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

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.Fil: Horimoto, Andrea R.V.R.. University of Washington; Estados UnidosFil: Boyken, Lisa A.. University of Washington; Estados UnidosFil: Blue, Elizabeth E.. University of Washington; Estados Unidos. Brotman Baty Institute for Precision Medicine; Estados UnidosFil: Grinde, Kelsey E.. University of Washington; Estados Unidos. Macalester College; Estados UnidosFil: Nafikov, Rafael A.. University of Washington; Estados UnidosFil: Sohi, Harkirat K.. University of Washington; Estados UnidosFil: Nato, Alejandro Q.. University of Washington; Estados Unidos. Marshall University; Estados UnidosFil: Bis, Joshua C.. University of Washington; Estados UnidosFil: Brusco, Luis Ignacio. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Morelli, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ramirez, Alfredo Jose. University Of Cologne; Alemania. Universitat Bonn; Alemania. German Center for Neurodegenerative Diseases; Alemania. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Dalmasso, Maria Carolina. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. University Of Cologne; AlemaniaFil: Temple, Seth. University of Washington; Estados UnidosFil: Satizabal, Claudia. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . University of Texas at San Antonio; Estados UnidosFil: Browning, Sharon R.. University of Washington; Estados UnidosFil: Seshadri, Sudha. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . University of Texas at San Antonio; Estados UnidosFil: Wijsman, Ellen M.. University of Washington; Estados UnidosFil: Thornton, Timothy A.. University of Washington; Estados Unido

Key Variants via the Alzheimer\u27s Disease Sequencing Project Whole Genome Sequence Data

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer\u27s 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\u27s 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

Genetic Analysis Workshop 15: gene expression analysis and approaches to detecting multiple functional loci

National Institutes of Health (AR44422, N01-AR-7-2232, 5R01-HL049609-14, IR01-AG021917-01A1); Genome Canada and Associations AFP; Polyarctique-Groupe Taitbout; Rhumatisme et Travail; Arthritis Research Campaign; Unversity of Minnesota; Minnesota Supercomputing Institute; National Institute of General Medical Sciences (R01 GM31575

Evidence for involvement of GNB1L in autism

Structural variations in the chromosome 22q11.2 region mediated by nonallelic homologous recombination result in 22q11.2 deletion (del22q11.2) and 22q11.2 duplication (dup22q11.2) syndromes. The majority of del22q11.2 cases have facial and cardiac malformations, immunologic impairments, specific cognitive profile and increased risk for schizophrenia and autism spectrum disorders (ASDs). The phenotype of dup22q11.2 is frequently without physical features but includes the spectrum of neurocognitive abnormalities. Although there is substantial evidence that haploinsufficiency for TBX1 plays a role in the physical features of del22q11.2, it is not known which gene(s) in the critical 1.5 Mb region are responsible for the observed spectrum of behavioral phenotypes. We identified an individual with a balanced translocation 46,XY,t(1;22)(p36.1;q11.2) and a behavioral phenotype characterized by cognitive impairment, autism, and schizophrenia in the absence of congenital malformations. Using somatic cell hybrids and comparative genomic hybridization (CGH) we mapped the chromosome-22 breakpoint within intron 7 of the GNB1L gene. Copy number evaluations and direct DNA sequencing of GNB1L in 271 schizophrenia and 513 autism cases revealed dup22q11.2 in two families with autism and private GNB1L missense variants in conserved residues in three families (P = 0.036). The identified missense variants affect residues in the WD40 repeat domains and are predicted to have deleterious effects on the protein. Prior studies provided evidence that GNB1L may have a role in schizophrenia. Our findings support involvement of GNB1L in ASDs as well. © 2011 Wiley Periodicals, Inc

Replication of CNTNAP2 association with nonword repetition and support for FOXP2 association with timed reading and motor activities in a dyslexia family sample

Two functionally related genes, FOXP2 and CNTNAP2, influence language abilities in families with rare syndromic and common nonsyndromic forms of impaired language, respectively. We investigated whether these genes are associated with component phenotypes of dyslexia and measures of sequential motor ability. Quantitative transmission disequilibrium testing (QTDT) and linear association modeling were used to evaluate associations with measures of phonological memory (nonword repetition, NWR), expressive language (sentence repetition), reading (real word reading efficiency, RWRE; word attack, WATT), and timed sequential motor activities (rapid alternating place of articulation, RAPA; finger succession in the dominant hand, FS-D) in 188 family trios with a child with dyslexia. Consistent with a prior study of language impairment, QTDT in dyslexia showed evidence of CNTNAP2 single nucleotide polymorphism (SNP) association with NWR. For FOXP2, we provide the first evidence for SNP association with component phenotypes of dyslexia, specifically NWR and RWRE but not WATT. In addition, FOXP2 SNP associations with both RAPA and FS-D were observed. Our results confirm the role of CNTNAP2 in NWR in a dyslexia sample and motivate new questions about the effects of FOXP2 in neurodevelopmental disorders

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders