Genome-wide linkage studies for Alzheimer's disease have implicated several chromosomal regions as potential loci for susceptibility genes.
In the present study, we have combined a selection of affected relative pairs (ARPs) from the UK and the USA included in a previous linkage study by Myers et al. (Am J Med Genet, 2002), with ARPs from Sweden and Washington University. In this total sample collection of 397 ARPs, we have analyzed linkage to chromosomes 1, 9, 10, 12, 19 and 21, implicated in the previous scan.
The analysis revealed that linkage to chromosome 19q13 close to the APOE locus increased considerably as compared to the earlier scan. However, linkage to chromosome 10q21, which provided the strongest linkage in the previous scan could not be detected.
The present investigation provides yet further evidence that 19q13 is the only chromosomal region consistently linked to Alzheimer's disease.
A genome scan was previously performed and pointed to chromosome 6q21 as a candidate region for autism. This region contains the glutamate receptor 6 (GluR6 or GRIK2) gene, a functional candidate for the syndrome. Glutamate is the principal excitatory neurotransmitter in the brain and is directly involved in cognitive functions such as memory and learning. We used two different approaches, the affected sib-pair (ASP) method and the transmission disequilibrium test (TDT), to investigate the linkage and association between GluR6 and autism. The ASP method, conducted with additional markers on the 51 original families and in 8 new sibling pairs, showed a significant excess of allele sharing, generating an elevated multipoint maximum LOD score (ASPEX MLS = 3.28). TDT analysis, performed in the ASP families and in an independent data set of 107 parent-offspring trios, indicated a significant maternal transmission disequilibrium (TDTall P = 0.0004). Furthermore, TDT analysis (with only one affected proband per family) showed significant association between GluR6 and autism (TDT association P = 0.008). In contrast to maternal transmission, paternal transmission of GluR6 alleles was as expected in the absence of linkage, suggesting a maternal effect such as imprinting. Mutation screening was performed in 33 affected individuals, revealing several nucleotide polymorphisms (SNPs), including one amino acid change (M867I) in a highly conserved domain of the intracytoplasmic C-terminal region of the protein. This change is found in 8% of the autistic subjects and in 4% of the control population and seems to be more maternally transmitted than expected to autistic males (P = 0.007). Taken together, these data suggest that GluR6 is in linkage disequilibrium with autism.
Amino Acid Sequence; Autistic Disorder; genetics; Brain; physiopathology; Child; Chromosome Mapping; Chromosomes; Human; Pair 6; Exons; Family; Female; Genetic Markers; Genotype; Glutamic Acid; physiology; Humans; Linkage (Genetics); Male; Molecular Sequence Data; Open Reading Frames; Receptors; Kainic Acid; genetics; Restriction Mapping; autistic disorder; GluR6; GRIK2; mutation screening; affected sib-pair method; TDT; linkage disequilibrium; single nucleotide polymorphism; editing; isoforms
The basic idea of affected-sib-pair (ASP) linkage analysis is to test whether the inheritance pattern of a marker deviates from Mendelian expectation in a sample of ASPs. The test depends on an assumed Mendelian control distribution of the number of marker alleles shared identical by descent (IBD), i.e., 1/4, 1/2, and 1/4 for 2, 1, and 0 allele(s) IBD, respectively. However, Mendelian transmission may not always hold, for example because of inbreeding or meiotic drive at the marker or a nearby locus. A more robust and valid approach is to incorporate discordant-sib-pairs (DSPs) as controls to avoid possible false-positive results. To be robust to deviation from Mendelian transmission, here we analyzed Collaborative Study on the Genetics of Alcoholism data by modifying the ASP LOD score method to contrast the estimated distribution of the number of allele(s) shared IBD by ASPs with that by DSPs, instead of with the expected distribution under the Mendelian assumption. This strategy assesses the difference in IBD sharing between ASPs and the IBD sharing between DSPs. Further, it works better than the conventional LOD score ASP linkage method in these data in the sense of avoiding false-positive linkage evidence.
