Ancestry estimation and control of population stratification for sequence-based association studies

GWAS for autoimmune Addison's disease identifies multiple risk loci and highlights AIRE in disease susceptibility

Autoimmune Addison's disease (AAD) is characterized by the autoimmune destruction of the adrenal cortex. Low prevalence and complex inheritance have long hindered successful genetic studies. We here report the first genome-wide association study on AAD, which identifies nine independent risk loci (P < 5 × 10^-8). In addition to loci implicated in lymphocyte function and development shared with other autoimmune diseases such as HLA, BACH2, PTPN22 and CTLA4, we associate two protein-coding alterations in Autoimmune Regulator (AIRE) with AAD. The strongest, p.R471C (rs74203920, OR = 3.4 (2.7-4.3), P = 9.0 × 10^-25) introduces an additional cysteine residue in the zinc-finger motif of the second PHD domain of the AIRE protein. This unbiased elucidation of the genetic contribution to development of AAD points to the importance of central immunological tolerance, and explains 35-41% of heritability (h²).

Inherited predisposition to breast cancer in the Carolina Breast Cancer Study

The Carolina Breast Cancer Study (CBCS) phases I-II was a case-control study of biological and social risk factors for invasive breast cancer that enrolled cases and controls between 1993 and 1999. Case selection was population-based and stratified by ancestry and age at diagnosis. Controls were matched to cases by age, self-identified race, and neighborhood of residence. Sequencing genomic DNA from 1370 cases and 1635 controls yielded odds ratios (with 95% confidence limits) for breast cancer of all subtypes of 26.7 (3.59, 189.1) for BRCA1, 8.8 (3.44, 22.48) for BRCA2, and 9.0 (2.06, 39.60) for PALB2; and for triple-negative breast cancer (TNBC) of 55.0 (7.01, 431.4) for BRCA1, 12.1 (4.18, 35.12) for BRCA2, and 10.8 (1.97, 59.11) for PALB2. Overall, 5.6% of patients carried a pathogenic variant in BRCA1, BRCA2, PALB2, or TP53, the four most highly penetrant breast cancer genes. Analysis of cases by tumor subtype revealed the expected association of TNBC versus other tumor subtypes with BRCA1, and suggested a significant association between TNBC versus other tumor subtypes with BRCA2 or PALB2 among African-American (AA) patients [2.95 (1.18, 7.37)], but not among European-American (EA) patients [0.62 (0.18, 2.09)]. AA patients with pathogenic variants in BRCA2 or PALB2 were 11 times more likely to be diagnosed with TNBC versus another tumor subtype than were EA patients with pathogenic variants in either of these genes (P = 0.001). If this pattern is confirmed in other comparisons of similarly ascertained AA and EA breast cancer patients, it could in part explain the higher prevalence of TNBC among AA breast cancer patients.

Evaluation of Patient Demographics in Clinical Cancer Genomic Testing

BACKGROUND

Inequitable use of next-generation sequencing (NGS) testing for cancer risk and treatment can contribute to heath disparity. Consequently, it is important to assess the population receiving this testing. In this article, we characterize the population receiving both germline and somatic NGS testing for cancer predisposition and precision oncology at the Genetics and Solid Tumors Laboratory of the University of Washington Medical Center.

METHODS

The general demographics, including ancestry, of patients receiving somatic testing to identify genes related to cancer treatment or prognosis, diagnosis, or germline testing for heritable cancer risk from January 2015 to July 2017 were characterized. Ancestry was determined using single nucleotide variant data and documented pedigree. The demographics of the patient population receiving testing were compared with a reference population comprising patients receiving care from the University of Washington Medical Center with a diagnosis of malignant neoplasm of breast, ovary, colon, rectum, or prostate between January 2015 and May 2018.

RESULTS

A total of 2210 unique patients were included in this study. Women composed 66% of our total tested population. Patients of European ancestry composed 78% of the tested cohort. The percentages of American Indian/Alaskan Native and Native Hawaiian/Other Pacific Islander in the cohort receiving NGS testing were significantly different than their respective distributions in the reference cohort.

CONCLUSIONS

Characterizing the demographics of patients receiving NGS testing for cancer predisposition and precision oncology using single nucleotide variant data and documented pedigree may help identify potential health disparities.

