Empirical significance values for linkage analysis: trait simulation using posterior model distributions from MCMC oligogenic segregation analysis

Genome scan for cognitive trait loci of dyslexia: Rapid naming and rapid switching of letters, numbers, and colors

Dyslexia, or specific reading disability, is a common developmental disorder that affects 5-12% of school-aged children. Dyslexia and its component phenotypes, assessed categorically or quantitatively, have complex genetic bases. The ability to rapidly name letters, numbers, and colors from rows presented visually correlates strongly with reading in multiple languages and is a valid predictor of reading and spelling impairment. Performance on measures of rapid naming and switching, RAN and RAS, is stable throughout elementary school years, with slowed performance persisting in adults who still manifest dyslexia. Targeted analyses of dyslexia candidate regions have included RAN measures, but only one other genome-wide linkage study has been reported. As part of a broad effort to identify genetic contributors to dyslexia, we performed combined oligogenic segregation and linkage analyses of measures of RAN and RAS in a family-based cohort ascertained through probands with dyslexia. We obtained strong evidence for linkage of RAN letters to the DYX3 locus on chromosome 2p and RAN colors to chromosome 10q, but were unable to confirm the chromosome 6p21 linkage detected for a composite measure of RAN colors and objects in the previous genome-wide study.

Genome scan in familial late-onset Alzheimer's disease: a locus on chromosome 6 contributes to age-at-onset

Alzheimer's disease (AD) is a common, genetically complex, fatal neurodegenerative disorder of late life. Although several genes are known to play a role in early-onset AD, identification of the genetic basis of late onset AD (LOAD) has been challenging, with only the APOE gene known to have a high contribution to both AD risk and age-at-onset. Here, we present the first genome-scan analysis of the complete, well-characterized University of Washington LOAD sample of 119 pedigrees, using age-at-onset as the trait of interest. The analysis approach used allows for a multilocus trait model while at the same time accommodating age censoring, effects of APOE as a known genetic covariate, and full pedigree and marker information. The results provide strong evidence for linkage of loci contributing to age-at-onset to genomic regions on chromosome 6q16.3, and to 19q13.42 in the region of the APOE locus. There was evidence for interaction between APOE and the locus on chromosome 6q and suggestive evidence for linkage to chromosomes 11p13, 15q12-14, and 19p13.12. These results provide the first independent confirmation of an AD age-at-onset locus on chromosome 6 and suggest that further efforts towards identifying the underlying causal locus or loci are warranted.

Genome scan of age-at-onset in the NIMH Alzheimer disease sample uncovers multiple loci, along with evidence of both genetic and sample heterogeneity

Alzheimer's disease (AD) is a common neurodegenerative disorder of late life with a complex genetic basis. Although several genes are known to play a role in rare early onset AD, only the APOE gene is known to have a high contribution to risk of the common late-onset form of the disease (LOAD, onset >60 years). APOE genotypes vary in their AD risk as well as age-at-onset distributions, and it is likely that other loci will similarly affect AD age-at-onset. Here we present the first analysis of age-at-onset in the NIMH LOAD sample that allows for both a multilocus trait model and genetic heterogeneity among the contributing sites, while at the same time accommodating age censoring, effects of known genetic covariates, and full pedigree and marker information. The results provide evidence for genomic regions not previously implicated in this data set, including regions on chromosomes 7q, 15, and 19p. They also affirm evidence for loci on chromosomes 1q, 6p, 9q, 11, and, of course, the APOE locus on 19q, all of which have been reported previously in the same sample. The analyses failed to find evidence for linkage to chromosome 10 with inclusion of unaffected subjects and extended pedigrees. Several regions implicated in these analyses in the NIMH sample have been previously reported in genome scans of other AD samples. These results, therefore, provide independent confirmation of AD loci in family-based samples on chromosomes 1q, 7q, 19p, and suggest that further efforts towards identifying the underlying causal loci are warranted.

Linkage and association of phospholipid transfer protein activity to LASS4

Phospholipid transfer protein activity (PLTPa) is associated with insulin levels and has been implicated in atherosclerotic disease in both mice and humans. Variation at the PLTP structural locus on chromosome 20 explains some, but not all, heritable variation in PLTPa. In order to detect quantitative trait loci (QTLs) elsewhere in the genome that affect PLTPa, we performed both oligogenic and single QTL linkage analysis on four large families (n = 227 with phenotype, n = 330 with genotype, n = 462 total), ascertained for familial combined hyperlipidemia. We detected evidence of linkage between PLTPa and chromosome 19p (lod = 3.2) for a single family and chromosome 2q (lod = 2.8) for all families. Inclusion of additional marker and exome sequence data in the analysis refined the linkage signal on chromosome 19 and implicated coding variation in LASS4, a gene regulated by leptin that is involved in ceramide synthesis. Association between PLTPa and LASS4 variation was replicated in the other three families (P = 0.02), adjusting for pedigree structure. To our knowledge, this is the first example for which exome data was used in families to identify a complex QTL that is not the structural locus.