This study evaluated the utility of unrelated controls and flanking markers when performing joint modeling of linkage and association by the LAMP software (version 0.0.6) [Am J Hum Genet 2005, 76:934–949; Am J Hum Genet 2006, 78:778–792]. Analyses were conducted on the simulated rheumatoid arthritis (RA) data in Genetic Analysis Workshop 15 (GAW15), using single-nucleotide polymorphisms (SNPs) on chromosome 6 over the 100 simulated replicates. We found that the LOD score for testing association in the presence of linkage dramatically increased when unrelated controls were added to affected sib pairs (ASPs), and that choosing a sufficient number of flanking markers is critical in order to distinguish between perfect linkage disequilibrium (which leads to the conclusion of a measured SNP explaining a linkage signal) and incomplete linkage disequilibrium (which leads to the conclusion of other undetected causal variants in a linkage region).
The presence of linkage disequilibrium violates the underlying assumption of linkage equilibrium in most traditional multipoint linkage approaches. Studies have shown that such violation leads to bias in qualitative trait linkage analysis when parental genotypes are unavailable. Appropriate handling of marker linkage disequilibrium can avoid such false positive evidence. Using the rheumatoid arthritis simulated data from Genetic Analysis Workshop 15, we examined and compared the following three approaches to handle linkage disequilibrium among dense markers in both qualitative and quantitative trait linkage analyses: a simple algorithm; SNPLINK, methods for marker selection; and MERLIN-LD, a method for modeling linkage disequilibrium by creating marker clusters. In analysis ignoring linkage disequilibrium between markers, we observed LOD score inflation only in the affected sib-pair linkage analysis without parental genotypes; no such inflation was present in the quantitative trait locus linkage analysis with severity as our phenotype with or without parental genotypes. Using methods to model or adjust for linkage disequilibrium, we found a substantial reduction of inflation of LOD score in affected sib-pair linkage analysis. Greater LOD score reduction was observed by decreasing the amount of tolerable linkage disequilibrium among markers selected or marker clusters using MERLIN-LD; the latter approach showed most reduction. SNPLINK performed better with selected markers based on the D' measure of linkage disequilibrium as opposed to the r2 measure and outperformed the simple algorithm. Our findings reiterate the necessity of properly handling dense markers in linkage analysis, especially when parental genotypes are unavailable.
In a small chromosomal region, a number of polymorphisms may be both linked to and associated with a disease. Distinguishing the potential causal sites from those indirectly associated due to linkage disequilibrium (LD) with a causal site is an important problem. This problem may be approached by determining which of the associations can explain the observed linkage signal. Recently, several methods have been proposed to aid in the identification of disease associated polymorphisms that may explain an observed linkage signal, using genotype data from affected sib pairs (ASPs) [Li et al.  Am. J. Hum. Genet. 76:934–949; Sun et al.  Am. J. Hum. Genet. 70:399–411]. These methods can be used to test the null hypothesis that a candidate single nucleotide polymorphism (SNP) is the sole causal variant in the region, or is in complete LD with the sole causal variant in the region. We extend variations of these methods to test for complete LD between a disease locus and haplotypes composed of two or more tightly linked candidate SNPs. We study properties of the proposed methods by simulation and apply them to type 1 diabetes data for ASPs and their parents at candidate SNP and microsatellite marker loci in the Insulin (INS) gene region. Genet. Epidemiol. 31:2727–740, 2007. © 2007 Wiley-Liss, Inc.
fine mapping; association; conditional tests
The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state – identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.
Rheumatoid arthritis is a complex disease in which environmental factors interact with genetic factors that influence susceptibility. Incorporating information about related quantitative traits or environmental factors into linkage mapping could therefore greatly improve the efficiency and precision of identifying the disease locus. Using a multipoint linkage approach that allows the incorporation of quantitative variables into multipoint linkage mapping based on affected sib pairs, we incorporated data on anti-cyclic citrullinated peptide antibodies, immunoglobulin M rheumatoid factor and age at onset into genome-wide linkage scans. The strongest evidence of linkage was observed on chromosome 6p with a p-value of 3.8 × 10-15 for the genetic effect. The trait locus is estimated at approximately 45.51–45.82 cM, with standard errors of the estimates range from 0.82 to 1.26 cM, depending on whether and which quantitative variable is incorporated. The standard error of the estimate of trait locus decreased about 28% to 35% after incorporating the additional information from the quantitative variables. This mapping technique helps to narrow down the regions of interest when searching for a susceptibility locus and to elucidate underlying disease mechanisms.
