Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project.

Alzheimer's Disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. Here, we investigated the association between AD and both common variants and aggregates of rare coding and noncoding variants in 13,371 individuals of diverse ancestry with whole genome sequence (WGS) data. Pooled-population analyses identified genetic variants in or near APOE, BIN1, and LINC00320 significantly associated with AD (p < 5×10-8). Population-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and noncoding variants in this region. Finally, we observed suggestive associations (p < 5×10-5) of aggregates of rare coding rare variants in ABCA7 among non-Hispanic Whites (p=5.4×10-6), and rare noncoding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). Complementary pooled-population and population-specific analyses offered unique insights into the genetic architecture of AD.

Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations.

Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown. Here, we conduct genome-wide analysis of AD in multiplex CH families from the Alzheimer Disease Sequencing Project (ADSP). We developed, validated, and applied an implementation of a logistic mixed model for admixture mapping with binary traits that leverages genetic ancestry to identify ancestry-of-origin loci contributing to AD. We identified three loci on chromosome 13q33.3 associated with reduced risk of AD, where associations were driven by Native American (NAM) ancestry. This AD admixture mapping signal spans the FAM155A, ABHD13, TNFSF13B, LIG4, and MYO16 genes and was supported by evidence for association in an independent sample from the Alzheimer's Genetics in Argentina-Alzheimer Argentina consortium (AGA-ALZAR) study with considerable NAM ancestry. We also provide evidence of NAM haplotypes and key variants within 13q33.3 that segregate with AD in the ADSP whole-genome sequencing data. Interestingly, the widely used genome-wide association study approach failed to identify associations in this region. Our findings underscore the potential of leveraging genetic ancestry diversity in recently admixed populations to improve genetic mapping, in this case for AD-relevant loci.

The Advisory Group on Risk Evidence Education for Dementia: Multidisciplinary and Open to All.

The brain changes of Alzheimer's disease and other degenerative dementias begin long before cognitive dysfunction develops, and in people with subtle cognitive complaints, clinicians often struggle to predict who will develop dementia. The public increasingly sees benefits to accessing dementia risk evidence (DRE) such as biomarkers, predictive algorithms, and genetic information, particularly as this information moves from research to demonstrated usefulness in guiding diagnosis and clinical management. For example, the knowledge that one has high levels of amyloid in the brain may lead one to seek amyloid reducing medications, plan for disability, or engage in health promoting behaviors to fight cognitive decline. Researchers often hesitate to share DRE data, either because they are insufficiently validated or reliable for use in individuals, or there are concerns about assuring responsible use and ensuring adequate understanding of potential problems when one's biomarker status is known. Concerns include warning people receiving DRE about situations in which they might be compelled to disclose their risk status potentially leading to discrimination or stigma. The Advisory Group on Risk Evidence Education for Dementia (AGREEDementia) welcomes all concerned with how best to share and use DRE. Supporting understanding in clinicians, stakeholders, and people with or at risk for dementia and clearly delineating risks, benefits, and gaps in knowledge is vital. This brief overview describes elements that made this group effective as a model for other health conditions where there is interest in unfettered collaboration to discuss diagnostic uncertainty and the appropriate use and communication of health-related risk information.

Local ancestry at APOE modifies Alzheimer's disease risk in Caribbean Hispanics.

INTRODUCTION: Although the relationship between APOE and Alzheimer's disease (AD) is well established in populations of European descent, the effects of APOE and ancestry on AD risk in diverse populations is not well understood. METHODS: Logistic mixed model regression and survival analyses were performed in a sample of 3067 Caribbean Hispanics and 3028 individuals of European descent to assess the effects of APOE genotype, local ancestry, and genome-wide ancestry on AD risk and age at onset. RESULTS: Among the Caribbean Hispanics, individuals with African-derived ancestry at APOE had 39% lower odds of AD than individuals with European-derived APOE, after adjusting for APOE genotype, age, and genome-wide ancestry. While APOE E2 and E4 effects on AD risk and age at onset were significant in the Caribbean Hispanics, they were substantially attenuated compared with those in European ancestry individuals. DISCUSSION: These results suggest that additional genetic variation in the APOE region influences AD risk beyond APOE E2/E3/E4.

Alternative splicing in a presenilin 2 variant associated with Alzheimer disease.

OBJECTIVE: Autosomal-dominant familial Alzheimer disease (AD) is caused by by variants in presenilin 1 (PSEN1), presenilin 2 (PSEN2), and amyloid precursor protein (APP). Previously, we reported a rare PSEN2 frameshift variant in an early-onset AD case (PSEN2 p.K115Efs*11). In this study, we characterize a second family with the same variant and analyze cellular transcripts from both patient fibroblasts and brain lysates. METHODS: We combined genomic, neuropathological, clinical, and molecular techniques to characterize the PSEN2 K115Efs*11 variant in two families. RESULTS: Neuropathological and clinical evaluation confirmed the AD diagnosis in two individuals carrying the PSEN2 K115Efs*11 variant. A truncated transcript from the variant allele is detectable in patient fibroblasts while levels of wild-type PSEN2 transcript and protein are reduced compared to controls. Functional studies to assess biological consequences of the variant demonstrated that PSEN2 K115Efs*11 fibroblasts secrete less Aß 1-40 compared to controls, indicating abnormal γ-secretase activity. Analysis of PSEN2 transcript levels in brain tissue revealed alternatively spliced PSEN2 products in patient brain as well as in sporadic AD and age-matched control brain. INTERPRETATION: These data suggest that PSEN2 K115Efs*11 is a likely pathogenic variant associated with AD. We uncovered novel PSEN2 alternative transcripts in addition to previously reported PSEN2 splice isoforms associated with sporadic AD. In the context of a frameshift, these alternative transcripts return to the canonical reading frame with potential to generate deleterious protein products. Our findings suggest novel potential mechanisms by which PSEN variants may influence AD pathogenesis, highlighting the complexity underlying genetic contribution to disease risk.

