Robust relationship inference in genome-wide association studies

Quantitating and dating recent gene flow between European and East Asian populations

Historical records indicate that extensive cultural, commercial and technological interaction occurred between European and Asian populations. What have been the biological consequences of these contacts in terms of gene flow? We systematically estimated gene flow between Eurasian groups using genome-wide polymorphisms from 34 populations representing Europeans, East Asians, and Central/South Asians. We identified recent gene flow between Europeans and Asians in most populations we studied, including East Asians and Northwestern Europeans, which are normally considered to be non-admixed populations. In addition we quantitatively estimated the extent of this gene flow using two statistical approaches, and dated admixture events based on admixture linkage disequilibrium. Our results indicate that most genetic admixtures occurred between 2,400 and 310 years ago and show the admixture proportions to be highly correlated with geographic locations, with the highest admixture proportions observed in Central Asia and the lowest in East Asia and Northwestern Europe. Interestingly, we observed a North-to-South decline of European gene flow in East Asians, suggesting a northern path of European gene flow diffusing into East Asian populations. Our findings contribute to an improved understanding of the history of human migration and the evolutionary mechanisms that have shaped the genetic structure of populations in Eurasia.

Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions, finding major pathways contributing to risk, with some loci shared across immune disorders. To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new T1D-associated regions (P < 5 × 10(-8)). A comparative analysis with 15 immune diseases showed that T1D is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new T1D risk loci. Using a Bayesian approach, we defined credible sets for the T1D-associated SNPs. The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34(+) stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.

Unravelling the hidden ancestry of American admixed populations

The movement of people into the Americas has brought different populations into contact, and contemporary American genomes are the product of a range of complex admixture events. Here we apply a haplotype-based ancestry identification approach to a large set of genome-wide SNP data from a variety of American, European and African populations to determine the contributions of different ancestral populations to the Americas. Our results provide a fine-scale characterization of the source populations, identify a series of novel, previously unreported contributions from Africa and Europe and highlight geohistorical structure in the ancestry of American admixed populations.

Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.

Genome-wide association study and meta-analysis identify ISL1 as genome-wide significant susceptibility gene for bladder exstrophy

The bladder exstrophy-epispadias complex (BEEC) represents the severe end of the uro-rectal malformation spectrum, and is thought to result from aberrant embryonic morphogenesis of the cloacal membrane and the urorectal septum. The most common form of BEEC is isolated classic bladder exstrophy (CBE). To identify susceptibility loci for CBE, we performed a genome-wide association study (GWAS) of 110 CBE patients and 1,177 controls of European origin. Here, an association was found with a region of approximately 220kb on chromosome 5q11.1. This region harbors the ISL1 (ISL LIM homeobox 1) gene. Multiple markers in this region showed evidence for association with CBE, including 84 markers with genome-wide significance. We then performed a meta-analysis using data from a previous GWAS by our group of 98 CBE patients and 526 controls of European origin. This meta-analysis also implicated the 5q11.1 locus in CBE risk. A total of 138 markers at this locus reached genome-wide significance in the meta-analysis, and the most significant marker (rs9291768) achieved a P value of 2.13 × 10-12. No other locus in the meta-analysis achieved genome-wide significance. We then performed murine expression analyses to follow up this finding. Here, Isl1 expression was detected in the genital region within the critical time frame for human CBE development. Genital regions with Isl1 expression included the peri-cloacal mesenchyme and the urorectal septum. The present study identified the first genome-wide significant locus for CBE at chromosomal region 5q11.1, and provides strong evidence for the hypothesis that ISL1 is the responsible candidate gene in this region.

Challenges and opportunities in genetic improvement of local livestock breeds

Sufficient genetic variation in livestock populations is necessary both for adaptation to future changes in climate and consumer demand, and for continual genetic improvement of economically important traits. Unfortunately, the current trend is for reduced genetic variation, both within and across breeds. The latter occurs primarily through the loss of small, local breeds. Inferior production is a key driver for loss of small breeds, as they are replaced by high-output international transboundary breeds. Selection to improve productivity of small local breeds is therefore critical for their long term survival. The objective of this paper is to review the technology options available for the genetic improvement of small local breeds and discuss their feasibility. Most technologies have been developed for the high-input breeds and consequently are more favorably applied in that context. Nevertheless, their application in local breeds is not precluded and can yield significant benefits, especially when multiple technologies are applied in close collaboration with farmers and breeders. Breeding strategies that require cooperation and centralized decision-making, such as optimal contribution selection, may in fact be more easily implemented in small breeds.

Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways

Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment. We report the results of a moderate-scale sequencing study aimed at increasing the number of genes known to contribute to predisposition for ALS. We performed whole-exome sequencing of 2869 ALS patients and 6405 controls. Several known ALS genes were found to be associated, and TBK1 (the gene encoding TANK-binding kinase 1) was identified as an ALS gene. TBK1 is known to bind to and phosphorylate a number of proteins involved in innate immunity and autophagy, including optineurin (OPTN) and p62 (SQSTM1/sequestosome), both of which have also been implicated in ALS. These observations reveal a key role of the autophagic pathway in ALS and suggest specific targets for therapeutic intervention.

Mutations in SPECC1L, encoding sperm antigen with calponin homology and coiled-coil domains 1-like, are found in some cases of autosomal dominant Opitz G/BBB syndrome

BACKGROUND

Opitz G/BBB syndrome is a heterogeneous disorder characterised by variable expression of midline defects including cleft lip and palate, hypertelorism, laryngealtracheoesophageal anomalies, congenital heart defects, and hypospadias. The X-linked form of the condition has been associated with mutations in the MID1 gene on Xp22. The autosomal dominant form has been linked to chromosome 22q11.2, although the causative gene has yet to be elucidated.

METHODS AND RESULTS

In this study, we performed whole exome sequencing on DNA samples from a three-generation family with characteristics of Opitz G/BBB syndrome with negative MID1 sequencing. We identified a heterozygous missense mutation c.1189A>C (p.Thr397Pro) in SPECC1L, located at chromosome 22q11.23. Mutation screening of an additional 19 patients with features of autosomal dominant Opitz G/BBB syndrome identified a c.3247G>A (p.Gly1083Ser) mutation segregating with the phenotype in another three-generation family.