Focusing on chromosome 1, a recursive partitioning linkage algorithm (RP) was applied to perform linkage analysis on the rheumatoid arthritis NARAC data, incorporating covariates such as HLA-DRB1 genotype, age at onset, severity, anti-cyclic citrullinated peptide (anti-CCP), and life time smoking. All 617 affected sib pairs from the ascertained families were used, and an RP linkage model was used to identify linkage possibly influenced by covariates. This algorithm includes a likelihood ratio (LR)-based splitting rule, a pruning algorithm to identify optimal tree size, and a bootstrap method for final tree selection.
The strength of the linkage signals was evaluated by empirical p-values, obtained by simulating marker data under null hypothesis of no linkage. Two suggestive linkage regions on chromosome 1 were detected by the RP linkage model, with identified associated covariates HLA-DRB1 genotype and age at onset. These results suggest possible gene × gene and gene × environment interactions at chromosome 1 loci and provide directions for further gene mapping.
Using single-nucleotide polymorphism (SNP) genotypes and selected gene expression phenotypes from 14 CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees provided for Genetic Analysis Workshop 15 (GAW15), we analyzed quantitative traits with artificial neural networks (ANNs). Our goals were to identify individual linkage signals and examine gene × gene interactions. First, we used classical multipoint methods to identify phenotypes having nominal linkage evidence at two or more loci. ANNs were then applied to sib-pair identity-by-descent (IBD) allele sharing across the genome as input variables and squared trait sums and differences for the sib pairs as output variables. The weights of the trained networks were analyzed to assess the linkage evidence at each locus as well as potential interactions between them.
Loci identified by classical linkage analysis could also be identified by our ANN analysis. However some ANN results were noisy, and our attempts to use cross-validated training to avoid overtraining and thereby improve results were only partially successful. Potential interactions between loci with high-ranked weight measures were also evaluated, with the resulting patterns suggesting existence of both synergistic and antagonistic effects between loci.
Our results suggest that ANNs can serve as a useful method to analyze quantitative traits and are a potential tool for detecting gene × gene interactions. However, for the approach implemented here, optimizing the ANNs and obtaining stable results remains challenging.
Currently the ε4 allele of the apolipoprotein E gene (APOE) is the strongest genetic risk factor for late onset Alzheimer's disease (AD). However, inheritance of the APOE ε 4 allele is not necessary or sufficient for the development of AD. Genetic evidence suggests that multiple loci in a 70 kb region surrounding APOE are associated with AD risk. Even though these loci could represent surrogate markers in linkage disequilibrium with APOE ε4 allele, they could also contribute biological effects independent of the APOE ε4 allele. Our previous study identified multiple SNPs upstream from APOE that are associated with cerebrospinal fluid apoE levels, suggesting that a haplotype structure proximal to APOE can influence apoE expression. In this study, we examined apoE expression in human post-mortem brain (PMB), and constructed chromosome-phase-separated haplotypes of the APOE proximal region to evaluate their effect on PMB apoE expression. ApoE protein expression was found to differ among AD brain regions and to differ between AD and control hippocampus. In addition, an extended APOE proximal haplotype structure, spanning from the TOMM40 gene to the APOE promoter, may modulate apoE expression in a brain region-specific manner and may influence AD disease status. In conclusion, this haplotype-phenotype analysis of apoE expression in PMB suggests that either; (1) the cis-regulation of APOE expression levels extends far upstream of the APOE promoter or (2) an APOE ε4 allele independent mechanism involving the TOMM40 gene plays a role in the risk of AD.
Alzheimer's disease; APOE; post-mortem brain; TOMM40
Despite the current trend towards large epidemiological studies of unrelated individuals, linkage studies in families are still thoroughly being utilized as tools for disease gene mapping. The use of the single-nucleotide-polymorphisms (SNP) array technology in genotyping of family data has the potential to provide more informative linkage data. Nevertheless, SNP array data are not immune to genotyping error which, as has been suggested in the past, could dramatically affect the evidence for linkage especially in selective designs such as affected sib pair (ASP) designs. The influence of genotyping error on selective designs for continuous traits has not been assessed yet.