Evaluation of Whole-Exome Enrichment Solutions: Lessons from the High-End of the Short-Read Sequencing Scale

Whole-exome sequencing has become a popular technique in research and clinical settings, assisting in disease diagnosis and increasing the understanding of disease pathogenesis. In this study, we aimed to compare common enrichment capture solutions available in the market. Peripheral blood-purified DNA samples were enriched with SureSelect^QXT V6 (Agilent) and various Illumina solutions: TruSeq DNA Nano, TruSeq DNA Exome, Nextera DNA Exome, and Illumina DNA Prep with Enrichment, and sequenced on a HiSeq 4000. We found that their percentage of duplicate reads was as much as 2 times higher than previously reported values for the previous HiSeq series. SureSelect^QXT and Illumina DNA Prep with Enrichment showed the best average on-target coverage, which improved when off-target regions were included. At high coverage levels and in shared bases, these two solutions and TruSeq DNA Exome provided three of the best performances. With respect to the number of small variants detected, SureSelect^QXT presented the lowest number of detected variants in target regions. When off-target regions were considered, its ability equalized to other solutions. Our results show SureSelect^QXT and Illumina DNA Prep with Enrichment to be the best enrichment capture solutions.

Targeted sequencing reveals the somatic mutation landscape in a Swedish breast cancer cohort

Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER +  85%) and their normal tissue counterparts (n= 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

Novel score test to increase power in association test by integrating external controls

Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high-quality genotyped data to identify variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost-effective approach to increase the power of association testing. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, and so forth. Hence, based on the insight of iECAT, we propose a novel score-based test that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We assess the existence of batch effect at a variant by comparing control samples of internal and external sources. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age-related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium and Michigan Genomics Initiative. Through the incorporation of the score test approach, we extend the use of iECAT to adjust for covariates and improve power, further honing the statistical methods needed to identify disease-causing variants within the human genome.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Impact of Genetic Ancestry on Prognostic Biomarkers in Uveal Melanoma

Simple Summary

Genomic prognostic biomarkers play an important role in the application of precision medicine in patients with uveal melanoma (UM). In this study, we performed a pilot study to assess the impact of global and local genetic ancestry on the presence of these prognostic biomarkers. We found a trend for correlations between high risk biomarkers and European ancestry. These results highlight the need for a rigorous genetic ancestry methodology to study the role of ancestry in determining prognosis in patients with UM.

Abstract

Uveal melanoma (UM) is the most common cancer of the eye and leads to metastatic death in up to half of patients. Genomic prognostic biomarkers play an important role in clinical management in UM. However, research has been conducted almost exclusively in patients of European descent, such that the association between genetic admixture and prognostic biomarkers is unknown. In this study, we compiled 1381 control genomes from West African, European, East Asian, and Native American individuals, assembled a bioinformatic pipeline for assessing global and local ancestry, and performed an initial pilot study of 141 UM patients from our international referral center that manages many admixed individuals. Global and local estimates were associated with genomic prognostic determinants. Expression quantitative trait loci (eQTL) analysis was performed on variants found in segments. Globally, after correction for multiple testing, no prognostic variable was significantly enriched in a given ancestral group. However, there was a trend suggesting an increased proportion of European ancestry associated with expression of the PRAME oncogene (q = 0.06). Locally enriched European haplotypes were associated with the poor prognosis class 2 gene expression profile and with genes involved in immune regulation (q = 4.7 × 10⁻¹¹). These findings reveal potential influences of genetic ancestry on prognostic variables, implicate immune genes in prognostic differences based on ancestry, and provide a basis for future studies of admixed patients with UM using rigorous genetic ancestry methodology.

Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort

There is a robust observational relationship between lower birthweight and higher risk of cardiometabolic disease in later life. The Developmental Origins of Health and Disease (DOHaD) hypothesis posits that adverse environmental factors in utero increase future risk of cardiometabolic disease. Here, we explore if a genetic risk score (GRS) of maternal SNPs associated with offspring birthweight is also associated with offspring cardiometabolic risk factors, after controlling for offspring GRS, in up to 26,057 mother-offspring pairs (and 19,792 father-offspring pairs) from the Nord-Trøndelag Health (HUNT) Study. We find little evidence for a maternal (or paternal) genetic effect of birthweight associated variants on offspring cardiometabolic risk factors after adjusting for offspring GRS. In contrast, offspring GRS is strongly related to many cardiometabolic risk factors, even after conditioning on maternal GRS. Our results suggest that the maternal intrauterine environment, as proxied by maternal SNPs that influence offspring birthweight, is unlikely to be a major determinant of adverse cardiometabolic outcomes in population based samples of individuals.

Evaluating individual genome similarity with a topic model

MOTIVATION

Evaluating genome similarity among individuals is an essential step in data analysis. Advanced sequencing technology detects more and rarer variants for massive individual genomes, thus enabling individual-level genome similarity evaluation. However, the current methodologies, such as the principal component analysis (PCA), lack the capability to fully leverage rare variants and are also difficult to interpret in terms of population genetics.

