Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland

Localized variation in ancestral admixture identifies pilocytic astrocytoma risk loci among Latino children

Background

Pilocytic astrocytoma (PA) is the most common pediatric brain tumor. PA has at least a 50% higher incidence in populations of European ancestry compared to other ancestral groups, which may be due in part to genetic differences.

Methods

We first compared the global proportions of European, African, and Amerindian ancestries in 301 PA cases and 1185 controls of self-identified Latino ethnicity from the California Biobank. We then conducted admixture mapping analysis to assess PA risk with local ancestry.

Results

We found PA cases had a significantly higher proportion of global European ancestry than controls (case median = 0.55, control median = 0.51, P value = 3.5x10^-3). Admixture mapping identified 13 SNPs in the 6q14.3 region (SNX14) contributing to risk, as well as three other peaks approaching significance on chromosomes 7, 10 and 13. Downstream fine mapping in these regions revealed several SNPs potentially contributing to childhood PA risk.

Conclusions

There is a significant difference in genomic ancestry associated with Latino PA risk and several genomic loci potentially mediating this risk.

Childhood brain tumors are among the most prevalent and lethal childhood cancers. Despite this, the epidemiology as well as genetic risks are not well defined. For example, children of European ancestry have a higher risk of contracting pilocytic astrocytoma (PA) compared to other ancestries, but the genetic or environmental basis for this is unknown. Latino children are a mixture of multiple ancestries including European, African, and Native American. Using a group of Californian Latino children, we show that the risk of PA increases when a Latino child has a higher proportion of European ancestry. This global ancestry difference shows that germline genetic risk alleles contribute to a higher PA risk in children of European descendent. Moreover, this ancestral risk is localized to specific regions of the genome, especially in Chromosome 6 near the SNX14 gene, which is associated with cancer-related growth signaling pathway described by MAPK/ERK. This result brings us one step closer to understanding the etiology of this common childhood brain tumor.

A genealogical estimate of genetic relationships

The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM treats linked markers as independent and does not explicitly model the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework, namely the expected GRM (eGRM), to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations, we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed with ARG inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to SNP array genotypes from a population sample from Northern and Eastern Finland, we find that clustering analysis with the eGRM reveals population structure driven by subpopulations that would not be apparent via the canonical GRM and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.

Allele frequency differentiation at height-associated SNPs among continental human populations

Methods to detect polygenic adaptation have recently been shown to be sensitive to uncorrected stratification in GWAS, thereby casting doubts on whether polygenic adaptation is prevalent among humans. Consistent with a signal of adaptation at human height loci, the mean F_ST among African, East Asian, and European populations was shown to be significantly higher at height-associated SNPs than that at non-associated SNPs. This conclusion was reached, however, using height-associated SNPs ascertained from a GWAS design impacted by residual confounding due to uncorrected stratification. Specifically, we show here that the estimated effect sizes are significantly correlated with population structure across continents, potentially explaining the elevated differentiation previously reported. We alleviated these concerns of confounding by ascertaining height-associated SNPs from two biobank GWAS (UK Biobank, UKB, and Biobank Japan, BBJ), where measures to control for confounding in GWAS are more effective. Consistent with a global signature of polygenic adaptation, we found that compared to non-associated SNPs, frequencies of height-associated SNPs are indeed significantly more differentiated among continental populations from both the 1000 Genomes Project (p = 0.0012 for UKB and p = 0.0265 for BBJ), and the Human Genome Diversity Project (p = 0.0225 for UKB and p = 0.0032 for BBJ). However, we found no significant difference among continental populations in polygenic height scores. Through simulations, we found that polygenic score-based statistics could lose power in detecting polygenic adaptation in presence of independent converging selections, thereby potentially explaining the inconsistent results based on F_ST and polygenic scores.

