Dosage transmission disequilibrium test (dTDT) for linkage and association detection.
paper
Primary
Public
- Authors
- Zhang, Zhehao; Wang, Jen-Chyong; Howells, William; Lin, Peng; Agrawal, Arpana; Edenberg, Howard J; Tischfield, Jay A; Schuckit, Marc A; Bierut, Laura J; Goate, Alison; Rice, John P
- Year
- 2013
- Journal
- PloS one
- PMID
- 23691058
- DOI
- 10.1371/journal.pone.0063526
- PMCID
- PMC3653954
Both linkage and association studies have been successfully applied to identify disease susceptibility genes with genetic markers such as microsatellites and Single Nucleotide Polymorphisms (SNPs). As one of the traditional family-based studies, the Transmission/Disequilibrium Test (TDT) measures the over-transmission of an allele in a trio from its heterozygous parents to the affected offspring and can be potentially useful to identify genetic determinants for complex disorders. However, there is reduced information when complete trio information is unavailable. In this study, we developed a novel approach to "infer" the transmission of SNPs by combining both the linkage and association data, which uses microsatellite markers from families informative for linkage together with SNP markers from the offspring who are genotyped for both linkage and a Genome-Wide Association Study (GWAS). We generalized the traditional TDT to process these inferred dosage probabilities, which we name as the dosage-TDT (dTDT). For evaluation purpose, we developed a simulation procedure to assess its operating characteristics. We applied the dTDT to the simulated data and documented the power of the dTDT under a number of different realistic scenarios. Finally, we applied our methods to a family study of alcohol dependence (COGA) and performed individual genotyping on complete families for the top signals. One SNP (rs4903712 on chromosome 14) remained significant after correcting for multiple testing Methods developed in this study can be adapted to other platforms and will have widespread applicability in genomic research when case-control GWAS data are collected in families with existing linkage data.
Demonstration of genotype inference within a family.(a) The observed data, which consist of genotypes at a series of microsatellite and SNP markers. A subset of microsatellite markers has been typed in all individuals except for founders (red), whereas both microsatellite and SNP markers have been typed in only a few selected common individuals (black). (b) Genotypes of dense SNPs for missing individuals are inferred by comparing the haplotypes they share with the common individuals.
Combined pedigree structure used in inference.The pedigree has indtotal individuals and (mrkMS+mrkSNP) markers. Most individuals have been genotyped on microsatellite markers. indcommon out of indtotal individuals are selected for SNP genotyping. Microsatellite and SNP markers are mapped based on their genetic positions. Missing SNPs of (indtotal - indcommon) will be inferred by MERLIN –infer.
Pedigree of one Map03 family (FAM_ID 20059).Common individuals (#1, 2, 9 and 13, from left to right) are genotyped on both microsatellite and SNP markers (circled in red). Box shadowed in upper left: AB, alcohol abuse; shadowed in upper left & right: AD, alcohol dependence (DSM III-R Diagnosis). Box shadowed in lower left: PROB, probable; shadowed in lower left & right: DEF, definite (Feighner Diagnosis).
Distribution of cleaned microsatellite and SNP markers on 23 chromosomes in EA families.These markers are used in the combined pedigrees for genotype inference. (a) distribution of microsatellite markers on 23 chromosomes in Map03 (blue) and Marshfield (red) (overlapped); (b) distribution of SNPs on 23 chromosomes in Map03 and Marshfield. Because the difference of SNPs numbers in these two datasets is trivial, we only display the distribution of SNPs in Map03.
–log10(P) distribution of nine disease models from the simulation results.Graphs from the top row to the bottom row represent the dominant, recessive and codominant models respectively, and from left to right represent the results with three different K values (0.01, 0.1 and 0.2). Each blue dot corresponds to a –log10(p-value) under that specific setting. Every graph is broken down into four bins with R = 0.5, 0.7, 0.9 and 1.0. Within each bin, the –log10(p-values) are ordered by descending f 11 and increasing f 12 & f 22.
Manhattan plot of –log10P from MH test across all 23 chromosomes for LinkageMS EA data.The dashed red line shows the genome-wide significant level –log10(5 × 10− 8) ≈ 7.3. SNP rs11583322 has given –log10P ≈ 8.1 above this level that lies in gene STK40.
QQ-plots of Case-Control, dTDT and MH tests.
Line chart of top 100 signals from Case-Control, dTDT and MH tests.(a) line chart ranked by -log10 (p-value) from dTDT and its corresponding -log10 (p-value) from the other two tests: Case-Control & MH test; (b) line chart ranked by -log10 (p-value) from MH test and its corresponding -log10 (p-value) from the other two tests: Case-Control and dTDT.
