Alejandro Q. Nato

The orphan GPCR, Gpr161, regulates the retinoic acid and canonical Wnt pathways during neurulation.

The vacuolated lens (vl) mouse mutation arose on the C3H/HeSnJ background and results in lethality, neural tube defects (NTDs) and cataracts. The vl phenotypes are due to a deletion/frameshift mutation in the orphan GPCR, Gpr161. A recent study using a null allele demonstrated that Gpr161 functions in primary cilia and represses the Shh pathway. We show the hypomorphic Gpr161(vl) allele does not severely affect the Shh pathway. To identify additional pathways regulated by Gpr161 during neurulation, we took advantage of naturally occurring genetic variation in the mouse. Previously Gpr161(vl-C3H) was crossed to different inbred backgrounds including MOLF/EiJ and the Gpr161(vl) mutant phenotypes were rescued. Five modifiers were mapped (Modvl: Modifier of vl) including Modvl5(MOLF). In this study we demonstrate the Modvl5(MOLF) congenic rescues the Gpr161(vl)-associated lethality and NTDs but not cataracts. Bioinformatics determined the transcription factor, Cdx1, is the only annotated gene within the Modvl5 95% CI co-expressed with Gpr161 during neurulation and not expressed in the eye. Using Cdx1 as an entry point, we identified the retinoid acid (RA) and canonical Wnt pathways as downstream targets of Gpr161. QRT-PCR, ISH and IHC determined that expression of RA and Wnt genes are down-regulated in Gpr161(vl/vl) but rescued by the Modvl5(MOLF) congenic during neurulation. Intraperitoneal RA injection restores expression of canonical Wnt markers and rescues Gpr161(vl/vl) NTDs. These results establish the RA and canonical Wnt as pathways downstream of Gpr161 during neurulation, and suggest that Modvl5(MOLF) bypasses the Gpr161(vl) mutation by restoring the activity of these pathways.

Genetic candidate variants in two multigenerational families with childhood apraxia of speech

Childhood apraxia of speech (CAS) is a severe and socially debilitating form of speech sound disorder with suspected genetic involvement, but the genetic etiology is not yet well understood. Very few known or putative causal genes have been identified to date, e.g., FOXP2 and BCL11A. Building a knowledge base of the genetic etiology of CAS will make it possible to identify infants at genetic risk and motivate the development of effective very early intervention programs. We investigated the genetic etiology of CAS in two large multigenerational families with familial CAS. Complementary genomic methods included Markov chain Monte Carlo linkage analysis, copy-number analysis, identity-by-descent sharing, and exome sequencing with variant filtering. No overlaps in regions with positive evidence of linkage between the two families were found. In one family, linkage analysis detected two chromosomal regions of interest, 5p15.1-p14.1, and 17p13.1-q11.1, inherited separately from the two founders. Single-point linkage analysis of selected variants identified CDH18 as a primary gene of interest and additionally, MYO10, NIPBL, GLP2R, NCOR1, FLCN, SMCR8, NEK8, and ANKRD12, possibly with additive effects. Linkage analysis in the second family detected five regions with LOD scores approaching the highest values possible in the family. A gene of interest was C4orf21 (ZGRF1) on 4q25-q28.2. Evidence for previously described causal copy-number variations and validated or suspected genes was not found. Results are consistent with a heterogeneous CAS etiology, as is expected in many neurogenic disorders. Future studies will investigate genome variants in these and other families with CAS.

PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers

MOTIVATION Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility. RESULTS We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets. AVAILABILITY AND IMPLEMENTATION PBAP scripts may be downloaded from http://faculty.washington.edu/wijsman/software.shtml. CONTACT wijsman@uw.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Mapping genes with longitudinal phenotypes via Bayesian posterior probabilities

Most association studies focus on disease risk, with less attention paid to disease progression or severity. These phenotypes require longitudinal data. This paper presents a new method for analyzing longitudinal data to map genes in both population-based and family-based studies. Using simulated systolic blood pressure measurements obtained from Genetic Analysis Workshop 18, we cluster the phenotype data into trajectory subgroups. We then use the Bayesian posterior probability of being in the high subgroup as a quantitative trait in an association analysis with genotype data. This method maintains high power (>80%) in locating genes known to affect the simulated phenotype for most specified significance levels (α). We believe that this method can be useful to aid in the discovery of genes that affect severity or progression of disease.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Estimating relationships between phenotypes and subjects drawn from admixed families.

BackgroundEstimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model.ResultsWe found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them.ConclusionsAdmixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.

Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer’s Disease Sequencing Project

Background/Aims: The Alzheimer’s Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer’s disease (AD). Variants within genes known to cause dementias other than AD have previously been associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP. Methods: We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as “pathogenic” in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations. Results/Conclusions: Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.

Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data

BackgroundIn the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.MethodsWe compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.ResultsDifferent combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.ConclusionsOur results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.

GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data

Summary Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGIs input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses. Availability and and implementation GIGI-Quick is open source and publicly available via: https://cse-git.qcri.org/Imputation/GIGI-Quick. Contact msaad@hbku.edu.qa. Supplementary information Supplementary data are available at Bioinformatics online.

GENOMEWIDE LINKAGE ANALYSIS IDENTIFIES NOVEL CANDIDATE GENES FOR ALZHEIMER’S DISEASE

ages 65-79 years. At original enrollment into the trials, women had been randomly assigned to 0.625 mg/d conjugated equine estrogens (CEE) for those with prior hysterectomy (mean 7.1 years), CEE with 2.5 mg/day medroxyprogesterone acetate for thosewith an intact uterus (mean 5.4 years), or matching placebos. Cognitive assessments included telephone evaluations of global cognition, verbal memory, working memory, verbal memory, and executive function. Results: Hormone therapy, when prescribed to women aged 50-54 years, had no significant long-term post-treatment effects on cognitive function or change in cognitive status over time. When prescribed to older women, it was associated with long term mean (SE) relative decrements (standard deviation units) in global cognitive function of 0.081 (0.029) SD, working memory of 0.070 (0.025) SD, and executive function of 0.054 (0.023) SD, all p<0.05. These decrements were relatively stable over time. Findings did not vary depending on the hormone therapy regimen or pre-study use. Although mean intervention effects were small, the largest were comparable in magnitude to those seen during the trial’s active intervention phase. Conclusions: CEE-based hormone therapy delivered near the time of menopause provides neither cognitive benefit nor detriment. If administered in older women, it results in small decrements in several cognitive domains that remain for many years.