Novel ultra-rare exonic variants identified in a founder population implicate cadherins in schizophrenia

The identification of rare variants associated with schizophrenia has proven challenging due to genetic heterogeneity, which is reduced in founder populations. In samples from the Ashkenazi Jewish population, we report that schizophrenia cases had a greater frequency of novel missense or loss of function (MisLoF) ultra-rare variants (URVs) compared to controls, and the MisLoF URV burden was inversely correlated with polygenic risk scores in cases. Characterizing 141 "case-only" genes (MisLoF URVs in ≥3 cases with none in controls), the cadherin gene set was associated with schizophrenia. We report a recurrent case mutation in PCDHA3 that results in the formation of cytoplasmic aggregates and failure to engage in homophilic interactions on the plasma membrane in cultured cells. Modeling purifying selection, we demonstrate that deleterious URVs are greatly overrepresented in the Ashkenazi population, yielding enhanced power for association studies. Identification of the cadherin/protocadherin family as risk genes helps specify the synaptic abnormalities central to schizophrenia.

Increased ultra-rare variant load in an isolated Scottish population impacts exonic and regulatory regions

Human population isolates provide a snapshot of the impact of historical demographic processes on population genetics. Such data facilitate studies of the functional impact of rare sequence variants on biomedical phenotypes, as strong genetic drift can result in higher frequencies of variants that are otherwise rare. We present the first whole genome sequencing (WGS) study of the VIKING cohort, a representative collection of samples from the isolated Shetland population in northern Scotland, and explore how its genetic characteristics compare to a mainland Scottish population. Our analyses reveal the strong contributions played by the founder effect and genetic drift in shaping genomic variation in the VIKING cohort. About one tenth of all high-quality variants discovered are unique to the VIKING cohort or are seen at frequencies at least ten fold higher than in more cosmopolitan control populations. Multiple lines of evidence also suggest relaxation of purifying selection during the evolutionary history of the Shetland isolate. We demonstrate enrichment of ultra-rare VIKING variants in exonic regions and for the first time we also show that ultra-rare variants are enriched within regulatory regions, particularly promoters, suggesting that gene expression patterns may diverge relatively rapidly in human isolates.

A genome-wide association study of tramadol metabolism from post-mortem samples

Phase I tramadol metabolism requires cytochrome p450 family 2, subfamily D, polypeptide 6 (CYP2D6) to form O-desmethyltramadol (M1). CYP2D6 genetic variants may infer metabolizer phenotype; however, drug ADME (absorption, distribution, metabolism, and excretion) and response depend on protein pathway(s), not CYP2D6 alone. There is a paucity of data regarding the contribution of trans-acting proteins to idiosyncratic phenotypes following drug exposure. A genome-wide association study identified five markers (rs79983226/kgp11274252, rs9384825, rs62435418/kgp10370907, rs72732317/kgp3743668, and rs184199168/exm1592932) associated with the conversion of tramadol to M1 (M1:T). These SNPs reside within five genes previously implicated with adverse reactions. Analysis of accompanying toxicological meta-data revealed a significant positive linear relationship between M1:T and degree of sample polypharmacy. Taken together, these data identify candidate loci for potential clinical inferences of phenotype following exposure to tramadol and highlight sample polypharmacy as a possible diagnostic covariate in post-mortem genetic studies.

Understanding the Hidden Complexity of Latin American Population Isolates

Most population isolates examined to date were founded from a single ancestral population. Consequently, there is limited knowledge about the demographic history of admixed population isolates. Here we investigate genomic diversity of recently admixed population isolates from Costa Rica and Colombia and compare their diversity to a benchmark population isolate, the Finnish. These Latin American isolates originated during the 16th century from admixture between a few hundred European males and Amerindian females, with a limited contribution from African founders. We examine whole-genome sequence data from 449 individuals, ascertained as families to build mutigenerational pedigrees, with a mean sequencing depth of coverage of approximately 36×. We find that Latin American isolates have increased genetic diversity relative to the Finnish. However, there is an increase in the amount of identity by descent (IBD) segments in the Latin American isolates relative to the Finnish. The increase in IBD segments is likely a consequence of a very recent and severe population bottleneck during the founding of the admixed population isolates. Furthermore, the proportion of the genome that falls within a long run of homozygosity (ROH) in Costa Rican and Colombian individuals is significantly greater than that in the Finnish, suggesting more recent consanguinity in the Latin American isolates relative to that seen in the Finnish. Lastly, we find that recent consanguinity increased the number of deleterious variants found in the homozygous state, which is relevant if deleterious variants are recessive. Our study suggests that there is no single genetic signature of a population isolate.