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| Name | Type |
|---|---|
| 5-HTTLPR | variant |
| affected case group local | cohort |
| affected child local | phenotype |
| affected child (proband) local | phenotype |
| affected children local | phenotype |
| affected offspring | phenotype |
| African American | cohort |
| Age-related disease local | phenotype |
| age-related macular degeneration | phenotype |
| alcohol | phenotype |
| alcohol dependence | phenotype |
| alcoholism | phenotype |
| allele M1 local | variant |
| allele M2 local | variant |
| allele M3 local | variant |
| allele M4 local | variant |
| Alzheimer's disease | phenotype |
| American adults | cohort |
| biologically unrelated family member(s) local | cohort |
| Bruker mass spectrometry workstation | drug |
| cardiovascular disease | phenotype |
| case-control sample | cohort |
| CIDR | cohort |
| CIDRSNP local | cohort |
| CIDRSNP data local | cohort |
| CIDRSNP GWAS local | cohort |
| CIDRSNP individuals local | cohort |
| CNV | variant |
| co-dominant disease model local | phenotype |
| Codominant model local | phenotype |
| Collaborative Study on the Genetics of Alcoholism (COGA) | cohort |
| Common disease model local | phenotype |
| complex traits | phenotype |
| CSMD2 | gene |
| cystic fibrosis | phenotype |
| D local | gene |
| D1 local | variant |
| D2 local | variant |
| D allele | variant |
| diabetes | phenotype |
| DiDi local | variant |
| DiDj local | variant |
| disease | phenotype |
| disease prevalence | phenotype |
| disease susceptibility | phenotype |
| Disease Susceptibility Alleles local | phenotype |
| dominant disease model local | phenotype |
| Dominant model local | phenotype |
| dTDT local | drug |
| EA families | cohort |
| EA participants | cohort |
| Edenberg et al’s study local | cohort |
| European ancestry | cohort |
| excessive alcohol consumption | phenotype |
| EXOC6B local | gene |
| extension primers local | drug |
| false positive results local | phenotype |
| FAM_ID 20059 local | cohort |
| families | cohort |
| family-based study local | cohort |
| family DNA local | cohort |
| FTO | gene |
| GCOM1 local | gene |
| genetic disorders | phenotype |
| genetic trait local | phenotype |
| Genotypic Markers local | variant |
| GWAS marker local | variant |
| HC | cohort |
| Huntington’s disease | phenotype |
| hyper-cholesterolaemia local | phenotype |
| iPLEX local | drug |
| late-onset local | phenotype |
| linkage marker local | variant |
| LinkageMS local | cohort |
| LinkageMS data local | cohort |
| LinkageMS EA families local | cohort |
| LinkageMS EA group local | cohort |
| LZTS2 local | gene |
| Map03 local | cohort |
| Map03MS local | cohort |
| Marshfield local | cohort |
| MarshfieldMS local | cohort |
| Merlin | drug |
| MH test local | drug |
| microsatellite | variant |
| microsatellite marker M local | variant |
| microsatellite markers local | drug |
| mutual individuals local | cohort |
| Ncam2 | gene |
| non-genetic risk factor local | phenotype |
| Oct4 | gene |
| parents | cohort |
| PCR primers | drug |
| PDZD7 local | gene |
| Plink | drug |
| population | cohort |
| population-based studies local | cohort |
| population stratification | phenotype |
| PPEF1 local | gene |
| Quantitative trait locus local | phenotype |
| rare disease model local | phenotype |
| Rare disease model local | phenotype |
| recessive disease model local | phenotype |
| Recessive model local | phenotype |
| rs10511260 local | variant |
| rs11583322 local | variant |
| rs4903712 local | variant |
| S local | gene |
| S1 local | variant |
| S1S1 local | variant |
| S1S2 local | variant |
| S2 local | variant |
| S2S2 local | variant |
| Sequenom MassARRAY Assay Designer software v3.1.2.2 local | drug |
| Sequenom MassArray technology local | drug |
| Sequenom SpectroTYPER software v3.4 local | drug |
| silicon SpectroChip local | drug |
| single-nucleotide polymorphisms | variant |
| SNP | cohort |
| SNP data local | variant |
| SNP genotyping local | drug |
| STK40 local | gene |
| top SNPs | cohort |
| Transmission/Disequilibrium Test local | drug |
| Trio Pedigrees local | cohort |
| trios | cohort |
| type 2 diabetes | phenotype |
| type I errors local | phenotype |
| Wave I & II families local | cohort |
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