CONCLUSIONS

Previously, SPECC1L was shown to be required for proper facial morphogenesis with disruptions identified in two patients with oblique facial clefts. Collectively, these data demonstrate that SPECC1L mutations can cause syndromic forms of facial clefting including some cases of autosomal dominant Opitz G/BBB syndrome and support the original linkage to chromosome 22q11.2.

Immunogenetic influences on acquisition of HIV-1 infection: consensus findings from two African cohorts point to an enhancer element in IL19 (1q32.2)

Numerous reports have suggested that immunogenetic factors may influence HIV-1 acquisition, yet replicated findings that translate between study cohorts remain elusive. Our work aimed to test several hypotheses about genetic variants within the IL10-IL24 gene cluster that encodes interleukin (IL)-10, IL-19, IL-20, and IL-24. In aggregated data from 515 Rwandans and 762 Zambians with up to 12 years of follow-up, 190 single nucleotide polymorphisms (SNPs) passed quality control procedures. When HIV-1-exposed seronegative subjects (n = 486) were compared with newly seroconverted individuals (n = 313) and seroprevalent subjects (n = 478) who were already infected at enrollment, rs12407485 (G>A) in IL19 showed a robust association signal in adjusted logistic regression models (odds ratio = 0.64, P = 1.7 × 10⁻⁴, and q = 0.033). Sensitivity analyses demonstrated that (i) results from both cohorts and subgroups within each cohort were highly consistent; (ii) verification of HIV-1 infection status after enrollment was critical; and (iii) supporting evidence was readily obtained from Cox proportional hazards models. Data from public databases indicate that rs12407485 is part of an enhancer element for three transcription factors. Overall, these findings suggest that molecular features at the IL19 locus may modestly alter the establishment of HIV-1 infection.

Conservation of trans-acting circuitry during mammalian regulatory evolution

The basic body plan and major physiological axes have been highly conserved during mammalian evolution, yet only a small fraction of the human genome sequence appears to be subject to evolutionary constraint. To quantify cis- versus trans-acting contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ∼8.6 million transcription factor (TF) occupancy sites at nucleotide resolution. Here we show that mouse TF footprints conjointly encode a regulatory lexicon that is ∼95% similar with that derived from human TF footprints. However, only ∼20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Furthermore, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results indicate that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry, enabling and potentiating cis-regulatory plasticity.

Mouse genomic footprinting reveals conservation of transcription factor (TF) recognition repertoires and trans-regulatory circuitry despite massive turnover of DNA elements that contact TFs in vivo.

Having generated genomic DNase I footprinting data of the mouse genome across 25 cell and tissue types, these authors use these data to quantify cis-versus-trans regulatory contributions to mammalian regulatory evolution. They describe more than 600 motifs that collectively are over 95% similar to that recognized in vivo by human transcription factors (TFs). Despite substantial turnover of the cis-regulatory landscape around each TF gene, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Conservation between mouse and human TF regulatory networks is particularly similar at the highest organization level. The work was performed as part of the mouse ENCODE project.

A parametric approach to kinship hypothesis testing using identity-by-descent parameters

There is a large number of applications where family relationships need to be determined from DNA data. In forensic science, competing ideas are in general verbally formulated as the two hypotheses of a test. For the most common paternity case, the null hypothesis states that the alleged father is the true father against the alternative hypothesis that the father is an unrelated man. A likelihood ratio is calculated to summarize the evidence. We propose an alternative framework whereby a model and the hypotheses are formulated in terms of parameters representing identity-by-descent probabilities. There are several advantages to this approach. Firstly, the alternative hypothesis can be completely general. Specifically, the alternative does not need to specify an unrelated man. Secondly, the parametric formulation corresponds to the approach used in most other applications of statistical hypothesis testing and so there is a large theory of classical statistics that can be applied. Theoretical properties of the test statistic under the null hypothesis are studied. An extension to trios of individuals has been carried out. The methods are exemplified using simulations and a real dataset of 27 Spanish Romani individuals.

Imputation and quality control steps for combining multiple genome-wide datasets

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R² (estimated correlation between the imputed and true genotypes), and the relationship between allelic R² and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

High-resolution linkage map and chromosome-scale genome assembly for cassava (Manihot esculenta Crantz) from 10 populations

Cassava (Manihot esculenta Crantz) is a major staple crop in Africa, Asia, and South America, and its starchy roots provide nourishment for 800 million people worldwide. Although native to South America, cassava was brought to Africa 400-500 years ago and is now widely cultivated across sub-Saharan Africa, but it is subject to biotic and abiotic stresses. To assist in the rapid identification of markers for pathogen resistance and crop traits, and to accelerate breeding programs, we generated a framework map for M. esculenta Crantz from reduced representation sequencing [genotyping-by-sequencing (GBS)]. The composite 2412-cM map integrates 10 biparental maps (comprising 3480 meioses) and organizes 22,403 genetic markers on 18 chromosomes, in agreement with the observed karyotype. We used the map to anchor 71.9% of the draft genome assembly and 90.7% of the predicted protein-coding genes. The chromosome-anchored genome sequence will be useful for breeding improvement by assisting in the rapid identification of markers linked to important traits, and in providing a framework for genomic selection-enhanced breeding of this important crop.

T2DM GWAS in the Lebanese population confirms the role of TCF7L2 and CDKAL1 in disease susceptibility

Genome-wide association studies (GWAS) of multiple populations with distinctive genetic and lifestyle backgrounds are crucial to the understanding of Type 2 Diabetes Mellitus (T2DM) pathophysiology. We report a GWAS on the genetic basis of T2DM in a 3,286 Lebanese participants. More than 5,000,000 SNPs were directly genotyped or imputed using the 1000 Genomes Project reference panels. We identify genome-wide significant variants in two loci CDKAL1 and TCF7L2, independent of sex, age and BMI, with leading variants rs7766070 (OR = 1.39, P = 4.77 × 10(-9)) and rs34872471 (OR = 1.35, P = 1.01 × 10(-8)) respectively. The current study is the first GWAS to find genomic regions implicated in T2DM in the Lebanese population. The results support a central role of CDKAL1 and TCF7L2 in T2DM susceptibility in Southwest Asian populations and provide a plausible component for understanding molecular mechanisms involved in the disease.