RESULTS

Here, we introduce a probabilistic topic model, latent Dirichlet allocation, to evaluate individual genome similarity. A total of 2535 individuals from the 1000 Genomes Project (KGP) were used to demonstrate our method. Various aspects of variant choice and model parameter selection were studied. We found that relatively rare (0.001<allele frequency < 0.175) and sparse (average interval > 20 000 bp) variants are more efficient for genome similarity evaluation. At least 100 000 such variants are necessary. In our results, the populations show significantly less mixed and more cohesive visualization than the PCA results. The global similarities among the KGP genomes are consistent with known geographical, historical and cultural factors.

AVAILABILITY AND IMPLEMENTATION

The source code and data access are available at: https://github.com/lrjuan/LDA_genome.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Principals about principal components in statistical genetics

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

A missense mutation in the MLKL brace region promotes lethal neonatal inflammation and hematopoietic dysfunction

MLKL is the essential effector of necroptosis, a form of programmed lytic cell death. We have isolated a mouse strain with a single missense mutation, Mlkl^D139V, that alters the two-helix 'brace' that connects the killer four-helix bundle and regulatory pseudokinase domains. This confers constitutive, RIPK3 independent killing activity to MLKL. Homozygous mutant mice develop lethal postnatal inflammation of the salivary glands and mediastinum. The normal embryonic development of Mlkl^D139V homozygotes until birth, and the absence of any overt phenotype in heterozygotes provides important in vivo precedent for the capacity of cells to clear activated MLKL. These observations offer an important insight into the potential disease-modulating roles of three common human MLKL polymorphisms that encode amino acid substitutions within or adjacent to the brace region. Compound heterozygosity of these variants is found at up to 12-fold the expected frequency in patients that suffer from a pediatric autoinflammatory disease, chronic recurrent multifocal osteomyelitis (CRMO).

Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction

Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. We leverage linkage disequilibrium shared within study samples and from an external reference panel to improve genotyping accuracy. In an application to WES of 2527 Chinese and Malays, WEScall can reduce the genotype discordance rate from 0.26% (SE= 6.4 × 10-6) to 0.08% (SE = 3.6 × 10-6) across 1.1 million single nucleotide polymorphisms (SNPs) in the deeply sequenced target regions. Furthermore, we obtain genotypes at 0.70% (SE = 3.0 × 10-6) discordance rate across 5.2 million off-target SNPs, which had ~1.2× mean sequencing depth. Using this dataset, we perform genome-wide association studies of 10 metabolic traits. Despite of our small sample size, we identify 10 loci at genome-wide significance (P < 5 × 10-8), including eight well-established loci. The two novel loci, both associated with glycated haemoglobin levels, are GPATCH8-SLC4A1 (rs369762319, P = 2.56 × 10-12) and ROR2 (rs1201042, P = 3.24 × 10-8). Finally, using summary statistics from UK Biobank and Biobank Japan, we show that polygenic risk prediction can be significantly improved for six out of nine traits by incorporating off-target data (P < 0.01). These results demonstrate WEScall as a useful tool to facilitate WES studies with decent amounts of off-target data.

Fast and robust ancestry prediction using principal component analysis

MOTIVATION

Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computation cost because it requires running PCA separately for each study sample on the augmented dataset.

RESULTS

We develop and propose two alternative approaches: bias-adjusted projection (AP) and online ADP (OADP). Using random matrix theory, AP asymptotically estimates and adjusts for the bias of SP. OADP uses a computationally efficient online singular value decomposition algorithm, which can greatly reduce the computation cost of ADP. We carried out extensive simulation studies to show that these alternative approaches are unbiased and the computation speed can be 16-16 000 times faster than ADP. We applied our approaches to the UK Biobank data of 488 366 study samples with 2492 samples from the 1000 Genomes data as the reference. AP and OADP required 0.82 and 21 CPU hours, respectively, while the projected computation time of ADP was 1628 CPU hours. Furthermore, when inferring sub-European ancestry, SP clearly showed bias, unlike the proposed approaches.

AVAILABILITY AND IMPLEMENTATION

The OADP and AP methods, as well as SP and ADP, have been implemented in the open-source Python software FRAPOSA, available at github.com/daviddaiweizhang/fraposa.

CONTACT

leeshawn@umich.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Increased Burden of Common Risk Alleles in Children With a Significant Fracture History

Extreme presentations of common disease in children are often presumed to be of Mendelian etiology, but their polygenic basis has not been fully explored. We tested whether children with significant fracture history and no osteogenesis imperfecta (OI) are at increased polygenic risk for fracture. A childhood significant fracture history was defined as the presence of low-trauma vertebral fractures or multiple long bone fractures. We generated a polygenic score of heel ultrasound-derived speed of sound, termed "gSOS," which predicts risk of osteoporotic fracture. We tested if individuals from three cohorts with significant childhood fracture history had lower gSOS. A Canadian cohort included 94 children with suspected Mendelian osteoporosis, of which 68 had negative OI gene panel. Two Finnish cohorts included 59 children with significant fracture history and 22 with suspected Mendelian osteoporosis, among which 18 had no OI. After excluding individuals with OI and ancestral outliers, we generated gSOS estimates and compared their mean to that of a UK Biobank subset, representing the general population. The average gSOS across all three cohorts (n = 131) was -0.47 SD lower than that in UK Biobank (n = 80,027, p = 1.1 × 10^-5 ). The gSOS of 78 individuals with suspected Mendelian osteoporosis was even lower (-0.76 SD, p = 5.3 × 10^-10 ). Among the 131 individuals with a significant fracture history, we observed 8 individuals with gSOS below minus 2 SD from the mean; their mean lumbar spine DXA-derived bone mineral density Z-score was -1.7 (SD 0.8). In summary, children with significant fracture history but no OI have an increased burden of common risk alleles. This suggests that a polygenic contribution to disease should be considered in children with extreme presentations of fracture. © 2020 American Society for Bone and Mineral Research.

Ancient cattle genomics, origins, and rapid turnover in the Fertile Crescent

Genome-wide analysis of 67 ancient Near Eastern cattle, Bos taurus, remains reveals regional variation that has since been obscured by admixture in modern populations. Comparisons of genomes of early domestic cattle to their aurochs progenitors identify diverse origins with separate introgressions of wild stock. A later region-wide Bronze Age shift indicates rapid and widespread introgression of zebu, Bos indicus, from the Indus Valley. This process was likely stimulated at the onset of the current geological age, ~4.2 thousand years ago, by a widespread multicentury drought. In contrast to genome-wide admixture, mitochondrial DNA stasis supports that this introgression was male-driven, suggesting that selection of arid-adapted zebu bulls enhanced herd survival. This human-mediated migration of zebu-derived genetics has continued through millennia, altering tropical herding on each continent.

Ancestry-agnostic estimation of DNA sample contamination from sequence reads

Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

Genetic variation in GC and CYP2R1 affects 25-hydroxyvitamin D concentration and skeletal parameters: A genome-wide association study in 24-month-old Finnish children

Vitamin D is important for normal skeletal homeostasis, especially in growing children. There are no previous genome-wide association (GWA) studies exploring genetic factors that influence vitamin D metabolism in early childhood. We performed a GWA study on serum 25-hydroxyvitamin D (25(OH)D) and response to supplementation in 761 healthy term-born Finnish 24-month-old children, who participated in a randomized clinical trial comparing effects of 10 μg and 30 μg of daily vitamin D supplementation from age 2 weeks to 24 months. Using the Illumina Infinium Global Screening Array, which has been optimized for imputation, a total of 686085 markers were genotyped across the genome. Serum 25(OH)D was measured at the end of the intervention at 24 months of age. Skeletal parameters reflecting bone strength were determined at the distal tibia at 24 months using peripheral quantitative computed tomography (pQCT) (data available for 648 children). For 25(OH)D, two strong GWA signals were identified, localizing to GC (Vitamin D binding protein) and CYP2R1 (Vitamin D 25-hydroxylase) genes. The GWA locus comprising the GC gene also associated with response to supplementation. Further evidence for the importance of these two genes was obtained by comparing association signals to gene expression data from the Genotype-Tissue Expression project and performing colocalization analyses. Through the identification of haplotypes associated with low or high 25(OH)D concentrations we used a Mendelian randomization approach to show that haplotypes associating with low 25(OH)D were also associated with low pQCT parameters in the 24-month-old children. In this first GWA study on 25(OH)D in this age group we show that already at the age of 24 months genetic variation influences 25(OH)D concentrations and determines response to supplementation, with genome-wide significant associations with GC and CYP2R1. Also, the dual association between haplotypes, 25(OH)D and pQCT parameters gives support for vertical pleiotropy mediated by 25(OH)D.

In trans variant calling reveals enrichment for compound heterozygous variants in genes involved in neuronal development and growth