We use the identity-by-descent (IBD) regression-based paradigm for linkage testing to analytically quantify the effect of simple genotyping error models under specific selection schemes for sibling pairs. We show, for example, that in extremely concordant (EC) designs, genotyping error leads to decreased power whereas it leads to increased type I error in extremely discordant (ED) designs. Perhaps surprisingly, the effect of genotyping error on inference is most severe in designs where selection is least extreme. We suggest a genomic control for genotyping errors via a simple modification of the intercept in the regression for linkage.
This study extends earlier findings: genotyping error can substantially affect type I error and power in selective designs for continuous traits. Designs involving both EC and ED sib pairs are fairly immune to genotyping error. When those designs are not feasible the simple genomic control strategy that we suggest offers the potential to deliver more robust inference, especially if genotyping is carried out by SNP array technology.
BACKGROUND AND AIMS—Genetic predisposition for inflammatory bowel disease (IBD) has been demonstrated by epidemiological and genetic linkage studies. Genetic linkage of IBD to chromosome 3 has been observed previously. A high density analysis of chromosome 3p was performed to confirm prior linkages and elucidate potential genetic associations.
METHODS—Forty three microsatellite markers on chromosome 3 were genotyped in 353 affected sibling pairs of North European Caucasian extraction (average marker density 2 cM in the linkage interval). Marker order was defined by genetic and radiation hybrid techniques.
RESULTS—The maximum single point logarithm of odds (LOD) score was observed for Crohn's disease at D3S3591. Peak multipoint LOD scores of 1.65 and 1.40 for the IBD phenotype were observed near D3S1304 (distal 3p) and near D3S1283 in the linkage region previously reported. Crohn's disease contributed predominantly to the linkage. The transmission disequilibrium test showed significant evidence of association (p=0.009) between allele 4 of D3S1076 and the IBD phenotype (51 transmitted v 28 non-transmitted). Two known polymorphisms in the CCR2 and CCR5 genes were analysed, neither of which showed significant association with IBD. Additional haplotype associations were observed in the vicinity of D3S1076.
CONCLUSIONS—This study provides confirmatory linkage evidence for an IBD susceptibility locus on chromosome 3p and suggests that CCR2 and CCR5 are unlikely to be major susceptibility loci for IBD. The association findings in this region warrant further investigation.
Keywords: inflammatory bowel disease; fine mapping; chromosome 3
Most linkage programs assume linkage equilibrium among multiple linked markers. This assumption may lead to bias for tightly linked markers where strong linkage disequilibrium (LD) exists. We used simulated data from Genetic Analysis Workshop 14 to examine the possible effect of LD on multipoint linkage analysis. Single-nucleotide polymorphism packets from a non-disease-related region that was generated with LD were used for both model-free and parametric linkage analyses. Results showed that high LD among markers can induce false-positive evidence of linkage for affected sib-pair analysis when parental data are missing. Bias can be eliminated with parental data and can be reduced when additional markers not in LD are included in the analyses.
Objective: To undertake a full genome-wide screen for Parkinson's disease susceptibility loci.
Methods: A genome-wide linkage study was undertaken in 227 affected sibling pairs from 199 pedigrees with Parkinson's disease. The pedigree sample consisted of 188 pedigrees from five European countries, and 11 from the USA. Individuals were genotyped for 391 microsatellite markers at ∼10 cM intervals throughout the genome. Multipoint model-free affected sibling pair linkage analyses were carried out using the MLS (maximum LOD score) test.
Results: There were six chromosomal regions with maximum MLS peaks of 1 or greater (pointwise p<0.018). Four of these chromosomal regions appear to be newly identified regions, and the highest MLS values were obtained on chromosomes 11q (MLS = 1.60, at 91 cM, D11S4175) and 7p (MLS = 1.51, at 5 cM, D7S531). The remaining two MLS peaks, on 2p11–q12 and 5q23, are consistent with excess sharing in regions reported by other studies. The highest MLS peak was observed on chromosome 2p11–q12 (MLS = 2.04, between markers D2S2216 and D2S160), within a relatively short distance (∼17 cM) from the PARK3 region. Although a stronger support of linkage to this region was observed in the late age of onset subgroup of families, these differences were not significant. The peak on 5q23 (MLS = 1.05, at 130 cM, D5S471) coincides with the region identified by three other genome scans. All peak locations fell within a 10 cM distance.
Conclusions: These stratified linkage analyses suggest linkage heterogeneity within the sample across the 2p11–q12 and 5q23 regions, with these two regions contributing independently to Parkinson's disease susceptibility.
Simulated Genetic Analysis Workshop14 data were analyzed by jointly testing linkage and association and by accounting for epistasis using a candidate gene approach. Our group was unblinded to the "answers." The 48 single-nucleotide polymorphisms (SNPs) within the six disease loci were analyzed in addition to five SNPs from each of two non-disease-related loci. Affected sib-parent data was extracted from the first 10 replicates for populations Aipotu, Kaarangar, and Danacaa, and analyzed separately for each replicate. We developed a likelihood for testing association and/or linkage using data from affected sib pairs and their parents. Identical-by-descent (IBD) allele sharing between sibs was explicitly modeled using a conditional logistic regression approach and incorporating a covariate that represents expected IBD allele sharing given the genotypes of the sibs and their parents. Interactions were accounted for by performing likelihood ratio tests in stages determined by the highest order interaction term in the model. In the first stage, main effects were tested independently, and in subsequent stages, multilocus effects were tested conditional on significant marginal effects. A reduction in the number of tests performed was achieved by prescreening gene combinations with a goodness-of-fit chi square statistic that depended on mating-type frequencies. SNP-specific joint effects of linkage and association were identified for loci D1, D2, D3, and D4 in multiple replicates. The strongest effect was for SNP B03T3056, which had a median p-value of 1.98 × 10-34. No two- or three-locus effects were found in more than one replicate.
To determine whether elderly normal APOE E2 (APOE2) carriers exhibit slower rates of hippocampal atrophy and memory decline compared to APOE3/3 carriers. We also determined whether APOE2 carriers have less Alzheimer pathology as reflected by CSF biomarkers.
We included longitudinal data from 134 cognitively normal individuals (27 APOE2/2 or E2/3, 107 APOE3/3) from the Alzheimer's Disease Neuroimaging Initiative, a prospective cohort study. A linear mixed-effects model was used to determine how APOE2 affected rates of hippocampal atrophy and cognitive change over time. In a subsample of 72 individuals who also underwent CSF analysis, an ordinary least-squares regression was used to determine whether CSF β-amyloid (Aβ), total tau, and phosphorylated tau-181 (p-tau) differed by APOE2 status.
APOE2 carriers demonstrated slower rates of hippocampal atrophy (p = 0.004). The mean rate of hippocampal atrophy among APOE2 carriers was −33 mm3/year (95% confidence interval −65 to +0.4), or −0.5%/year, compared to −86 mm3/year (95% confidence interval −102 to −71), or −1.3%/year, in the APOE3/3 group. No differences in the rates of episodic memory (p = 0.23) or overall cognitive change (p = 0.90) were detected. In the CSF subsample, APOE2 carriers had higher levels of CSF Aβ (p = 0.01), lower p-tau (p = 0.02), and marginally lower tau (p = 0.12).
A slower rate of hippocampal atrophy in normal APOE2 carriers is consistent with the lower risk of Alzheimer disease in these individuals. We hypothesize that the slower atrophy rate is related to decreased preclinical Alzheimer pathology.
= Alzheimer disease;
= Alzheimer's Disease Assessment Scale Cognitive Subscale;
= Alzheimer's Disease Neuroimaging Initiative;
= mild cognitive impairment;
= magnetization-prepared rapid gradient echo;
= phosphorylated tau-181;
= Wechsler Memory Scale–Revised.
Although many years of genetic epidemiological studies have demonstrated that genetics plays a significant role in determining smoking behavior, little information is available on genomic loci or genes affecting nicotine dependence. Several susceptibility chromosomal regions for nicotine dependence have been reported, but few have received independent confirmation. To identify susceptibility loci for nicotine dependence, 313 extended pedigrees selected from the Framingham Heart Study population were analyzed by both the GENEHUNTER and S.A.G.E. programs.
After performing linkage analyses on the 313 extended Framingham Heart Study families, the EM Haseman-Elston method implemented in GENEHUNTER provided evidence for significant linkage of smoking rate to chromosome 11 and suggestive linkage to chromosomes 9, 14, and 17. Multipoint sib-pair regression analysis using the SIBPAL program of S.A.G.E. on 1389 sib pairs that were split from the 313 extended families identified suggestive linkage of smoking rate to chromosomes 4, 7, and 17. Of these identified positive regions for nicotine dependence, loci on chromosomes 7, 11, and 17 were identified by both GENEHUNTER and S.A.G.E. programs.
Our genome-wide scan results on the Framingham Heart Study data provide evidence for significant linkage of smoking rate to chromosome 11 and suggestive linkage to chromosomes 4, 7, 9, 14, and 17. These findings suggest that some of these regions may harbor susceptibility loci for nicotine dependence, and warrant further investigation in this and other populations.
In the first reported positive result from a genome scan for non-insulin-dependent diabetes mellitus (NIDDM), Hanis et al. found significant evidence of linkage for NIDDM on chromosome 2q37 and named the putative disease locus NIDDM1 (Hanis et al. 1996. Nat. Genet. 13:161-166). Their total sample was comprised of 440 Mexican-American affected sib-pairs from 246 sibships. The strongest evidence for linkage was at marker D2S125 and best estimates of lambdas (risk to siblings of probands/population prevalence) using this marker were 1.37 under an additive model and 1.36 under a multiplicative model. We examined this chromosomal region using linkage analysis in a Finnish sample comprised of 709 affected sib-pairs from 472 sibships. We excluded this region in our sample (multipoint logarithm of odds score = -2) for lambdas >/= 1.37. We discuss possible reasons why linkage to 2q37 was not found and conclude that this region is unlikely to be playing a major role in NIDDM susceptibility in the Finnish Caucasian population.
The aim of the study was to identify chromosomal regions containing putative genetic variants influencing age-at-onset in familial late-onset Alzheimer’s disease. Data from a genome-wide scan that included genotyping of APOE was analyzed in 1,161 individuals from 209 families of Caribbean Hispanic ancestry with a mean age-at-onset of 73.3 years multiply affected by late-onset Alzheimer’s disease. Two-point and multipoint analyses were conducted using variance component methods from 376 microsatellite markers with an average inter-marker distance of 9.3 cM. Family-based test of association were also conducted for the same set of markers. Age-at-onset of symptoms among affected individuals was used as the quantitative trait. Our results showed that the presence of APOE-ε4 lowered the age-at-onset by three years. Using linkage analysis strategy, the highest LOD scores were obtained using a conservative definition of LOAD at 5q15 (LOD 3.1) 17q25.1 (LOD=2.94) and 14q32.12 (LOD=2.36) and 7q36.3 (LOD=2.29) in covariate adjusted models that included APOE-ε4. Both linkage and family-based association identified 17p13 as a candidate region. In addition, family-based association analysis showed markers at 12q13 (p=0.00002), 13q (p=0.00043) and 14q23 (p=0.00046) to be significantly associated with age at onset. The current study supports the hypothesis that there are additional genetic loci that could influence age-at-onset of late onset Alzheimer’s disease. The novel loci at 5q15, 17q25.1, 13q and 17p13, and the previously reported loci at 7q36.3, 12q13, 14q23 and 14q32 need further investigation.
Alzheimer’s disease; age-at-onset; linkage analysis; family-based association analysis; APOE
BACKGROUND—There is evidence for
genetic susceptibility to Crohn's disease, and a tentative association
with tumour necrosis factor (TNF) and HLA class II alleles.
AIMS—To examine the potential of
genetic linkage between Crohn's disease and the MHC region on
METHODS—TNF microsatellite markers
and, for some families, additional HLA antigens were typed for 323 individuals from 49Crohn's disease multiplex families to generate
informative haplotypes. Non-parametric linkage analysis methods,
including sib pair and affected relative pair methods, were used.
