mendelian trait. As an application of a non-Mendelian inheritance, we construct a quadratic stochastic operator that describes transmission of {\textbf{ABO}} and Rh blood groups.
Commentary on "Genetic linkage and transmission disequilibrium of marker haplotypes at chromosome 1q41 in human systemic lupus erythematosus", by RR Graham et al.
Anne C Barton, Jane Worthington
Arthritis Research & Therapy , 2001, DOI: 10.1186/ar394
Abstract: Because linkage analysis approaches had been successful in the identification of disorders inherited as Mendelian traits, it was expected that the genetic basis of common diseases would be identified using a similar approach, but results to date may seem disappointing. As for most common diseases, susceptibility to autoimmunity is thought to be determined by both genetic and environmental factors. These autoimmune diseases tend not to be inherited in simple Mendelian fashion, but exhibit complex patterns of segregation. Investigation of these diseases can often be hampered by factors such as late age at disease onset, variable penetrance, variable phenotypic expression (different combinations of genes may predispose to different patterns of disease), unknown gene–gene and gene–environment interactions, genetic heterogeneity (different genes may produce the same phenotype), and misclassification of clinical phenotypes. Hence, the task of identifying susceptibility genes for complex disorders is enormous.Twin and family studies suggest that systemic lupus erythematosus (SLE) has a substantial genetic susceptibility component [1,2,3]. Whole-genome scans of SLE families with affected sibling pairs have now been published, and, despite the relatively small sizes of the individual studies and the ethnic heterogeneity of the populations studied, there appears to be a surprising degree of overlap between findings [4,5,6,7,8]. All the studies have reported linkage to regions of the long arm of chromosome 1. In volume 3 issue 5 of this journal, Graham et al. described their approach to following up this linkage data for one of these regions, mapping to 1q41–42 [9].Linkage analysis identifies genomic regions that are shared, identical-by-descent, by siblings affected by disease more often than would be expected by chance alone. However, linkage typically extends for 10 cM or more and such a region could contain 500 genes. Variation in any one of these genes could be responsibl
Linkage Mapping Identifies the Sex Determining Region as a Single Locus in the Pennate Diatom Seminavis robusta  [PDF]
Ives Vanstechelman, Koen Sabbe, Wim Vyverman, Pieter Vanormelingen, Marnik Vuylsteke
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0060132
Abstract: The pennate diatom Seminavis robusta, characterized by an archetypical diatom life cycle including a heterothallic mating system, is emerging as a model system for studying the molecular regulation of the diatom cell and life cycle. One of its main advantages compared with other diatom model systems is that sexual crosses can be made routinely, offering unprecedented possibilities for forward genetics. To date, nothing is known about the genetic basis of sex determination in diatoms. Here, we report on the construction of mating type-specific linkage maps for S. robusta, and use them to identify a single locus sex determination system in this diatom. We identified 13 mating type plus and 15 mating type minus linkage groups obtained from the analysis of 463 AFLP markers segregating in a full-sib family, covering 963.7 and 972.2 cM, respectively. Five linkage group pairs could be identified as putative homologues. The mating type phenotype mapped as a monogenic trait, disclosing the mating type plus as the heterogametic sex. This study provides the first evidence for a genetic sex determining mechanism in a diatom.
A Picea abies Linkage Map Based on SNP Markers Identifies QTLs for Four Aspects of Resistance to Heterobasidion parviporum Infection  [PDF]
M?rten Lind, Thomas K?llman, Jun Chen, Xiao-Fei Ma, Jean Bousquet, Michele Morgante, Giusi Zaina, Bo Karlsson, Malin Elfstrand, Martin Lascoux, Jan Stenlid
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0101049
Abstract: A consensus linkage map of Picea abies, an economically important conifer, was constructed based on the segregation of 686 SNP markers in a F1 progeny population consisting of 247 individuals. The total length of 1889.2 cM covered 96.5% of the estimated genome length and comprised 12 large linkage groups, corresponding to the number of haploid P. abies chromosomes. The sizes of the groups (from 5.9 to 9.9% of the total map length) correlated well with previous estimates of chromosome sizes (from 5.8 to 10.8% of total genome size). Any locus in the genome has a 97% probability to be within 10 cM from a mapped marker, which makes the map suited for QTL mapping. Infecting the progeny trees with the root rot pathogen Heterobasidion parviporum allowed for mapping of four different resistance traits: lesion length at the inoculation site, fungal spread within the sapwood, exclusion of the pathogen from the host after initial infection, and ability to prevent the infection from establishing at all. These four traits were associated with two, four, four and three QTL regions respectively of which none overlapped between the traits. Each QTL explained between 4.6 and 10.1% of the respective traits phenotypic variation. Although the QTL regions contain many more genes than the ones represented by the SNP markers, at least four markers within the confidence intervals originated from genes with known function in conifer defence; a leucoanthocyanidine reductase, which has previously been shown to upregulate during H. parviporum infection, and three intermediates of the lignification process; a hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyltransferase, a 4-coumarate CoA ligase, and a R2R3-MYB transcription factor.
Improving sequence-based genotype calls with linkage disequilibrium and pedigree information  [PDF]
Baiyu Zhou,Alice S. Whittemore
Quantitative Biology , 2012, DOI: 10.1214/11-AOAS527
Abstract: Whole and targeted sequencing of human genomes is a promising, increasingly feasible tool for discovering genetic contributions to risk of complex diseases. A key step is calling an individual's genotype from the multiple aligned short read sequences of his DNA, each of which is subject to nucleotide read error. Current methods are designed to call genotypes separately at each locus from the sequence data of unrelated individuals. Here we propose likelihood-based methods that improve calling accuracy by exploiting two features of sequence data. The first is the linkage disequilibrium (LD) between nearby SNPs. The second is the Mendelian pedigree information available when related individuals are sequenced. In both cases the likelihood involves the probabilities of read variant counts given genotypes, summed over the unobserved genotypes. Parameters governing the prior genotype distribution and the read error rates can be estimated either from the sequence data itself or from external reference data. We use simulations and synthetic read data based on the 1000 Genomes Project to evaluate the performance of the proposed methods. An R-program to apply the methods to small families is freely available at http://med.stanford.edu/epidemiology/PHGC/.
Can we apply the Mendelian randomization methodology without considering epigenetic effects?
Ikechukwu U Ogbuanu, Hongmei Zhang, Wilfried Karmaus
Emerging Themes in Epidemiology , 2009, DOI: 10.1186/1742-7622-6-3
Abstract: Mendelian randomization studies using instrumental variables (IVs) have the potential to avoid some of the limitations of observational epidemiology (confounding, reverse causality, regression dilution bias) for making causal inferences. Certain limitations of randomized controlled trials, such as problems with generalizability, feasibility and ethics for some exposures, and high costs, also make the use of Mendelian randomization in observational studies attractive. Unlike conventional randomized controlled trials (RCTs), Mendelian randomization studies can be conducted in a representative sample without imposing any exclusion criteria or requiring volunteers to be amenable to random treatment allocation.Within the last decade, epigenetics has gained recognition as an independent field of study, and appears to be the new direction for future research into the genetics of complex diseases. Although previous articles have addressed some of the limitations of Mendelian randomization (such as the lack of suitable genetic variants, unreliable associations, population stratification, linkage disequilibrium (LD), pleiotropy, developmental canalization, the need for large sample sizes and some potential problems with binary outcomes), none has directly characterized the impact of epigenetics on Mendelian randomization. The possibility of epigenetic effects (non-Mendelian, heritable changes in gene expression not accompanied by alterations in DNA sequence) could alter the core instrumental variable assumptions of Mendelian randomization.This paper applies conceptual considerations, algebraic derivations and data simulations to question the appropriateness of Mendelian randomization methods when epigenetic modifications are present.Given an inheritance of gene expression from parents, Mendelian randomization studies not only need to assume a random distribution of alleles in the offspring, but also a random distribution of epigenetic changes (e.g. gene expression) at concept
Mendelian and Non-Mendelian Regulation of Gene Expression in Maize  [PDF]
Lin Li,Katherine Petsch,Rena Shimizu,Sanzhen Liu,Wayne Wenzhong Xu,Kai Ying,Jianming Yu,Michael J. Scanlon,Patrick S. Schnable,Marja C. P. Timmermans,Nathan M. Springer,Gary J. Muehlbauer
PLOS Genetics , 2013, DOI: 10.1371/journal.pgen.1003202
Abstract: Transcriptome variation plays an important role in affecting the phenotype of an organism. However, an understanding of the underlying mechanisms regulating transcriptome variation in segregating populations is still largely unknown. We sought to assess and map variation in transcript abundance in maize shoot apices in the intermated B73×Mo17 recombinant inbred line population. RNA–based sequencing (RNA–seq) allowed for the detection and quantification of the transcript abundance derived from 28,603 genes. For a majority of these genes, the population mean, coefficient of variation, and segregation patterns could be predicted by the parental expression levels. Expression quantitative trait loci (eQTL) mapping identified 30,774 eQTL including 96 trans-eQTL “hotspots,” each of which regulates the expression of a large number of genes. Interestingly, genes regulated by a trans-eQTL hotspot tend to be enriched for a specific function or act in the same genetic pathway. Also, genomic structural variation appeared to contribute to cis-regulation of gene expression. Besides genes showing Mendelian inheritance in the RIL population, we also found genes whose expression level and variation in the progeny could not be predicted based on parental difference, indicating that non-Mendelian factors also contribute to expression variation. Specifically, we found 145 genes that show patterns of expression reminiscent of paramutation such that all the progeny had expression levels similar to one of the two parents. Furthermore, we identified another 210 genes that exhibited unexpected patterns of transcript presence/absence. Many of these genes are likely to be gene fragments resulting from transposition, and the presence/absence of their transcripts could influence expression levels of their ancestral syntenic genes. Overall, our results contribute to the identification of novel expression patterns and broaden the understanding of transcriptional variation in plants.
Genome-Wide Linkage and Association Analysis Identifies Major Gene Loci for Guttural Pouch Tympany in Arabian and German Warmblood Horses  [PDF]
Julia Metzger, Bernhard Ohnesorge, Ottmar Distl
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0041640
Abstract: Equine guttural pouch tympany (GPT) is a hereditary condition affecting foals in their first months of life. Complex segregation analyses in Arabian and German warmblood horses showed the involvement of a major gene as very likely. Genome-wide linkage and association analyses including a high density marker set of single nucleotide polymorphisms (SNPs) were performed to map the genomic region harbouring the potential major gene for GPT. A total of 85 Arabian and 373 German warmblood horses were genotyped on the Illumina equine SNP50 beadchip. Non-parametric multipoint linkage analyses showed genome-wide significance on horse chromosomes (ECA) 3 for German warmblood at 16–26 Mb and 34–55 Mb and for Arabian on ECA15 at 64–65 Mb. Genome-wide association analyses confirmed the linked regions for both breeds. In Arabian, genome-wide association was detected at 64 Mb within the region with the highest linkage peak on ECA15. For German warmblood, signals for genome-wide association were close to the peak region of linkage at 52 Mb on ECA3. The odds ratio for the SNP with the highest genome-wide association was 0.12 for the Arabian. In conclusion, the refinement of the regions with the Illumina equine SNP50 beadchip is an important step to unravel the responsible mutations for GPT.
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