Shortcut to success? Negotiating genetic uniqueness in global biomedicine

Since the sequencing of the human genome, as well as the completion of the first Human Genome Diversity Project, the benefits of studying one human population over another has been an ongoing debate relating to the replicability of findings in other populations. The leveraging of specific populations into research markets has made headlines in cases such as deCode in Iceland, Quebec Founder Population, and Generation Scotland. In such cases, researchers and policy makers have used the genetic and historical uniqueness of their populations to attract scientific, commercial and political interest. In this article, we explore how in countries with population isolates, such as Finland, the researchers balance considerations relating to the generalization and replicability of findings in small yet unique research populations to global biomedical research interests. This highlights challenges related to forms of competition associated with genetics research markets, as well as what counts as the 'right' population for genetic research.

Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland

Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assembled a comprehensive view of recent population history (≤100 generations), the timespan during which most rare-disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to that of 16,060 Swedes, Estonians, Russians, and Hungarians from geographically and linguistically adjacent countries with different population histories. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from more than 25,000 individuals, we find that although haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland typically share several-fold more of their genome in identity-by-descent segments than individuals from southwest regions. We estimate recent effective population-size changes through time across regions of Finland, and we find that there was more continuous gene flow as Finns migrated from southwest to northeast between the early- and late-settlement regions than was dichotomously described previously. Lastly, we show that haplotype sharing is locally enriched by an order of magnitude among pairs of individuals sharing rare alleles and especially among pairs sharing rare disease-causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits

In genetic association studies of rare variants, low statistical power and potential violations of established estimator properties are among the main challenges of association tests. Multi-marker tests (MMTs) have been proposed to target these challenges, but any comparison with single-marker tests (SMTs) has to consider that their aim is to identify causal genomic regions instead of variants. Valid power comparisons have been performed for the analysis of binary traits indicating that MMTs have higher power, but there is a lack of conclusive studies for quantitative traits. The aim of our study was therefore to fairly compare SMTs and MMTs in their empirical power to identify the same causal loci associated with a quantitative trait. The results of extensive simulation studies indicate that previous results for binary traits cannot be generalized. First, we show that for the analysis of quantitative traits, conventional estimation methods and test statistics of single-marker approaches have valid properties yielding association tests with valid type I error, even when investigating singletons or doubletons. Furthermore, SMTs lead to more powerful association tests for identifying causal genes than MMTs when the effect sizes of causal variants are large, and less powerful tests when causal variants have small effect sizes. For moderate effect sizes, whether SMTs or MMTs have higher power depends on the sample size and percentage of causal SNVs. For a more complete picture, we also compare the power in studies of quantitative and binary traits, and the power to identify causal genes with the power to identify causal rare variants. In a genetic association analysis of systolic blood pressure in the Genetic Analysis Workshop 19 data, SMTs yielded smaller p-values compared to MMTs for most of the investigated blood pressure genes, and were least influenced by the definition of gene regions.

Estimating the selective effects of heterozygous protein-truncating variants from human exome data

The dispensability of individual genes for viability has interested generations of geneticists. For some genes it is essential to maintain two functional chromosomal copies, while others may tolerate the loss of one or both copies. Exome sequence data from 60,706 individuals provide sufficient observations of rare protein truncating variants (PTVs) to make genome-wide estimates of selection against heterozygous loss of gene function. The cumulative frequency of rare deleterious PTVs is primarily determined by the balance between incoming mutations and purifying selection rather than genetic drift. This enables the estimation of the genome-wide distribution of selection coefficients for heterozygous PTVs and corresponding Bayesian estimates for individual genes. The strength of selection can discriminate the severity, age of onset, and mode of inheritance in Mendelian exome sequencing cases. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, putatively cell-essential genes, Mendelian disease genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that likely have critical function but have not yet been extensively annotated in published literature.