Fine mapping and functional studies of risk variants for type 1 diabetes at chromosome 16p13.13

Single nucleotide polymorphisms (SNPs) located in the chromosomal region 16p13.13 have been previously associated with risk for several autoimmune diseases, including type 1 diabetes. To identify and localize specific risk variants for type 1 diabetes in this region and understand the mechanism of their action, we resequenced a 455-kb region in type 1 diabetic patients and unaffected control subjects, identifying 93 novel variants. A panel of 939 SNPs that included 46 of these novel variants was genotyped in 3,070 multiplex families with type 1 diabetes. Forty-eight SNPs, all located in CLEC16A, provided a statistically significant association (P < 5.32 × 10(-5)) with disease, with rs34306440 being most significantly associated (P = 5.74 × 10(-6)). The panel of SNPs used for fine mapping was also tested for association with transcript levels for each of the four genes in the region in B lymphoblastoid cell lines. Significant associations were observed only for transcript levels of DEXI, a gene with unknown function. We examined the relationship between the odds ratio for type 1 diabetes and the magnitude of the effect of DEXI transcript levels for each SNP in the region. Among SNPs significantly associated with type 1 diabetes, the common allele conferred an increased risk for disease and corresponded to lower DEXI expression. Our results suggest that the primary mechanism by which genetic variation at CLEC16A contributes to the risk for type 1 diabetes is through reduced expression of DEXI.

Relatedness in the post-genomic era: is it still useful?

Relatedness is a fundamental concept in genetics but is surprisingly hard to define in a rigorous yet useful way. Traditional relatedness coefficients specify expected genome sharing between individuals in pedigrees, but actual genome sharing can differ considerably from these expected values, which in any case vary according to the pedigree that happens to be available. Nowadays, we can measure genome sharing directly from genome-wide single-nucleotide polymorphism (SNP) data; however, there are many such measures in current use, and we lack good criteria for choosing among them. Here, we review SNP-based measures of relatedness and criteria for comparing them. We discuss how useful pedigree-based concepts remain today and highlight opportunities for further advances in quantitative genetics, with a focus on heritability estimation and phenotype prediction.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) sibling pairs genome-wide data

Here we provide a detailed description of the genome-wide information available on the National Longitudinal Study of Adolescent to Adult Health (Add Health) sibling pair subsample (Harris et al. in Twin Res Hum Genet 16:391-398, 2013). A total of 2,020 samples were genotyped (including duplicates) arising from 1946 Add Health individuals from the sibling pairs subsample. After various steps for quality control (QC) and quality assurance (QA), we have high quality genome-wide data available on 1,888 individuals. In this report, we first highlight the QC and QA steps that were taken to prune the data of poorly performing samples and genetic markers. We further estimate the pairwise biological relationships using genome-wide data and compare those estimates to the assumed relationships in Add Health. Additionally, using genome-wide data from known regional reference populations from Europe, West Africa, North and South America, Japan and China, we estimate the relative genetic ancestry of the respondents. Finally, rather than conducting a traditional cross-sectional genome-wide association study (GWAS) of body mass index (BMI), we opted to utilize the extensive publicly available genome-wide information to conduct a weighted GWAS of longitudinal BMI while accounting for both family and ethnic variation.

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10⁻⁰⁶ (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10⁻¹⁰. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.

PRIMUS: rapid reconstruction of pedigrees from genome-wide estimates of identity by descent

Understanding and correctly utilizing relatedness among samples is essential for genetic analysis; however, managing sample records and pedigrees can often be error prone and incomplete. Data sets ascertained by random sampling often harbor cryptic relatedness that can be leveraged in genetic analyses for maximizing power. We have developed a method that uses genome-wide estimates of pairwise identity by descent to identify families and quickly reconstruct and score all possible pedigrees that fit the genetic data by using up to third-degree relatives, and we have included it in the software package PRIMUS (Pedigree Reconstruction and Identification of the Maximally Unrelated Set). Here, we validate its performance on simulated, clinical, and HapMap pedigrees. Among these samples, we demonstrate that PRIMUS can verify reported pedigree structures and identify cryptic relationships. Finally, we show that PRIMUS reconstructed pedigrees, all of which were previously unknown, for 203 families from a cohort collected in Starr County, TX (1,890 samples).

OTX2 mutations cause autosomal dominant pattern dystrophy of the retinal pigment epithelium

PURPOSE

To identify the genetic cause of autosomal-dominant pattern dystrophy (PD) of the retinal pigment epithelium (RPE) in two families.

METHODS AND RESULTS

Two families with autosomal-dominant PD were identified. Eight members of family 1 (five affected) were subjected to whole-genome SNP genotyping; multipoint genome-wide linkage analysis identified 7 regions of potential linkage, and genotyping four additional individuals from family 1 resulted in a maximum logarithm of odds score of 2.09 observed across four chromosomal regions. Exome sequencing of two affected family 1 members identified 15 shared non-synonymous rare coding sequence variants within the linked regions; candidate genes were prioritised and further analysed. Sanger sequencing confirmed a novel heterozygous missense variant (E79K) in orthodenticle homeobox 2 (OTX2) that segregated with the disease phenotype. Family 2 with PD (two affected) harboured the same missense variant in OTX2. A shared haplotype of 19.68 cM encompassing OTX2 was identified between affected individuals in the two families. Within the two families, all except one affected demonstrated distinct 'patterns' at the macula. In vivo structural retinal imaging showed discrete areas of RPE-photoreceptor separation at the macula in all cases. Electroretinogram testing showed generalised photoreceptor degeneration in three cases. Mild developmental anomalies were observed, including optic nerve head dysplasia (four cases), microcornea (one case) and Rathke's cleft cyst (one case); pituitary hormone levels were normal.

CONCLUSIONS

This is the first report implicating OTX2 to underlie PD. The retinal disease resembles conditional mice models that show slow photoreceptor degeneration secondary to loss of Otx2 function in the adult RPE.

Privacy preserving protocol for detecting genetic relatives using rare variants

MOTIVATION

High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.

RESULTS

In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals.

AVAILABILITY

The software is freely available for download at http://genetics.cs.ucla.edu/crypto/.