Compound heterozygotes occur when different variants at the same locus on both maternal and paternal chromosomes produce a recessive trait. Here we present the tool VarCount for the quantification of variants at the individual level. We used VarCount to characterize compound heterozygous coding variants in patients with epileptic encephalopathy and in the 1000 Genomes Project participants. The Epi4k data contains variants identified by whole exome sequencing in patients with either Lennox-Gastaut Syndrome (LGS) or infantile spasms (IS), as well as their parents. We queried the Epi4k dataset (264 trios) and the phased 1000 Genomes Project data (2504 participants) for recessive variants. To assess enrichment, transcript counts were compared between the Epi4k and 1000 Genomes Project participants using minor allele frequency (MAF) cutoffs of 0.5 and 1.0%, and including all ancestries or only probands of European ancestry. In the Epi4k participants, we found enrichment for rare, compound heterozygous variants in six genes, including three involved in neuronal growth and development - PRTG (p = 0.00086, 1% MAF, combined ancestries), TNC (p = 0.022, 1% MAF, combined ancestries) and MACF1 (p = 0.0245, 0.5% MAF, EU ancestry). Due to the total number of transcripts considered in these analyses, the enrichment detected was not significant after correction for multiple testing and higher powered or prospective studies are necessary to validate the candidacy of these genes. However, PRTG, TNC and MACF1 are potential novel recessive epilepsy genes and our results highlight that compound heterozygous variants should be considered in sporadic epilepsy.

Lack of APC somatic mutation is associated with early-onset colorectal cancer in African Americans

African Americans (AAs) have higher incidence and mortality rates of colorectal cancer (CRC) compared with other US populations. They present with more right-sided, microsatellite stable disease and are diagnosed at earlier ages compared with non-Hispanic Whites (NHWs). To gain insight into these trends, we conducted exome sequencing (n = 45), copy number (n = 33) and methylation analysis (n = 11) of microsatellite stable AA CRCs. Results were compared with data from The Cancer Genome Atlas (TCGA). Two of the 45 tumors contained POLE mutations. In the remaining 43 tumors, only 27 (63%) contained loss-of-function mutations in APC compared with 80% of TCGA NHW CRCs. APC-mutation-negative CRCs were associated with an earlier onset of CRC (P = 0.01). They were also associated with lower overall mutation burden, fewer copy number variants and a DNA methylation signature that was distinct from the CpG island methylator phenotype characterized in microsatellite unstable disease. Three of the APC-mutation-negative CRCs had loss-of-function mutations in BCL9L. Mutations in driver genes identified by TCGA exome analysis were less frequent in AA CRC cases than TCGA NHWs. Genes that regulate the WNT signaling pathway, including SOX9, GATA6, TET1, GLIS1 and FAT1, were differentially hypermethylated in APC-mutation-negative CRCs, suggesting a novel mechanism for cancer development in these tumors. In summary, we have identified a subtype of CRC that is associated with younger age of diagnosis, lack of APC mutation, microsatellite and chromosome stability, lower mutation burden and distinctive methylation changes.

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.

Ancient goat genomes reveal mosaic domestication in the Fertile Crescent

Current genetic data are equivocal as to whether goat domestication occurred multiple times or was a singular process. We generated genomic data from 83 ancient goats (51 with genome-wide coverage) from Paleolithic to Medieval contexts throughout the Near East. Our findings demonstrate that multiple divergent ancient wild goat sources were domesticated in a dispersed process that resulted in genetically and geographically distinct Neolithic goat populations, echoing contemporaneous human divergence across the region. These early goat populations contributed differently to modern goats in Asia, Africa, and Europe. We also detect early selection for pigmentation, stature, reproduction, milking, and response to dietary change, providing 8000-year-old evidence for human agency in molding genome variation within a partner species.

Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits

The genetic causes of cancer include both somatic mutations and inherited germline variants. Large-scale tumor sequencing has revolutionized the identification of somatic driver alterations but has had limited impact on the identification of cancer predisposition genes (CPGs). Here we present a statistical method, ALFRED, that tests Knudson's two-hit hypothesis to systematically identify CPGs from cancer genome data. Applied to ~10,000 tumor exomes the approach identifies known and putative CPGs - including the chromatin modifier NSD1 - that contribute to cancer through a combination of rare germline variants and somatic loss-of-heterozygosity (LOH). Rare germline variants in these genes contribute substantially to cancer risk, including to ~14% of ovarian carcinomas, ~7% of breast tumors, ~4% of uterine corpus endometrial carcinomas, and to a median of 2% of tumors across 17 cancer types.

Ancient genomes from North Africa evidence prehistoric migrations to the Maghreb from both the Levant and Europe

The extent to which prehistoric migrations of farmers influenced the genetic pool of western North Africans remains unclear. Archaeological evidence suggests that the Neolithization process may have happened through the adoption of innovations by local Epipaleolithic communities or by demic diffusion from the Eastern Mediterranean shores or Iberia. Here, we present an analysis of individuals' genome sequences from Early and Late Neolithic sites in Morocco and from Early Neolithic individuals from southern Iberia. We show that Early Neolithic Moroccans (∼5,000 BCE) are similar to Later Stone Age individuals from the same region and possess an endemic element retained in present-day Maghrebi populations, confirming a long-term genetic continuity in the region. This scenario is consistent with Early Neolithic traditions in North Africa deriving from Epipaleolithic communities that adopted certain agricultural techniques from neighboring populations. Among Eurasian ancient populations, Early Neolithic Moroccans are distantly related to Levantine Natufian hunter-gatherers (∼9,000 BCE) and Pre-Pottery Neolithic farmers (∼6,500 BCE). Late Neolithic (∼3,000 BCE) Moroccans, in contrast, share an Iberian component, supporting theories of trans-Gibraltar gene flow and indicating that Neolithization of North Africa involved both the movement of ideas and people. Lastly, the southern Iberian Early Neolithic samples share the same genetic composition as the Cardial Mediterranean Neolithic culture that reached Iberia ∼5,500 BCE. The cultural and genetic similarities between Iberian and North African Neolithic traditions further reinforce the model of an Iberian migration into the Maghreb.