RESULTS—Increased sharing of
haplotypes was observed in affected sib pairs: 92% (48/52) shared one
or two haplotypes versus an expected 75% if linkage did not exist
(p=0.004). After other affected relative pairs were included, the
significance level reached 0.001. The mean proportion of haplotype
sharing was increased for both concordant affected (π=0.60, p=0.002)
and unaffected sib pairs (π=0.58, p=0.031) compared with the expected value (π=0.5). In contrast, sharing in discordant sib pairs was significantly decreased (π=0.42, p=0.007). Linear regression analysis using all three types of sib pairs yielded a slope of −0.38 at p=0.00003. It seemed that the HLA effect was stronger in non-Jewish families than in Jewish families.
analytical methods support linkage of Crohn's disease to the MHC
region in these Crohn's disease families. This region is estimated to
contribute approximately 10-33% of the total genetic risk to Crohn's disease.
Crohn's disease; HLA; linkage; inflammatory bowel
disease; tumour necrosis factor; genetics
Family history is one of the most consistent risk factors for dementia. Therefore, analysis of families with a distinct inheritance pattern of disease can be a powerful approach for the identification of previously unknown disease genes.
To map susceptibility regions for Alzheimer's disease.
A complete genome scan with 369 microsatellite markers was carried out in 12 extended families collected in Sweden. Age at disease onset ranged from 53 to 78 years, but in 10 of the families there was at least one member with age at onset of ⩽65 years. Mutations in known early‐onset Alzheimer's disease susceptibility genes have been excluded. All people were genotyped for APOE, but no clear linkage with the ε4 allele was observed.
Although no common disease locus could be found in all families, in two families an extended haplotype was identified on chromosome 8q shared by all affected members. In one of the families, a non‐parametric multimarker logarithm of the odds (LOD) score of 4.2 (p = 0.004) was obtained and analysis based on a dominant model showed a parametric LOD score of 2.4 for this region. All six affected members of this family shared a haplotype of 10 markers spanning about 40 cM. Three affected members in another family also shared a haplotype in the same region.
On the basis of our data, we propose the existence of a dominantly acting Alzheimer's disease susceptibility locus on chromosome 8.
Our Markov chain Monte Carlo (MCMC) methods were used in linkage analyses of the Framingham Heart Study data using all available pedigrees. Our goal was to detect and map loci associated with covariate-adjusted traits log triglyceride (lnTG) and high-density lipoprotein cholesterol (HDL) using multipoint LOD score analysis, Bayesian oligogenic linkage analysis and identity-by-descent (IBD) scoring methods. Each method used all marker data for all markers on a chromosome. Bayesian linkage analysis detected a linkage signal on chromosome 7 for lnTG and HDL, corroborating previously published results. However, these results were not replicated in a classical linkage analysis of the data or by using IBD scoring methods.
We conclude that Bayesian linkage analysis provides a powerful paradigm for mapping trait loci but interpretation of the Bayesian linkage signals is subjective. In the absence of a LOD score method accommodating genetically complex traits and linkage heterogeneity, validation of these signals remains elusive.
The purposes of this study were 1) to examine the performance of a new multimarker regression approach for model-free linkage analysis in comparison to a conventional multipoint approach, and 2) to determine the whether a conditioning strategy would improve the performance of the conventional multipoint method when applied to data from two interacting loci. Linkage analysis of the Kofendrerd Personality Disorder phenotype to chromosomes 1 and 3 was performed in three populations for all 100 replicates of the Genetic Analysis Workshop 14 simulated data. Three approaches were used: a conventional multipoint analysis using the Zlr statistic as calculated in the program ALLEGRO; a conditioning approach in which the per-family contribution on one chromosome was weighted according to evidence for linkage on the other chromosome; and a novel multimarker regression approach. The multipoint and multimarker approaches were generally successful in localizing known susceptibility loci on chromosomes 1 and 3, and were found to give broadly similar results. No advantage was found with the per-family conditioning approach. The effect on power and type I error of different choices of weighting scheme (to account for different numbers of affected siblings) in the multimarker approach was examined.
Motivation: We developed an EM-random forest (EMRF) for Haseman–Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.
Results: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman–Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs.
Availability: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF
Supplementary information: Supplementary data are available at www.infornomics.utoronto.ca/downloads/EMRF