Association score testing for rare variants and binary traits in family data with shared controls.

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project.

The Alzheimer's Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed "consensus calling," to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.

Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees.

Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R 2 for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R 2 criterion; and (5) R 2 is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.

Replication of a rare risk haplotype on 1p36.33 for autism spectrum disorder.

Hundreds of genes have been implicated in autism spectrum disorders (ASDs). In genetically heterogeneous conditions, large families with multiple affected individuals provide strong evidence implicating a rare variant, and replication of the same variant in multiple families is unusual. We previously published linkage analyses and follow-up exome sequencing in seven large families with ASDs, implicating 14 rare exome variants. These included rs200195897, which was transmitted to four affected individuals in one family. We attempted replication of those variants in the MSSNG database. MSSNG is a unique resource for replication of ASD risk loci, containing whole genome sequence (WGS) on thousands of individuals diagnosed with ASDs and family members. For each exome variant, we obtained all carriers and their relatives in MSSNG, using a TDT test to quantify evidence for transmission and association. We replicated the transmission of rs200195897 to four affected individuals in three additional families. rs200195897 was also present in three singleton affected individuals, and no unaffected individuals other than transmitting parents. We identified two additional rare variants (rs566472488 and rs185038034) transmitted with rs200195897 on 1p36.33. Sanger sequencing confirmed the presence of these variants in the original family segregating rs200195897. To our knowledge, this is the first example of a rare haplotype being transmitted with ASD in multiple families. The candidate risk variants include a missense mutation in SAMD11, an intronic variant in NOC2L, and a regulatory region variant close to both genes. NOC2L is a transcription repressor, and several genes involved in transcription regulation have been previously associated with ASDs.

Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP.

Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.

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.

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 GIGI's 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.

Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals.

Genetic Analysis Workshop 19 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence and gene expression data from a pedigree-based sample, as well as whole-exome sequence data from a large cohort of unrelated individuals. In this article we present an overview of the data sets, the GAW experience, and summaries of the contributions arranged into nine methodological themes.

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

BACKGROUND: In 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. METHODS: We 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. RESULTS: Different 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. CONCLUSIONS: Our 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.

Estimating relationships between phenotypes and subjects drawn from admixed families.

BACKGROUND: Estimating 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. RESULTS: We 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. CONCLUSIONS: Admixed 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.

Early event-related potentials to emotional faces differ for adults with autism spectrum disorder and by serotonin transporter genotype.

OBJECTIVE: To test differences in neural sensitivity to facial expressions, including expressions with open versus closed mouths, exhibited by (1) adults with autism spectrum disorder (ASD) compared to neurotypical adults, and by (2) short versus long serotonin transporter allele (SLC6A4) carriers. METHODS: Event related potentials (ERPs) to happy, fearful, and neutral expressions were collected from neurotypical adults (n=25) and adults with ASD (n=27)-of whom 32 had short and 13 had homozygous long SLC6A4 alleles. RESULTS: In the neurotypical group, we confirmed that the N170, VPP and EPN, but not the P1, were influenced by emotional expressions, and determined the EPN was the earliest component modulated by open mouth. Compared to the neurotypical group, individuals with ASD exhibited differences in EPN amplitude in response to open versus closed mouths and in hemispheric distribution. Across groups, short serotonin transporter allele carriers had reduced P1 amplitude compared to long allele carriers. CONCLUSIONS: Individuals with ASD exhibited a different pattern of neural response when encoding and recognizing facial expressions at the EPN component. Across groups, SLC6A4 allele type modulated early sensory attention at the P1. SIGNIFICANCE: These results provide insight into the nature of early responses to emotional information according to genetic variation and clinical condition.

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.

Family-based approaches: design, imputation, analysis, and beyond.

Participants in the family-based analysis group at Genetic Analysis Workshop 19 addressed diverse topics, all of which used the family data. Topics addressed included questions of study design and data quality control (QC), genotype imputation to augment available sequence data, and linkage and/or association analyses. Results show that pedigree-based tests that are sensitive to genotype error may be useful for QC. Imputation quality improved with inclusion of small amounts of pedigree information used to phase the data in evaluation of 5 commonly used approaches for imputation in samples of (typically) unrelated subjects. It improved still further when pedigree-based imputation using larger pedigrees was also added. An important distinction was made between methods that do versus do not make use of Mendelian transmission in pedigrees, because this serves as a key difference between underlying models and assumptions. Methods that model relatedness generally had higher power in association testing than did analyses that carry out testing in the presence of a transmission model, but this may reflect details of implementation and/or ability of more general methods to jointly include data from larger pedigrees. In either case, for single nucleotide polymorphism-set approaches, weights that incorporate information on functional effects may be more useful than those that are based only on allele frequencies. The overall results demonstrate that family data continue to provide important information in the search for trait loci.