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.

LASER server: ancestry tracing with genotypes or sequence reads

Summary

To enable direct comparison of ancestry background in different studies, we developed LASER to estimate individual ancestry by placing either sezquenced or genotyped samples in a common ancestry space, regardless of the sequencing strategy or genotyping array used to characterize each sample. Here we describe the LASER server to facilitate application of the method to a wide range of genetic studies. The server provides genetic ancestry estimation for different geographic regions and user-friendly interactive visualization of the results.

Availability and Implementation

The LASER server is freely accessible at http://laser.sph.umich.edu/

Supplementary information

Supplementary data are available at Bioinformatics online.

Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing

Motivation

Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project.

Results

To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies.

Availability and implementation

TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html.

Contact

yijuan.hu@emory.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F₂, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

The global spectrum of protein-coding pharmacogenomic diversity

Differences in response to medications have a strong genetic component. By leveraging publically available data, the spectrum of such genomic variation can be investigated extensively. Pharmacogenomic variation was extracted from the 1000 Genomes Project Phase 3 data (2504 individuals, 26 global populations). A total of 12 084 genetic variants were found in 120 pharmacogenes, with the majority (90.0%) classified as rare variants (global minor allele frequency <0.5%), with 52.9% being singletons. Common variation clustered individuals into continental super-populations and 23 pharmacogenes contained highly differentiated variants (FST>0.5) for one or more super-population comparison. A median of three clinical variants (PharmGKB level 1A/B) was found per individual, and 55.4% of individuals carried loss-of-function variants, varying by super-population (East Asian 60.9%>African 60.1%>South Asian 60.3%>European 49.3%>Admixed 39.2%). Genome sequencing can therefore identify clinical pharmacogenomic variation, and future studies need to consider rare variation to understand the spectrum of genetic diversity contributing to drug response.

On the substructure controls in rare variant analysis: Principal components or variance components?

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

Association of Rare Predicted Loss-of-Function Variants in Cellular Pathways with Sub-Phenotypes in Age-Related Macular Degeneration

PURPOSE

To investigate the association of rare predicted loss-of-function (pLoF) variants within age-related macular degeneration (AMD) risk loci and AMD sub-phenotypes.

DESIGN

Case-control study.

PARTICIPANTS

Participants of AREDS, AREDS2, and Michigan Genomics Initiative.

METHODS

Whole genome sequencing data were analyzed for rare pLoF variants (frequency <0.1%) in the regions of previously identified 52 independent risk variants known to be associated with AMD. Frequency of the rare pLoF variants in cases with intermediate or advanced AMD was compared with controls. Variants were assigned to the complement, extracellular matrix (ECM), lipid, cell survival, immune system, metabolism, or unknown/other pathway. Associations of rare pLoF variant pathways with AMD sub-phenotypes were analyzed using logistic and linear regression, and Cox proportional hazards models.

MAIN OUTCOME MEASURES

Differences in rare pLoF variant pathway burden and association of rare pLoF variant pathways with sub-phenotypes within the population with AMD were evaluated.

RESULTS

Rare pLoF variants were found in 298 of 1689 cases (17.6%) and 237 of 1518 controls (15.6%) (odds ratio [OR], 1.11; 95% confidence interval [CI], 0.91-1.36; P = 0.310). An enrichment of rare pLoF variants in the complement pathway in cases versus controls (OR, 2.94; 95% CI, 1.49-5.79; P = 0.002) was observed. Within cases, associations between all rare pLoF variants and choroidal neovascularization (CNV) (OR, 1.34; 95% CI, 1.04-1.73; P = 0.023), calcified drusen (OR, 1.33; 95% CI, 1.04-1.72; P = 0.025), higher scores on the AREDS Extended AMD Severity Scale (Standardized Coefficient Beta (β)=0.346 [0.086-0.605], P = 0.009), and progression to advanced disease (hazard ratio, 1.25; 95% CI, 1.01-1.55; P = 0.042) were observed. At the pathway level, there were associations between the complement pathway and geographic atrophy (GA) (OR, 2.17; 95% CI, 1.12-4.24; P = 0.023), the complement pathway and calcified drusen (OR, 3.75; 95% CI, 1.79-7.86; P < 0.001), and the ECM pathway and more severe levels in the AREDS Extended AMD Severity Scale (β = 0.62; 95% CI, 0.04-1.20; P = 0.035).