HLA-DQA1-HLA-DRB1 variants confer susceptibility to pancreatitis induced by thiopurine immunosuppressants

Pancreatitis occurs in approximately 4% of patients treated with the thiopurines azathioprine or mercaptopurine. Its development is unpredictable and almost always leads to drug withdrawal. We identified patients with inflammatory bowel disease (IBD) who had developed pancreatitis within 3 months of starting these drugs from 168 sites around the world. After detailed case adjudication, we performed a genome-wide association study on 172 cases and 2,035 controls with IBD. We identified strong evidence of association within the class II HLA region, with the most significant association identified at rs2647087 (odds ratio 2.59, 95% confidence interval 2.07-3.26, P = 2 × 10(-16)). We replicated these findings in an independent set of 78 cases and 472 controls with IBD matched for drug exposure. Fine mapping of the HLA region identified association with the HLA-DQA1*02:01-HLA-DRB1*07:01 haplotype. Patients heterozygous at rs2647087 have a 9% risk of developing pancreatitis after administration of a thiopurine, whereas homozygotes have a 17% risk.

The genetic prehistory of the New World Arctic

The New World Arctic, the last region of the Americas to be populated by humans, has a relatively well-researched archaeology, but an understanding of its genetic history is lacking. We present genome-wide sequence data from ancient and present-day humans from Greenland, Arctic Canada, Alaska, Aleutian Islands, and Siberia. We show that Paleo-Eskimos (~3000 BCE to 1300 CE) represent a migration pulse into the Americas independent of both Native American and Inuit expansions. Furthermore, the genetic continuity characterizing the Paleo-Eskimo period was interrupted by the arrival of a new population, representing the ancestors of present-day Inuit, with evidence of past gene flow between these lineages. Despite periodic abandonment of major Arctic regions, a single Paleo-Eskimo metapopulation likely survived in near-isolation for more than 4000 years, only to vanish around 700 years ago.

Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond

When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide polymorphisms, NGS data, and imputed data are generally concordant but that errors are particularly likely at rare variants, for homozygous genotypes, within regions with repeated sequences or structural variants, and within sequence data imputed from unrelated individuals. Admixture complicated identification of cryptic relatedness, but information from Mendelian transmission improved error detection and provided an estimate of the de novo mutation rate. Computationally, fast rule-based imputation was accurate but could not cover as many loci or subjects as more computationally demanding probability-based methods. Incorporating population-level data into pedigree-based imputation methods improved results. Observed data outperformed imputed data in association testing, but imputed data were also useful. We discuss the strengths and weaknesses of existing methods and suggest possible future directions, such as improving communication between data collectors and data analysts, establishing thresholds for and improving imputation quality, and incorporating error into imputation and analytical models.

Pathogenic variants for Mendelian and complex traits in exomes of 6,517 European and African Americans: implications for the return of incidental results

Exome sequencing (ES) is rapidly being deployed for use in clinical settings despite limited empirical data about the number and types of incidental results (with potential clinical utility) that could be offered for return to an individual. We analyzed deidentified ES data from 6,517 participants (2,204 African Americans and 4,313 European Americans) from the National Heart, Lung, and Blood Institute Exome Sequencing Project. We characterized the frequencies of pathogenic alleles in genes underlying Mendelian conditions commonly assessed by newborn-screening (NBS, n = 39) programs, genes associated with age-related macular degeneration (ARMD, n = 17), and genes known to influence drug response (PGx, n = 14). From these 70 genes, we identified 10,789 variants and curated them by manual review of OMIM, HGMD, locus-specific databases, or primary literature to a total of 399 validated pathogenic variants. The mean number of risk alleles per individual was 15.3. Every individual had at least five known PGx alleles, 99% of individuals had at least one ARMD risk allele, and 45% of individuals were carriers for at least one pathogenic NBS allele. The carrier burden for severe recessive childhood disorders was 0.57. Our results demonstrate that risk alleles of potential clinical utility for both Mendelian and complex traits are detectable in every individual. These findings highlight the necessity of developing guidelines and policies that consider the return of results to all individuals and underscore the need to develop innovative approaches and tools that enable individuals to exercise their choice about the return of incidental results.

Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial

Background: B vitamins play an important role in homocysteine metabolism, with vitamin deficiencies resulting in increased levels of homocysteine and increased risk for stroke. We performed a genome-wide association study (GWAS) in 2,100 stroke patients from the Vitamin Intervention for Stroke Prevention (VISP) trial, a clinical trial designed to determine whether the daily intake of high-dose folic acid, vitamins B₆, and B₁₂ reduce recurrent cerebral infarction.

Methods: Extensive quality control (QC) measures resulted in a total of 737,081 SNPs for analysis. Genome-wide association analyses for baseline quantitative measures of folate, Vitamins B₁₂, and B₆ were completed using linear regression approaches, implemented in PLINK.

Results: Six associations met or exceeded genome-wide significance (P ≤ 5 × 10⁻⁰⁸). For baseline Vitamin B₁₂, the strongest association was observed with a non-synonymous SNP (nsSNP) located in the CUBN gene (P = 1.76 × 10⁻¹³). Two additional CUBN intronic SNPs demonstrated strong associations with B₁₂ (P = 2.92 × 10⁻¹⁰ and 4.11 × 10⁻¹⁰), while a second nsSNP, located in the TCN1 gene, also reached genome-wide significance (P = 5.14 × 10⁻¹¹). For baseline measures of Vitamin B₆, we identified genome-wide significant associations for SNPs at the ALPL locus (rs1697421; P = 7.06 × 10⁻¹⁰ and rs1780316; P = 2.25 × 10⁻⁰⁸). In addition to the six genome-wide significant associations, nine SNPs (two for Vitamin B₆, six for Vitamin B₁₂, and one for folate measures) provided suggestive evidence for association (P ≤ 10⁻⁰⁷).

Conclusion: Our GWAS study has identified six genome-wide significant associations, nine suggestive associations, and successfully replicated 5 of 16 SNPs previously reported to be associated with measures of B vitamins. The six genome-wide significant associations are located in gene regions that have shown previous associations with measures of B vitamins; however, four of the nine suggestive associations represent novel finding and warrant further investigation in additional populations.

Comparison of methods to account for relatedness in genome-wide association studies with family-based data

Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.