Genetics of Type 2 Diabetes: the Power of Isolated Populations

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

Progress in methods for rare variant association

Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, methods for rare variant association have been an active area of research for the past decade. Here, we provide a survey of the current literature and developments from the Genetics Analysis Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we present the generalized linear regression framework and associated score statistic for the 2 major types of methods: burden and variance components methods. We further show that by simply modifying weights within these frameworks we arrive at many of the popular existing methods, for example, the cohort allelic sums test and sequence kernel association test. Meta-analysis techniques are also described. Next, we describe the 6 contributions from the GAW19 Collapsing Rare Variants working group. These included development of new methods, such as a retrospective likelihood for family data, a method using genomic structure to compare cases and controls, a haplotype-based meta-analysis, and a permutation-based method for combining different statistical tests. In addition, one contribution compared a mega-analysis of family-based and population-based data to meta-analysis. Finally, the power of existing family-based methods for binary traits was compared. We conclude with suggestions for open research questions.

Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations

Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.

Complex trait architecture: the pleiotropic model revisited

There is currently much debate about how much the genetic heritability of complex traits is due to very rare alleles. This issue is important because it determines sampling strategies for genetic association studies. Several recent theoretical papers based on a pleiotropic model for trait evolution suggest that it is possible that a large proportion of the genetic variance could be explained by rare alleles. This model assumes that mutations with a large effect on fitness also tend to have large positive or negative effects on phenotypic traits. We show that conclusions based on standard diffusion results are generally applicable to simulations of whole genomes with overlapping generations in a finite population, although the variance contribution of rare alleles is somewhat smaller than theoretical predictions. We show that under many scenarios the pleiotropic model predicts trait distributions that are unrealistically leptokurtic. We argue that this imposes a limit on the relationship between fitness and trait effects.

An exome study of Parkinson's disease in Sardinia, a Mediterranean genetic isolate

Parkinson's disease (PD) is a common neurodegenerative disorder of complex aetiology. Rare, highly penetrant PD-causing mutations and common risk factors of small effect size have been identified in several genes/loci. However, these mutations and risk factors only explain a fraction of the disease burden, suggesting that additional, substantial genetic determinants remain to be found. Genetically isolated populations offer advantages for dissecting the genetic architecture of complex disorders, such as PD. We performed exome sequencing in 100 unrelated PD patients from Sardinia, a genetic isolate. SNPs absent from dbSNP129 and 1000 Genomes, shared by at least five patients, and of functional effects were genotyped in an independent Sardinian case-control sample (n = 500). Variants associated with PD with nominal p value <0.05 and those with odds ratio (OR) ≥3 were validated by Sanger sequencing and typed in a replication sample of 2965 patients and 2678 controls from Italy, Spain, and Portugal. We identified novel moderately rare variants in several genes, including SCAPER, HYDIN, UBE2H, EZR, MMRN2 and OGFOD1 that were specifically present in PD patients or enriched among them, nominating these as novel candidate risk genes for PD, although no variants achieved genome-wide significance after Bonferroni correction. Our results suggest that the genetic bases of PD are highly heterogeneous, with implications for the design of future large-scale exome or whole-genome analyses of this disease.

Characteristics of neutral and deleterious protein-coding variation among individuals and populations

Whole-genome and exome data sets continue to be produced at a frenetic pace, resulting in massively large catalogs of human genomic variation. However, a clear picture of the characteristics and patterns of neutral and deleterious variation within and between populations has yet to emerge, given that recent large-scale sequencing studies have often emphasized different aspects of the data and sometimes appear to have conflicting conclusions. Here, we comprehensively studied characteristics of protein-coding variation in high-coverage exome sequence data from 6,515 European American (EA) and African American (AA) individuals. We developed an unbiased approach to identify putatively deleterious variants and investigated patterns of neutral and deleterious single-nucleotide variants and alleles between individuals and populations. We show that there are substantial differences in the composition of genotypes between EA and AA populations and that small but statistically significant differences exist in the average number of deleterious alleles carried by EA and AA individuals. Furthermore, we performed extensive simulations to delineate the temporal dynamics of deleterious alleles for a broad range of demographic models and use these data to inform the interpretation of empirical patterns of deleterious variation. Finally, we illustrate that the effects of demographic perturbations, such as bottlenecks and expansions, often manifest in opposing patterns of neutral and deleterious variation depending on whether the focus is on populations or individuals. Our results clarify seemingly disparate empirical characteristics of protein-coding variation and provide substantial insights into how natural selection and demographic history have patterned neutral and deleterious variation within and between populations.