Genome-scan for IQ discrepancy in autism: evidence for loci on chromosomes 10 and 16

Performance IQ (PIQ) greater than verbal IQ (VIQ) is often observed in studies of the cognitive abilities of autistic individuals. This characteristic is correlated with social and communication impairments, key parts of the autism diagnosis. We present the first genetic analyses of IQ discrepancy (PIQ-VIQ) as an autism-related phenotype. We performed genome-wide joint linkage and segregation analyses on 287 multiplex families, using a Markov chain Monte Carlo approach. Genetic data included a genome-scan of 387 micro-satellite markers in 210 families augmented with additional markers added in a subset of families. Empirical P values were calculated for five interesting regions. Linkage analysis identified five chromosomal regions with substantial regional evidence of linkage; 10p12 [P = 0.001; genome-wide (gw) P = 0.05], 16q23 (P = .015; gw P = 0.53), 2p21 (P = 0.03, gw P = 0.78), 6q25 (P = 0.047, gw P = 0.91) and 15q23-25 (P = 0.053, gw P = 0.93). The location of the chromosome 10 linkage signal coincides with a region noted in a much earlier genome-scan for autism, and the chromosome 16 signal coincides exactly with a linkage signal for non-word repetition in specific language impairment. This study provides strong evidence for a QTL influencing IQ discrepancy in families with autistic individuals on chromosome 10, and suggestive evidence for a QTL on chromosome 16. The location of the chromosome 16 signal suggests a candidate gene, CDH13, a T-cadherin expressed in the brain, which has been implicated in previous SNP studies of autism and ADHD.

Genome scan for spelling deficits: effects of verbal IQ on models of transmission and trait gene localization

Dyslexia is a complex learning disability with evidence for a genetic basis. Strategies that may be useful for dissecting its genetic basis include the study of component phenotypes, which may simplify the underlying genetic complexity, and use of an analytic approach that accounts for the multilocus nature of the trait to guide the investigation and increase power to detect individual loci. Here we present results of a genetic analysis of spelling disability as a component phenotype. Spelling disability is informative in analysis of extended pedigrees because it persists into adulthood. We show that a small number of hypothesized loci are sufficient to explain the inheritance of the trait in our sample, and that each of these loci maps to one of four genomic regions. Individual trait models and locations are a function of whether a verbal IQ adjustment is included, suggesting mediation through both IQ-related and unrelated pathways.

A multi-stage multi-design strategy provides strong evidence that the BAI3 locus is associated with early-onset venous thromboembolism

BACKGROUND

Factor VIII (FVIII) and von Willebrand factor (VWF) are two known quantitative risk factors for venous thromboembolism (VTE).

OBJECTIVES

To identify new loci that could contribute to VTE susceptibility and to modulating FVIII and/or VWF levels.

PATIENTS/METHODS

A pedigree linkage analysis was first performed in five extended French-Canadian families, including 253 individuals, to identify genomic regions linked to FVIII or VWF levels. Identified regions were further explored using 'in silico' genome-wide association studies (GWAS) data on VTE (419 patients and 1228 controls), and two independent case-control studies (MARTHA and FARIVE) for VTE, gathering 1166 early-onset patients and 1408 healthy individuals. Single nucleotide polymorphisms (SNPs) associated with VTE risk were further investigated in relation to plasma levels of FVIII and VWF in a cohort of 108 healthy nuclear families.

RESULTS

Four main linkage regions were identified, among which the well-characterized ABO locus, the recently identified STAB 2 gene, and a third one, on chromosome 6q13-14, harbouring four non-redundant SNPs, associated with VTE at P < 10(-4) in the GWAS dataset. The association of one of these SNPs, rs9363864, with VTE was further replicated in the MARTHA and FARIVE studies. The rs9363864-AA genotype was associated with a lower risk for VTE (OR = 0.58 [0.42-0.80], P = 0.0005) but mainly in non-carriers of the FV Leiden mutation. This genotype was further found to be associated with the lowest levels of FVIII (P = 0.006) and VWF (P = 0.001).

CONCLUSIONS

The BAI3 locus where the rs9363864 maps is a new candidate for VTE risk.