Recently, statistical approaches known as linear mixed models (LMMs) have become popular for analysing data from genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it has not always been clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several different LMM approaches (and software implementations) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals. We also compare the LMM results to those obtained using alternative analysis methods. Overall, we find strong concordance between the results from the different LMM approaches and high correlation between the results from LMMs and most alternative approaches. We conclude that LMM approaches perform well in comparison to competing approaches and, in most applications, the precise LMM implementation will not be too important, and can be chosen on the basis of speed or convenience.

Friendship and natural selection

More than any other species, humans form social ties to individuals who are neither kin nor mates, and these ties tend to be with similar people. Here, we show that this similarity extends to genotypes. Across the whole genome, friends' genotypes at the single nucleotide polymorphism level tend to be positively correlated (homophilic). In fact, the increase in similarity relative to strangers is at the level of fourth cousins. However, certain genotypes are also negatively correlated (heterophilic) in friends. And the degree of correlation in genotypes can be used to create a "friendship score" that predicts the existence of friendship ties in a hold-out sample. A focused gene-set analysis indicates that some of the overall correlation in genotypes can be explained by specific systems; for example, an olfactory gene set is homophilic and an immune system gene set is heterophilic, suggesting that these systems may play a role in the formation or maintenance of friendship ties. Friends may be a kind of "functional kin." Finally, homophilic genotypes exhibit significantly higher measures of positive selection, suggesting that, on average, they may yield a synergistic fitness advantage that has been helping to drive recent human evolution.

Common variants in the HLA-DQ region confer susceptibility to idiopathic achalasia

Idiopathic achalasia is characterized by a failure of the lower esophageal sphincter to relax due to a loss of neurons in the myenteric plexus. This ultimately leads to massive dilatation and an irreversibly impaired megaesophagus. We performed a genetic association study in 1,068 achalasia cases and 4,242 controls and fine-mapped a strong MHC association signal by imputing classical HLA haplotypes and amino acid polymorphisms. An eight-residue insertion at position 227-234 in the cytoplasmic tail of HLA-DQβ1 (encoded by HLA-DQB1*05:03 and HLA-DQB1*06:01) confers the strongest risk for achalasia (P=1.73×10(-19)). In addition, two amino acid substitutions in the extracellular domain of HLA-DQα1 at position 41 (lysine encoded by HLA-DQA1*01:03; P=5.60×10(-10)) and of HLA-DQβ1 at position 45 (glutamic acid encoded by HLA-DQB1*03:01 and HLA-DQB1*03:04; P=1.20×10(-9)) independently confer achalasia risk. Our study implies that immune-mediated processes are involved in the pathophysiology of achalasia.

Association of MAPT haplotypes with Alzheimer's disease risk and MAPT brain gene expression levels

Introduction

MAPT encodes for tau, the predominant component of neurofibrillary tangles that are neuropathological hallmarks of Alzheimer’s disease (AD). Genetic association of MAPT variants with late-onset AD (LOAD) risk has been inconsistent, although insufficient power and incomplete assessment of MAPT haplotypes may account for this.

Methods

We examined the association of MAPT haplotypes with LOAD risk in more than 20,000 subjects (n-cases = 9,814, n-controls = 11,550) from Mayo Clinic (n-cases = 2,052, n-controls = 3,406) and the Alzheimer’s Disease Genetics Consortium (ADGC, n-cases = 7,762, n-controls = 8,144). We also assessed associations with brain MAPT gene expression levels measured in the cerebellum (n = 197) and temporal cortex (n = 202) of LOAD subjects. Six single nucleotide polymorphisms (SNPs) which tag MAPT haplotypes with frequencies greater than 1% were evaluated.

Results

H2-haplotype tagging rs8070723-G allele associated with reduced risk of LOAD (odds ratio, OR = 0.90, 95% confidence interval, CI = 0.85-0.95, p = 5.2E-05) with consistent results in the Mayo (OR = 0.81, p = 7.0E-04) and ADGC (OR = 0.89, p = 1.26E-04) cohorts. rs3785883-A allele was also nominally significantly associated with LOAD risk (OR = 1.06, 95% CI = 1.01-1.13, p = 0.034). Haplotype analysis revealed significant global association with LOAD risk in the combined cohort (p = 0.033), with significant association of the H2 haplotype with reduced risk of LOAD as expected (p = 1.53E-04) and suggestive association with additional haplotypes. MAPT SNPs and haplotypes also associated with brain MAPT levels in the cerebellum and temporal cortex of AD subjects with the strongest associations observed for the H2 haplotype and reduced brain MAPT levels (β = -0.16 to -0.20, p = 1.0E-03 to 3.0E-03).

Conclusions

These results confirm the previously reported MAPT H2 associations with LOAD risk in two large series, that this haplotype has the strongest effect on brain MAPT expression amongst those tested and identify additional haplotypes with suggestive associations, which require replication in independent series. These biologically congruent results provide compelling evidence to screen the MAPT region for regulatory variants which confer LOAD risk by influencing its brain gene expression.

Current sequencing technology makes microhaplotypes a powerful new type of genetic marker for forensics

SNPs that are molecularly very close (<10kb) will generally have extremely low recombination rates, much less than 10(-4). Multiple haplotypes will often exist because of the history of the origins of the variants at the different sites, rare recombinants, and the vagaries of random genetic drift and/or selection. Such multiallelic haplotype loci are potentially important in forensic work for individual identification, for defining ancestry, and for identifying familial relationships. The new DNA sequencing capabilities currently available make possible continuous runs of a few hundred base pairs so that we can now determine the allelic combination of multiple SNPs on each chromosome of an individual, i.e., the phase, for multiple SNPs within a small segment of DNA. Therefore, we have begun to identify regions, encompassing two to four SNPs with an extent of <200bp that define multiallelic haplotype loci. We have identified candidate regions and have collected pilot data on many candidate microhaplotype loci. Here we present 31 microhaplotype loci that have at least three alleles, have high heterozygosity, are globally informative, and are statistically independent at the population level. This study of microhaplotype loci (microhaps) provides proof of principle that such markers exist and validates their usefulness for ancestry inference, lineage-clan-family inference, and individual identification. The true value of microhaplotypes will come with sequencing methods that can establish alleles unambiguously, including disentangling of mixtures, because a single sequencing run on a single strand of DNA will encompass all of the SNPs.