CONCLUSIONS

Rare pLoF variants are associated with disease progression. Variants in the complement pathway modify the clinical course of AMD and increase the risk of developing specific sub-phenotypes.

Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands

Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent-offspring trios, implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for ∼5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for ∼11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including ∼3% of isolated CHD patients and ∼28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in ∼440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.

Population Stratification in Genetic Association Studies

Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

Inference of genetic relatedness from molecular markers has broad applications in many areas, including quantitative genetics, forensics, evolution and ecology. Classic estimators, however, are not suitable for low-coverage sequencing data, which have high levels of genotype uncertainty and missing data. We evaluate existing methods and describe a new method for kinship estimation using sparse sequencing data. Our method leverages correlations between neighboring markers and models genotype uncertainty in kinship estimators for both homogeneous populations and admixed populations. We show that our method can accurately estimate kinship coefficient even when the sequencing depth is as low as ~0.15X, while existing methods have strong downward bias. Our method can be applied to estimate kinship using sparse off-target data and thus enables control of family structure and estimation of heritability in target sequencing studies, in which the deeply sequenced target regions are often too small to infer genetic relatedness. Even for whole exome sequencing, we show that our method can improve kinship and heritability estimation by including off-target data, compared to conventional analyses solely based on the target regions.

Improving power for rare-variant tests by integrating external controls

Due to the drop in sequencing cost, the number of sequenced genomes is increasing rapidly. To improve power of rare-variant tests, these sequenced samples could be used as external control samples in addition to control samples from the study itself. However, when using external controls, possible batch effects due to the use of different sequencing platforms or genotype calling pipelines can dramatically increase type I error rates. To address this, we propose novel summary statistics based single and gene- or region-based rare-variant tests that allow the integration of external controls while controlling for type I error. Our approach is based on the insight that batch effects on a given variant can be assessed by comparing odds ratio estimates using internal controls only vs. using combined control samples of internal and external controls. From simulation experiments and the analysis of data from age-related macular degeneration and type 2 diabetes studies, we demonstrate that our method can substantially improve power while controlling for type I error rate.

Extremely low-coverage whole genome sequencing in South Asians captures population genomics information

BACKGROUND

The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined.

RESULTS

South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples.

CONCLUSIONS

Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies.

Extended exome sequencing identifies BACH2 as a novel major risk locus for Addison's disease

BACKGROUND

Autoimmune disease is one of the leading causes of morbidity and mortality worldwide. In Addison's disease, the adrenal glands are targeted by destructive autoimmunity. Despite being the most common cause of primary adrenal failure, little is known about its aetiology.

METHODS

To understand the genetic background of Addison's disease, we utilized the extensively characterized patients of the Swedish Addison Registry. We developed an extended exome capture array comprising a selected set of 1853 genes and their potential regulatory elements, for the purpose of sequencing 479 patients with Addison's disease and 1394 controls.

RESULTS

We identified BACH2 (rs62408233-A, OR = 2.01 (1.71-2.37), P = 1.66 × 10^-15 , MAF 0.46/0.29 in cases/controls) as a novel gene associated with Addison's disease development. We also confirmed the previously known associations with the HLA complex.

CONCLUSION

Whilst BACH2 has been previously reported to associate with organ-specific autoimmune diseases co-inherited with Addison's disease, we have identified BACH2 as a major risk locus in Addison's disease, independent of concomitant autoimmune diseases. Our results may enable future research towards preventive disease treatment.

Height, height-related SNPs, and risk of non-melanoma skin cancer

Background:

Adult height has been associated with risk of several site-specific cancers, including melanoma. However, less attention has been given to non-melanoma skin cancer (NMSC).

Methods:

We prospectively examined the risk of squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) in relation to adult height in the Nurses' Health Study (NHS, n=117 863) and the Health Professionals Follow-up Study (HPFS, n=51 111). We also investigated the relationships between height-related genetic markers and risk of BCC and SCC in the genetic data sets of the NHS and HPFS (3898 BCC cases, and 8530 BCC controls; 527 SCC cases, and 8962 SCC controls).

Results:

After controlling for potential confounding factors, the hazard ratios were 1.09 (95% CI: 1.02, 1.15) and 1.10 (95% CI: 1.07, 1.13) for the associations between every 10 cm increase in height and risk of SCC and BCC respectively. None of the 687 height-related single-nucleotide polymorphisms (SNPs) was significantly associated with the risk of SCC or BCC, nor were the genetic scores combining independent height-related loci.