Modifier locus of the skeletal muscle involvement in Emery-Dreifuss muscular dystrophy

Autosomal dominant Emery-Dreifuss muscular dystrophy is caused by mutations in LMNA gene encoding lamins A and C. The disease is characterized by early onset joint contractures during childhood associated with humero-peroneal muscular wasting and weakness, and by the development of a cardiac disease in adulthood. Important intra-familial variability characterized by a wide range of age at onset of myopathic symptoms (AOMS) has been recurrently reported, suggesting the contribution of a modifier gene. Our objective was to identify a modifier locus of AOMS in relation with the LMNA mutation. To map the modifier locus, we genotyped 291 microsatellite markers in 59 individuals of a large French family, where 19 patients carrying the same LMNA mutation, exhibited wide range of AOMS. We performed Bayesian Markov Chain Monte Carlo-based joint segregation and linkage methods implemented in the Loki software, and detected a strong linkage signal on chromosome 2 between markers D2S143 and D2S2244 (211 cM) with a Bayes factor of 28.7 (empirical p value = 0.0032). The linked region harbours two main candidate genes, DES and MYL1 encoding desmin and light chain of myosin. Importantly, the impact of the genotype on the phenotype for this locus showed an overdominant effect with AOMS 2 years earlier for the homozygotes of the rare allele and 37 years earlier for the heterozygotes than the homozygotes for the common allele. These results provide important highlights for the natural history and for the physiopathology of Emery-Dreifuss muscular dystrophy.

Evidence for three loci modifying age-at-onset of Alzheimer's disease in early-onset PSEN2 families

Families with early-onset Alzheimer's disease (AD) sharing a single PSEN2 mutation exhibit a wide range of age-at-onset, suggesting that modifier loci segregate within these families. While APOE is known to be an age-at-onset modifier, it does not explain all of this variation. We performed a genome scan within nine such families for loci influencing age-at-onset, while simultaneously controlling for variation in the primary PSEN2 mutation (N141I) and APOE. We found significant evidence of linkage between age-at-onset and chromosome 1q23.3 (P < 0.001) when analysis included all families, and to chromosomes 1q23.3 (P < 0.001), 17p13.2 (P = 0.0002), 7q33 (P = 0.017), and 11p14.2 (P = 0.017) in a single large pedigree. Simultaneous analysis of these four chromosomes maintained strong evidence of linkage to chromosomes 1q23.3 and 17p13.2 when all families were analyzed, and to chromosomes 1q23.3, 7q33, and 17p13.2 within the same single pedigree. Inclusion of major gene covariates proved essential to detect these linkage signals, as all linkage signals dissipated when PSEN2 and APOE were excluded from the model. The four chromosomal regions with evidence of linkage all coincide with previous linkage signals, associated SNPs, and/or candidate genes identified in independent AD study populations. This study establishes several candidate regions for further analysis and is consistent with an oligogenic model of AD risk and age-at-onset. More generally, this study also demonstrates the value of searching for modifier loci in existing datasets previously used to identify primary causal variants for complex disease traits.

Power of competing strategies of linkage analysis for complex traits

Variance components (VC) and the Bayesian Markov chain Monte Carlo (MCMC) analysis are two of the widely used linkage analysis approaches to mapping genes for complex quantitative traits. Both approaches can handle extended pedigrees and multiple markers and do not require a prespecified genetic model. In this study, we used simulated data to compare the performance of these two approaches with the traditional parametric linkage analysis. Using simulated data sets without linkage between a quantitative trait and the markers, we estimated a critical value for various test scores used in VC or MCMC and the location (LOC) score at a fixed level of significance (5%). These critical values were then used to determine the power for the three methods for simulated data sets with linkage. We found that both the VC and MCMC approaches worked well, compared with the LOC score, when there was only one gene underlying the quantitative trait; however, VC had higher power than the other methods in a simulation study of a complex phenotype influenced by more than one gene. We also compared two implementations of MCMC analysis, finding interpretation of results using the log of placement score was more accurate for linkage inference than the Bayes factor but required much more intensive simulation studies.

Linkage and association analyses identify a candidate region for apoB level on chromosome 4q32.3 in FCHL families

Familial combined hyperlipidemia (FCHL) is a complex trait leading to cardiovascular disease (CVD) risk. Elevated levels and size of apolipoprotein B (apoB) and low-density lipoprotein (LDL) are associated with FCHL, which is genetically heterogeneous and is likely caused by rare variants. We carried out a linkage-based genome scan of four large FCHL pedigrees for apoB level that is independent of LDL: apoB level that is adjusted for LDL level and size. Follow-up included SNP genotyping in the region with the strongest evidence of linkage. Several regions with the evidence of linkage in individual pedigrees support the rare variant model. Evidence of linkage was strongest on chromosome 4q, with multipoint analysis in one pedigree giving LOD = 3.1 with a parametric model, and a log Bayes Factor = 1.5 from a Bayesian oligogenic approach. Of the 293 SNPs spanning the implicated region on 4q, rs6829588 completely explained the evidence of linkage. This SNP accounted for 39% of the apoB phenotypic variance, with heterozygotes for this SNP having a trait value that was approximately 30% higher than that of the high-frequency homozygote, thus identifying and considerably refining a strong candidate region. These results illustrate the advantage of using large pedigrees in the search for rare variants: reduced genetic heterogeneity within single pedigrees coupled with the large number of individuals segregating otherwise-rare single variants leads to high power to implicate such variants.