Whole exome sequencing of distant relatives in multiplex families implicates rare variants in candidate genes for oral clefts

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants

Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p <1 × 10⁻⁴) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.

Pedigree-based random effect tests to screen gene pathways

The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable, suggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers.

Identity-by-descent graphs offer a flexible framework for imputation and both linkage and association analyses

We demonstrate the flexibility of identity-by-descent (IBD) graphs for genotype imputation and testing relationships between genotype and phenotype. We analyzed chromosome 3 and the first replicate of simulated diastolic blood pressure. IBD graphs were obtained from complete pedigrees and full multipoint marker analysis, facilitating subsequent linkage and other analyses. For rare alleles, pedigree-based imputation using these IBD graphs had a higher call rate than did population-based imputation. Combining the two approaches improved call rates for common alleles. We found it advantageous to incorporate known, rather than estimated, pedigree relationships when testing for association. Replacing missing data with imputed alleles improved association signals as well. Analyses were performed with knowledge of the underlying model.

Evaluation of estimated genetic values and their application to genome-wide investigation of systolic blood pressure

The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquaculture, and cash crop industries where emphasis is placed on an individual's potential to pass desirable phenotypes on to the next generation. As breeding or genetic values (as referred to here) cannot be measured directly, estimated genetic values (EGVs) are based on an individual's own phenotype, phenotype information from relatives, and, increasingly, genetic data. Because EGVs represent additive genetic variation, calculating EGVs in an extended human pedigree is expected to provide a more refined phenotype for genetic analyses. To test the utility of EGVs in genome-wide association, EGVs were calculated for 847 members of 20 extended Mexican American families based on 100 replicates of simulated systolic blood pressure. Calculations were performed in GAUSS to solve a variation on the standard Best Linear Unbiased Predictor (BLUP) mixed model equation with age, sex, and the first 3 principal components of sample-wide genetic variability as fixed effects and the EGV as a random effect distributed around the relationship matrix. Three methods of calculating kinship were considered: expected kinship from pedigree relationships, empirical kinship from common variants, and empirical kinship from both rare and common variants. Genome-wide association analysis was conducted on simulated phenotypes and EGVs using the additive measured genotype approach in the SOLAR software package. The EGV-based approach showed only minimal improvement in power to detect causative loci.

A variance component-based gene burden test

We propose a novel variance component approach for the analysis of next-generation sequencing data. Our method is based on the detection of the proportion of the trait phenotypic variance that can be explained by the introduction of a new variance component that accounts for the local gene-specific departure of the empirical kinship relationship matrix, estimated from single-nucleotide polymorphism (SNP) genotypes, from their theoretical expectation based on the genealogical information in the pedigree. We tested our method with simulated phenotypes and imputed SNP genotypes from the Genetic Analysis Workshop 18 data set. We observed considerable variation in the differences between theoretical and gene-specific kinship estimates that proved to be informative for our test and allowed us to detect the MAP4 causal gene at a genome-wide significance level. The distribution of our test statistic show no inflation under the null hypothesis and results from a random set of genes suggest that the detection of MAP4 is both sensitive and specific. The use of 2 different strategies for the selection of the SNPs used to derive the gene-specific empirical kinship relationship matrices provides us with suggestive evidence that our method is performing as an empirical test of linkage.

Dense genotyping of immune-related loci identifies variants associated with clearance of HPV among HIV-positive women in the HIV epidemiology research study (HERS)

Persistent high-risk human papillomavirus (HR-HPV) is a necessary and causal factor of cervical cancer. Most women naturally clear HPV infections; however, the biological mechanisms related to HPV pathogenesis have not been clearly elucidated. Host genetic factors that specifically regulate immune response could play an important role. All HIV-positive women in the HIV Epidemiology Research Study (HERS) with a HR-HPV infection and at least one follow-up biannual visit were included in the study. Cervicovaginal lavage samples were tested for HPV using type-specific HPV hybridization assays. Type-specific HPV clearance was defined as two consecutive HPV-negative tests after a positive test. DNA from participants was genotyped for 196,524 variants within 186 known immune related loci using the custom ImmunoChip microarray. To assess the influence of each single-nucleotide polymorphism (SNP) with HR-HPV clearance, the Cox proportional hazards model with the Wei-Lin-Weissfeld approach was used, adjusting for CD4+ count, low risk HPV (LR-HPV) co-infection, and relevant confounders. Three analytical models were performed: race-specific (African Americans (n = 258), European Americans (n = 87), Hispanics (n = 55), race-adjusted combined analysis, and meta-analysis of pooled independent race-specific analyses. Women were followed for a median time of 1,617 days. Overall, three SNPs (rs1112085, rs11102637, and rs12030900) in the MAGI-3 gene and one SNP (rs8031627) in the SMAD3 gene were associated with HR-HPV clearance (p<10(-6)). A variant (rs1633038) in HLA-G were also significantly associated in African American. Results from this study support associations of immune-related genes, having potential biological mechanism, with differential cervical HR-HPV infection outcomes.

Native American ancestry, lung function, and COPD in Costa Ricans

BACKGROUND

Whether Native American ancestry (NAA) is associated with COPD or lung function in a racially admixed Hispanic population is unknown.

METHODS

We recruited 578 Costa Ricans with and without COPD into a hybrid case-control/family-based cohort, including 316 members of families of index case subjects. All participants completed questionnaires and spirometry and gave a blood sample for DNA extraction. Genome-wide genotyping was conducted with the Illumina Human610-Quad and HumanOmniExpress BeadChip kits (Illumina Inc), and individual ancestral proportions were estimated from these genotypic data and reference panels. For unrelated individuals, linear or logistic regression was used for the analysis of NAA and COPD (GOLD [Global Initiative for Chronic Obstructive Lung Disease] stage II or greater) or lung function. For extended families, linear mixed models and generalized estimating equations were used for the analysis. All models were adjusted for age, sex, educational level, and smoking behavior; models for FEV1 were also adjusted for height.

RESULTS

The average proportion of European, Native American, and African ancestry among participants was 62%, 35%, and 3%, respectively. After adjustment for current smoking and other covariates, NAA was inversely associated with COPD (OR per 10% increment, 0.55; 95% CI, 0.41-0.75) but positively associated with FEV1, FVC, and FEV1/FVC. After additional adjustment for pack-years of smoking, the association between NAA and COPD or lung function measures was slightly attenuated. We found that about 31% of the estimated effect of NAA on COPD is mediated by pack-years of smoking.

