The effect of linkage disequilibrium on linkage analysis of incomplete pedigrees

Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r(2 )between any pair of markers was ≤ 0.05. Additional simulation studies of affected sib pairs assuming uniform LD throughout a marker map demonstrated inflation of significance levels at r(2 )values greater than 0.05. When genotypes are available only from two affected siblings in many families in a sample, trimming SNP maps to limit r(2 )to 0–0.05 for all marker pairs will prevent inflation of linkage scores without sacrificing substantial linkage information. Simulation studies on the observed pedigree structures and map can also be used to determine the effect of LD on a particular study

Convergent genetic and expression data implicate immunity in Alzheimer's disease

© 2015, Elsevier Inc. All rights reserved. Background: Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. Methods: The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results: ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10-12 after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10-11), cholesterol transport (P = 2.96 × 10-9), and proteasome-ubiquitin activity (P = 1.34 × 10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P =.002-.05). Conclusions: The immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics.Medical Research Council, Alzheimer’s Research UK, and theWelsh Assembly Government. ADGC and CHARGE were supported by the National Institutes of Health, National Institute on Aging (NIH-NIA). CHARGE was also supported by Erasmus Medical Center and Erasmus University. IGAP was funded by the French National Foundation on Alzheimer’s Disease and Related Disorders, the Centre National de Genotypage and the Institut Pasteur de Lille, Inserm, FRC (Fondation pour la Recherche sur le Cerveau), and Rotary. This work has been developed and supported by the LABEX (Laboratory of Excellence Program Investment for the Future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer’s disease).Peer Reviewe

What is the pathogenic CAG expansion length in Huntington’s disease?

Huntington’s disease (HD) (OMIM 143100) is caused by an expanded CAG repeat tract in the HTT gene. The inherited CAG length is known to expand further in somatic and germline cells in HD subjects. Age at onset of the disease is inversely correlated with the inherited CAG length, but is further modulated by a series of genetic modifiers which are most likely to act on the CAG repeat in HTT that permit it to further expand. Longer repeats are more prone to expansions, and this expansion is age dependent and tissue-specific. Given that the inherited tract expands through life and most subjects develop disease in mid-life, this implies that in cells that degenerate, the CAG length is likely to be longer than the inherited length. These findings suggest two thresholds – the inherited CAG length which permits further expansion, and the intracellular pathogenic threshold, above which cells become dysfunctional and die. This two-step mechanism has been previously proposed and modelled mathematically to give an intracellular pathogenic threshold at a tract length of 115 CAG (95% confidence intervals 70-165 CAG). Empirically, the intracellular pathogenic threshold is difficult to determine. Clues from studies of people and models of HD, and from other diseases caused by expanded repeat tracts, place this threshold between 60-100 CAG, most likely towards the upper part of that range. We assess this evidence and discuss how the intracellular pathogenic threshold in manifest disease might be better determined. Knowing the cellular pathogenic threshold would be informative for both understanding the mechanism in HD and deploying treatments

Familial phenotype differences in PKD1111See Editorial, p. 344.

Familial phenotype differences in PKD1.BackgroundMutations within the PKD1 gene are responsible for the most common and most severe form of autosomal dominant polycystic kidney disease (ADPKD). Although it is known that there is a wide range of disease severity within PKD1 families, it is uncertain whether differences in clinical severity also occur among PKD1 families.MethodsTen large South Wales ADPKD families with at least 12 affected members were included in the study. From affected members, clinical information was obtained, including survival data and the presence of ADPKD-associated complications. Family members who were at risk of having inherited ADPKD but were proven to be non-affected were included as controls. Linkage and haplotype analysis were performed with highly polymorphic microsatellite markers closely linked to the PKD1 gene. Survival data were analyzed by the Kaplan–Meier method and the log rank test. Logistic regression analysis was used to test for differences in complication rates between families.ResultsHaplotype analysis revealed that each family had PKD1-linked disease with a unique disease-associated haplotype. Interfamily differences were observed in overall survival (P = 0.0004), renal survival (P = 0.0001), hypertension prevalence (P = 0.013), and hernia (P = 0.048). Individuals with hypertension had significantly worse overall (P = 0.0085) and renal (P = 0.03) survival compared with those without hypertension. No statistically significant differences in the prevalence of hypertension and hernia were observed among controls.ConclusionWe conclude that phenotype differences exist between PKD1 families, which, on the basis of having unique disease-associated haplotypes, are likely to be associated with a heterogeneous range of underlying PKD1 mutations

Covariate linkage analysis of GAW14 simulated data incorporating subclinical phenotype, sex, population, parent-of-origin, and interaction

BACKGROUND: We evaluate a method for the incorporation of covariates into linkage analysis using the Genetic Analysis Workshop 14 simulated data. Focusing on a randomly chosen replicate (42) we investigated the effect of the 12 subclinical phenotypes, sex, population, and parent-of-origin on the linkage signal from a model-free linkage analysis of Kofendrerd Personality Disorder. RESULTS: We detected a linkage peak on chromosome 1, at about 175 cM, which varied depending upon individuals' status for subclinical phenotype b. A linkage peak on chromosome 3 (310 cM) was found not to depend upon subclinical phenotype status. Further peaks were found on chromosomes 5 (12 cM), 9 (4 cM), and 10 (95 cM), depending on the status of subclinical phenotypes a, k, and c/d/g, respectively. CONCLUSION: Retrospective comparison of our results with the simulation model showed correct identification of disease loci D1-5 on chromosomes 1, 3, 5, 9 and 10, respectively

Large-scale linkage analysis of 1302 affected relative pairs with rheumatoid arthritis

Rheumatoid arthritis is the most common systematic autoimmune disease and its etiology is believed to have both strong genetic and environmental components. We demonstrate the utility of including genetic and clinical phenotypes as covariates within a linkage analysis framework to search for rheumatoid arthritis susceptibility loci. The raw genotypes of 1302 affected relative pairs were combined from four large family-based samples (North American Rheumatoid Arthritis Consortium, United Kingdom, European Consortium on Rheumatoid Arthritis Families, and Canada). The familiality of the clinical phenotypes was assessed. The affected relative pairs were subjected to autosomal multipoint affected relative-pair linkage analysis. Covariates were included in the linkage analysis to take account of heterogeneity within the sample. Evidence of familiality was observed with age at onset (p << 0.001) and rheumatoid factor (RF) IgM (p << 0.001), but not definite erosions (p = 0.21). Genome-wide significant evidence for linkage was observed on chromosome 6. Genome-wide suggestive evidence for linkage was observed on chromosomes 13 and 20 when conditioning on age at onset, chromosome 15 conditional on gender, and chromosome 19 conditional on RF IgM after allowing for multiple testing of covariates

Combining linkage data sets for meta-analysis and mega-analysis: the GAW15 rheumatoid arthritis data set

We have used the genome-wide marker genotypes from Genetic Analysis Workshop 15 Problem 2 to explore joint evidence for genetic linkage to rheumatoid arthritis across several samples. The data consisted of four high-density genome scans on samples selected for rheumatoid arthritis. We cleaned the data, removed intermarker linkage disequilibrium, and assembled the samples onto a common genetic map using genome sequence positions as a reference for map interpolation. The individual studies were combined first at the genotype level (mega-analysis) prior to a multipoint linkage analysis on the combined sample, and second using the genome scan meta-analysis method after linkage analysis of each sample. The two approaches were compared, and give strong support to the HLA locus on chromosome 6 as a susceptibility locus. Other regions of interest include loci on chromosomes 11, 2, and 12

Analyses of single marker and pairwise effects of candidate loci for rheumatoid arthritis using logistic regression and random forests

Using parametric and nonparametric techniques, our study investigated the presence of single locus and pairwise effects between 20 markers of the Genetic Analysis Workshop 15 (GAW15) North American Rheumatoid Arthritis Consortium (NARAC) candidate gene data set (Problem 2), analyzing 463 independent patients and 855 controls. Specifically, our work examined the correspondence between logistic regression (LR) analysis of single-locus and pairwise interaction effects, and random forest (RF) single and joint importance measures. For this comparison, we selected small but stable RFs (500 trees), which showed strong correlations (r~0.98) between their importance measures and those by RFs grown on 5000 trees. Both RF importance measures captured most of the LR single-locus and pairwise interaction effects, while joint importance measures also corresponded to full LR models containing main and interaction effects. We furthermore showed that RF measures were particularly sensitive to data imputation. The most consistent pairwise effect on rheumatoid arthritis was found between two markers within MAP3K7IP2/SUMO4 on 6q25.1, although LR and RFs assigned different significance levels

Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia

Background: Over the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15). Methods: We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls). Results: We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P=9 ×10−6). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a ‘neurodevelopmental hub’ on chromosome 8p11.23. Conclusions: This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4

Genetic modifiers of Mendelian disease: Huntington's disease and the trinucleotide repeat disorders

In the decades since the genes and mutations associated with the commoner Mendelian disorders were first discovered, technological advances in genetic analysis have made finding genomic variation a much less onerous task. Recently, the global efforts to collect subjects with Mendelian disorders, to better define the disorders and to empower appropriate clinical trials, along with improved genetic technologies, have allowed the identification of genetic variation that does not cause disease, but substantially modifies disease presentation. The advantage of this is it identifies biological pathways and molecules, that, if modified in people, might alter disease presentation. In Huntington’s disease (HD), caused by an expanded CAG repeat tract in HTT, genetic variation has been uncovered that is associated with change in the onset or progression of disease. Some of this variation lies in genes that are part of the DNA damage response, previously suggested to be important in modulating expansion of the repeat tract in germline and somatic cells. The genetic evidence implicates a DNA damage response-related pathway in modulating the pathogenicity of the repeat tracts in HD, and possibly, in other trinucleotide repeat disorders. These findings offer new targets for drug development in these currently intractable disorders