A novel approach for haplotype-based association analysis using family data

Background

Haplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.

Results

In this paper, we propose a new haplotype-based association method for family data that is different from the TDT framework. Our approach (termed F_HapMiner) is based on our previous successful experiences on haplotype inference from pedigree data and haplotype-based association mapping. It first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on those founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. We have performed extensive simulations based on realistic assumptions to evaluate the effectiveness of the proposed approach by considering different factors such as allele frequency, linkage disequilibrium (LD) structure, disease model and sample size. Comparisons with single-locus and haplotype-based TDT methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local LD structures or allele/haplotype frequencies.

Conclusion

We present a novel haplotype-based association approach using family data. Experiment results demonstrate that it achieves significantly higher power than TDT-based approaches.

Contrasting identity-by-descent estimators, association studies, and linkage analyses using the Framingham Heart Study data

We explored the utility of population- and pedigree-based analyses using the Framingham Heart Study genome-wide 50 k single-nucleotide polymorphism marker data provided for Genetic Analysis Workshop 16. Our aims were: 1) to compare identity-by-descent sharing estimates from variable amounts of data; 2) to apply each of these estimates to a case-control association study designed to control for relatedness among samples; and 3) to contrast these results to those obtained using model-based and model-free linkage analysis methods.

Same or different? Insights into the etiology of phonological awareness and rapid naming

This work's objective was to offer additional insights into the psychological and genetic bases of reading ability and disability, and to evaluate the plausibility of a variety of psychological models of reading involving phonological awareness (PA) and rapid naming (RN), both hypothesized to be principal components in such models. In Study 1, 488 unselected families were assessed with measures of PA and RN to investigate familial aggregation and to obtain estimates of both the number and effect-magnitude of genetic loci involved in these traits' transmission. The results of the analyses from Study 1 indicated the presence of genetic effects in the etiology of individual differences for PA and RN and pointed to both the shared and unique sources of this genetic variance, which appeared to be exerted by multiple (3-6 for PA and 3-5 for RN) genes. These results were used in Study 2 to parameterize a simulation of 3000 families with quantitatively distributed PA and RN, so that the robustness and generalizability of the Study 1 findings could be evaluated. The findings of both studies were interpreted according to established theories of reading and our own understanding of the etiology of complex developmental disorders.

Combining information from linkage and association methods

Group 12 evaluated approaches to incorporate outside information or otherwise optimize traditional linkage and association analyses. The abundance of available data allowed exploration of identity-by-descent (IBD) estimation, score statistics, formal combination of linkage and association testing, significance estimation, and replication. We observed that IBD estimation can be optimized with a subset of marker data while estimation of inheritance vectors can provide both IBD estimates and a measure of their uncertainty. Score statistics incorporating covariates or combining association and linkage information performed at least as well as standard approaches while requiring less computation time. The formal combination of linkage and association methods may be fruitful, although the nature of the simulated data limited our conclusions. Estimation of significance may be improved through simulation, correction for cryptic relatedness, and the inclusion of prior information. Replication using real data provided consistent results, though the same was not true of simulated data replicates. Overall, we found that increasing the amount of available data limits analyses due to computational constraints and motivates the need to improve methods for the identification of complex-trait genes.

Genome scan of a nonword repetition phenotype in families with dyslexia: evidence for multiple loci

To understand the genetic architecture of dyslexia and identify the locations of genes involved, we performed linkage analyses in multigenerational families using a phonological memory phenotype--Nonword Repetition (NWR). A genome scan was first performed on 438 people from 51 families (DS-1) and linkage was assessed using variance components (VC), Bayesian oligogenic (BO), and parametric analyses. For replication, the genome scan and analyses were repeated on 693 people from 93 families (DS-2). For the combined set (DS-C), analyses were performed with all three methods in the regions that were identified in both samples. In DS-1, regions on chromosomes 4p, 6q, 12p, 17q, and 22q exceeded our initial threshold for linkage, with 17q providing a parametric LOD score of 3.2. Analysis with DS-2 confirmed the locations on chromosomes 4p and 12p. The strongest VC and BO signals in both samples were on chromosome 4p in DS-C, with a parametric multipoint LOD(max) of 2.36 for the 4p locus. Our linkage analyses of NWR in dyslexia provide suggestive and reproducible evidence for linkage to 4p12 and 12p in both samples, and significant evidence for linkage to 17q in one of the samples. These results warrant further studies of phonological memory and chromosomal regions identified here in other datasets.