CONCLUSIONS

NAA is inversely associated with COPD but positively associated with FEV1 or FVC in Costa Ricans. Ancestral effects on smoking behavior partly explain the findings for COPD but not for FEV1 or FVC.

Early back-to-Africa migration into the Horn of Africa

Genetic studies have identified substantial non-African admixture in the Horn of Africa (HOA). In the most recent genomic studies, this non-African ancestry has been attributed to admixture with Middle Eastern populations during the last few thousand years. However, mitochondrial and Y chromosome data are suggestive of earlier episodes of admixture. To investigate this further, we generated new genome-wide SNP data for a Yemeni population sample and merged these new data with published genome-wide genetic data from the HOA and a broad selection of surrounding populations. We used multidimensional scaling and ADMIXTURE methods in an exploratory data analysis to develop hypotheses on admixture and population structure in HOA populations. These analyses suggested that there might be distinct, differentiated African and non-African ancestries in the HOA. After partitioning the SNP data into African and non-African origin chromosome segments, we found support for a distinct African (Ethiopic) ancestry and a distinct non-African (Ethio-Somali) ancestry in HOA populations. The African Ethiopic ancestry is tightly restricted to HOA populations and likely represents an autochthonous HOA population. The non-African ancestry in the HOA, which is primarily attributed to a novel Ethio-Somali inferred ancestry component, is significantly differentiated from all neighboring non-African ancestries in North Africa, the Levant, and Arabia. The Ethio-Somali ancestry is found in all admixed HOA ethnic groups, shows little inter-individual variance within these ethnic groups, is estimated to have diverged from all other non-African ancestries by at least 23 ka, and does not carry the unique Arabian lactase persistence allele that arose about 4 ka. Taking into account published mitochondrial, Y chromosome, paleoclimate, and archaeological data, we find that the time of the Ethio-Somali back-to-Africa migration is most likely pre-agricultural.

Efficient generalized least squares method for mixed population and family-based samples in genome-wide association studies

Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.

Genetic and educational assortative mating among US adults

Understanding the social and biological mechanisms that lead to homogamy (similar individuals marrying one another) has been a long-standing issue across many fields of scientific inquiry. Using a nationally representative sample of non-Hispanic white US adults from the Health and Retirement Study and information from 1.7 million single-nucleotide polymorphisms, we compare genetic similarity among married couples to noncoupled pairs in the population. We provide evidence for genetic assortative mating in this population but the strength of this association is substantially smaller than the strength of educational assortative mating in the same sample. Furthermore, genetic similarity explains at most 10% of the assortative mating by education levels. Results are replicated using comparable data from the Framingham Heart Study.

Host genetics and immune control of HIV-1 infection: fine mapping for the extended human MHC region in an African cohort

Multiple MHC loci encoding human leukocyte antigens (HLA) have allelic variants unequivocally associated with differential immune control of HIV-1 infection. Fine mapping based on single nucleotide polymorphisms (SNPs) in the extended MHC (xMHC) region is expected to reveal causal or novel factors and to justify a search for functional mechanisms. We have tested the utility of a custom fine-mapping platform (the ImmunoChip) for 172 HIV-1 seroconverters (SCs) and 449 seroprevalent individuals (SPs) from Lusaka, Zambia, with a focus on more than 6,400 informative xMHC SNPs. When conditioned on HLA and non-genetic factors previously associated with HIV-1 viral load (VL) in the study cohort, penalized approaches (HyperLasso models) identified an intergenic SNP (rs3094626 between RPP21 and HLA-E) and an intronic SNP (rs3134931 in NOTCH4) as novel correlates of early set-point VL in SCs. The minor allele of rs2857114 (downstream from HLA-DOB) was an unfavorable factor in SPs. Joint models based on demographic features, HLA alleles and the newly identified SNP variants could explain 29% and 15% of VL variance in SCs and SPs, respectively. These findings and bioinformatics strongly suggest that both classic and non-classic MHC genes deserve further investigation, especially in Africans with relatively short haplotype blocks.

Variant association tools for quality control and analysis of large-scale sequence and genotyping array data

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT's data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN)

Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA). In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of "public controls" and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects). The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.

Inferring rare disease risk variants based on exact probabilities of sharing by multiple affected relatives

MOTIVATION

Family-based designs are regaining popularity for genomic sequencing studies because they provide a way to test cosegregation with disease of variants that are too rare in the population to be tested individually in a conventional case-control study.

RESULTS

Where only a few affected subjects per family are sequenced, the probability that any variant would be shared by all affected relatives-given it occurred in any one family member-provides evidence against the null hypothesis of a complete absence of linkage and association. A P-value can be obtained as the sum of the probabilities of sharing events as (or more) extreme in one or more families. We generalize an existing closed-form expression for exact sharing probabilities to more than two relatives per family. When pedigree founders are related, we show that an approximation of sharing probabilities based on empirical estimates of kinship among founders obtained from genome-wide marker data is accurate for low levels of kinship. We also propose a more generally applicable approach based on Monte Carlo simulations. We applied this method to a study of 55 multiplex families with apparent non-syndromic forms of oral clefts from four distinct populations, with whole exome sequences available for two or three affected members per family. The rare single nucleotide variant rs149253049 in ADAMTS9 shared by affected relatives in three Indian families achieved significance after correcting for multiple comparisons ([Formula: see text]).

AVAILABILITY AND IMPLEMENTATION

Source code and binaries of the R package RVsharing are freely available for download at http://cran.r-project.org/web/packages/RVsharing/index.html.