Conclusions:

Our data from two large cohorts provide further evidence that height is associated with an increased risk of NMSC. More studies on height-related genetic loci and early-life exposures may help clarify the underlying mechanisms.

Recent advances in the study of fine-scale population structure in humans

Empowered by modern genotyping and large samples, population structure can be accurately described and quantified even when it only explains a fraction of a percent of total genetic variance. This is especially relevant and interesting for humans, where fine-scale population structure can both confound disease-mapping studies and reveal the history of migration and divergence that shaped our species' diversity. Here we review notable recent advances in the detection, use, and understanding of population structure. Our work addresses multiple areas where substantial progress is being made: improved statistics and models for better capturing differentiation, admixture, and the spatial distribution of variation; computational speed-ups that allow methods to scale to modern data; and advances in haplotypic modeling that have wide ranging consequences for the analysis of population structure. We conclude by outlining four important open challenges: the limitations of discrete population models, uncertainty in individual origins, the incorporation of both fine-scale structure and ancient DNA in parametric models, and the development of efficient computational tools, particularly for haplotype-based methods.

A missense variant in FGD6 confers increased risk of polypoidal choroidal vasculopathy

Polypoidal choroidal vasculopathy (PCV), a subtype of 'wet' age-related macular degeneration (AMD), constitutes up to 55% of cases of wet AMD in Asian patients. In contrast to the choroidal neovascularization (CNV) subtype, the genetic risk factors for PCV are relatively unknown. Exome sequencing analysis of a Han Chinese cohort followed by replication in four independent cohorts identified a rare c.986A>G (p.Lys329Arg) variant in the FGD6 gene as significantly associated with PCV (P = 2.19 × 10(-16), odds ratio (OR) = 2.12) but not with CNV (P = 0.26, OR = 1.13). The intracellular localization of FGD6-Arg329 is distinct from that of FGD6-Lys329. In vitro, FGD6 could regulate proangiogenic activity, and oxidized phospholipids increased expression of FGD6. FGD6-Arg329 promoted more abnormal vessel development in the mouse retina than FGD6-Lys329. Collectively, our data suggest that oxidized phospholipids and FGD6-Arg329 might act synergistically to increase susceptibility to PCV.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs.

Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing

OBJECTIVES

To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors.

METHODS

We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors.

RESULTS

We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique.

CONCLUSIONS

Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report.

Genomic signals of migration and continuity in Britain before the Anglo-Saxons

The purported migrations that have formed the peoples of Britain have been the focus of generations of scholarly controversy. However, this has not benefited from direct analyses of ancient genomes. Here we report nine ancient genomes (∼ 1 ×) of individuals from northern Britain: seven from a Roman era York cemetery, bookended by earlier Iron-Age and later Anglo-Saxon burials. Six of the Roman genomes show affinity with modern British Celtic populations, particularly Welsh, but significantly diverge from populations from Yorkshire and other eastern English samples. They also show similarity with the earlier Iron-Age genome, suggesting population continuity, but differ from the later Anglo-Saxon genome. This pattern concords with profound impact of migrations in the Anglo-Saxon period. Strikingly, one Roman skeleton shows a clear signal of exogenous origin, with affinities pointing towards the Middle East, confirming the cosmopolitan character of the Empire, even at its northernmost fringes.

Improved ancestry estimation for both genotyping and sequencing data using projection procrustes analysis and genotype imputation

Accurate estimation of individual ancestry is important in genetic association studies, especially when a large number of samples are collected from multiple sources. However, existing approaches developed for genome-wide SNP data do not work well with modest amounts of genetic data, such as in targeted sequencing or exome chip genotyping experiments. We propose a statistical framework to estimate individual ancestry in a principal component ancestry map generated by a reference set of individuals. This framework extends and improves upon our previous method for estimating ancestry using low-coverage sequence reads (LASER 1.0) to analyze either genotyping or sequencing data. In particular, we introduce a projection Procrustes analysis approach that uses high-dimensional principal components to estimate ancestry in a low-dimensional reference space. Using extensive simulations and empirical data examples, we show that our new method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantially outperform LASER 1.0 in estimating fine-scale genetic ancestry. Specifically, LASER 2.0 can accurately estimate fine-scale ancestry within Europe using either exome chip genotypes or targeted sequencing data with off-target coverage as low as 0.05×. Under the framework of LASER 2.0, we can estimate individual ancestry in a shared reference space for samples assayed at different loci or by different techniques. Therefore, our ancestry estimation method will accelerate discovery in disease association studies not only by helping model ancestry within individual studies but also by facilitating combined analysis of genetic data from multiple sources.