CONTACT

alexandre.bureau@msp.ulaval.ca or ingo@jhu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Admixture fine-mapping in African Americans implicates XAF1 as a possible sarcoidosis risk gene

Sarcoidosis is a complex, multi-organ granulomatous disease with a likely genetic component. West African ancestry confers a higher risk for sarcoidosis than European ancestry. Admixture mapping provides the most direct method to locate genes that underlie such ethnic variation in disease risk. We sought to identify genetic risk variants within four previously-identified ancestry-associated regions—6p24.3–p12.1, 17p13.3–13.1, 2p13.3–q12.1, and 6q23.3–q25.2—in a sample of 2,727 African Americans. We used logistic regression fit by generalized estimating equations and the MIX score statistic to determine which variants within ancestry-associated regions were associated with risk and responsible for the admixture signal. Fine mapping was performed by imputation, based on a previous genome-wide association study; significant variants were validated by direct genotyping. Within the 6p24.3–p12.1 locus, the most significant ancestry-adjusted SNP was rs74318745 (p = 9.4*10⁻¹¹), an intronic SNP within the HLA-DRA gene that did not solely explain the admixture signal, indicating the presence of more than a single risk variant within this well-established sarcoidosis risk region. The locus on chromosome 17p13.3–13.1 revealed a novel sarcoidosis risk SNP, rs6502976 (p = 9.5*10⁻⁶), within intron 5 of the gene X-linked Inhibitor of Apoptosis Associated Factor 1 (XAF1) that accounted for the majority of the admixture linkage signal. Immunohistochemical expression studies demonstrated lack of expression of XAF1 and a corresponding high level of expression of its downstream target, X-linked Inhibitor of Apoptosis (XIAP) in sarcoidosis granulomas. In conclusion, ancestry and association fine mapping revealed a novel sarcoidosis susceptibility gene, XAF1, which has not been identified by previous genome-wide association studies. Based on the known biology of the XIAP/XAF1 apoptosis pathway and the differential expression patterns of XAF1 and XIAP in sarcoidosis granulomas, we suggest that this pathway may play a role in the maintenance of sarcoidosis granulomas.

Genome-wide meta-analysis of homocysteine and methionine metabolism identifies five one carbon metabolism loci and a novel association of ALDH1L1 with ischemic stroke

Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of common diseases such as stroke, cardiovascular disease (CVD), cancer and dementia. The FOCM is the sole source of de novo methyl group synthesis, impacting many biological and epigenetic pathways. However, the genetic determinants of elevated tHcy (hyperhomocysteinemia), dysregulation of methionine metabolism and the underlying biological processes remain unclear. We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism, characterized by post-methionine load test tHcy, in 2,710 participants from the Framingham Heart Study (FHS) and 2,100 participants from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, and then examined the association of the identified loci with incident stroke in FHS. Five genes in the FOCM pathway (GNMT [p = 1.60 × 10(-63)], CBS [p = 3.15 × 10(-26)], CPS1 [p = 9.10 × 10(-13)], ALDH1L1 [p = 7.3 × 10(-13)] and PSPH [p = 1.17 × 10(-16)]) were strongly associated with the difference between pre- and post-methionine load test tHcy levels (ΔPOST). Of these, one variant in the ALDH1L1 locus, rs2364368, was associated with incident ischemic stroke. Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism. Additionally, a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP. Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism, and possibly the epigenome, impacting disease risk. These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets.

Identifying large sets of unrelated individuals and unrelated markers

Background

Genetic Analyses in large sample populations are important for a better understanding of the variation between populations, for designing conservation programs, for detecting rare mutations which may be risk factors for a variety of diseases, among other reasons. However these analyses frequently assume that the participating individuals or animals are mutually unrelated which may not be the case in large samples, leading to erroneous conclusions. In order to retain as much data as possible while minimizing the risk of false positives it is useful to identify a large subset of relatively unrelated individuals in the population. This can be done using a heuristic for finding a large set of independent of nodes in an undirected graph. We describe a fast randomized heuristic for this purpose. The same methodology can also be used for identifying a suitable set of markers for analyzing population stratification, and other instances where a rapid heuristic for maximal independent sets in large graphs is needed.

Results

We present FastIndep, a fast random heuristic algorithm for finding a maximal independent set of nodes in an arbitrary undirected graph along with an efficient implementation in C++. On a 64 bit Linux or MacOS platform the execution time is a few minutes, even with a graph of several thousand nodes. The algorithm can discover multiple solutions of the same cardinality. FastIndep can be used to discover unlinked markers, and unrelated individuals in populations.

Conclusions

The methods presented here provide a quick and efficient method for identifying sets of unrelated individuals in large populations and unlinked markers in marker panels. The C++ source code and instructions along with utilities for generating the input files in the appropriate format are available at http://taurus.ansci.iastate.edu/wiki/people/jabr/Joseph_Abraham.html

Genomic view of bipolar disorder revealed by whole genome sequencing in a genetic isolate

Bipolar disorder is a common, heritable mental illness characterized by recurrent episodes of mania and depression. Despite considerable effort to elucidate the genetic underpinnings of bipolar disorder, causative genetic risk factors remain elusive. We conducted a comprehensive genomic analysis of bipolar disorder in a large Old Order Amish pedigree. Microsatellite genotypes and high-density SNP-array genotypes of 388 family members were combined with whole genome sequence data for 50 of these subjects, comprising 18 parent-child trios. This study design permitted evaluation of candidate variants within the context of haplotype structure by resolving the phase in sequenced parent-child trios and by imputation of variants into multiple unsequenced siblings. Non-parametric and parametric linkage analysis of the entire pedigree as well as on smaller clusters of families identified several nominally significant linkage peaks, each of which included dozens of predicted deleterious variants. Close inspection of exonic and regulatory variants in genes under the linkage peaks using family-based association tests revealed additional credible candidate genes for functional studies and further replication in population-based cohorts. However, despite the in-depth genomic characterization of this unique, large and multigenerational pedigree from a genetic isolate, there was no convergence of evidence implicating a particular set of risk loci or common pathways. The striking haplotype and locus heterogeneity we observed has profound implications for the design of studies of bipolar and other related disorders.

Bipolar disorder is a common, heritable mental illness characterized by recurrent episodes of mania and depression. Despite considerable efforts genetic studies have yet to reveal the precise genetic underpinnings of the disorder. In this study we have analyzed a large extended pedigree of Old Order Amish that segregates bipolar disorder. Our study design integrates both dense genotype and whole-genome sequence data. In a combined linkage and association analysis we identify five chromosomal regions with nominally significant or suggestive evidence for linkage, several of which constitute replication of earlier linkage findings for bipolar disorder in non-Amish families. Association analysis of genetic variants in each of the linkage regions yielded a number of plausible candidate genes for bipolar disorder. The striking genetic heterogeneity we observed in this genetic isolate has profound implications for the study of bipolar disorder in the general population.