FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

Deconstructing the Pharmacological Contribution of Sphingosine-1 Phosphate Receptors to Mouse Models of Multiple Sclerosis Using the Species Selectivity of Ozanimod, a Dual Modulator of Human Sphingosine 1-Phosphate Receptor Subtypes 1 and 5.

Ozanimod, a sphingosine 1-phosphate (S1P) receptor modulator that binds with high affinity selectively to S1P receptor subtypes 1 (S1P1) and 5 (S1P5), is approved for the treatment of relapsing multiple sclerosis (MS) in multiple countries. Ozanimod profiling revealed a species difference in its potency for S1P5 in mouse, rat, and canine compared with that for human and monkey. Site-directed mutagenesis identified amino acid alanine at position 120 to be responsible for loss of activity for mouse, rat, and canine S1P5, and mutation back to threonine as in human/monkey S1P5 restored activity. Radioligand binding analysis performed with mouse S1P5 confirmed the potency loss is a consequence of a loss of affinity of ozanimod for mouse S1P5 and was restored with mutation of alanine 120 to threonine. Study of ozanimod in preclinical mouse models of MS can now determine the S1P receptor(s) responsible for observed efficacies with receptor engagement as measured using pharmacokinetic exposures of free drug. Hence, in the experimental autoimmune encephalomyelitis model, ozanimod exposures sufficient to engage S1P1, but not S1P5, resulted in reduced circulating lymphocytes, disease scores, and body weight loss; reduced inflammation, demyelination, and apoptotic cell counts in the spinal cord; and reduced circulating levels of the neuronal degeneration marker, neurofilament light. In the demyelinating cuprizone model, ozanimod prevented axonal degradation and myelin loss during toxin challenge but did not facilitate enhanced remyelination after intoxication. Since free drug levels in this model only engaged S1P1, we concluded that S1P1 activation is neuroprotective but does not appear to affect remyelination. SIGNIFICANCE STATEMENT: Ozanimod, a selective modulator of human sphingisone 1-phosphate receptor subtypes 1 and 5 (S1P1/5), displays reduced potency for rodent and dog S1P5 compared with human, which results from mutation of threonine to alanine at position 120. Ozanimod can thus be used as a selective S1P1 agonist in mouse models of multiple sclerosis to define efficacies driven by S1P1 but not S1P5. Based on readouts for experimental autoimmune encephalomyelitis and cuprizone intoxication, S1P1 modulation is neuroprotective, but S1P5 activity may be required for remyelination.

Accelerating Medicines Partnership: Parkinson's Disease. Genetic Resource.

BACKGROUND: Whole-genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical. OBJECTIVES: The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole-genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. METHODS: The version 1 release contains whole-genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole-genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. RESULTS: The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk-associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. CONCLUSIONS: We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.

Genome sequence of a diabetes-prone rodent reveals a mutation hotspot around the ParaHox gene cluster.

The sand rat Psammomys obesus is a gerbil species native to deserts of North Africa and the Middle East, and is constrained in its ecology because high carbohydrate diets induce obesity and type II diabetes that, in extreme cases, can lead to pancreatic failure and death. We report the sequencing of the sand rat genome and discovery of an unusual, extensive, and mutationally biased GC-rich genomic domain. This highly divergent genomic region encompasses several functionally essential genes, and spans the ParaHox cluster which includes the insulin-regulating homeobox gene Pdx1. The sequence of sand rat Pdx1 has been grossly affected by GC-biased mutation, leading to the highest divergence observed for this gene across the Bilateria. In addition to genomic insights into restricted caloric intake in a desert species, the discovery of a localized chromosomal region subject to elevated mutation suggests that mutational heterogeneity within genomes could influence the course of evolution.

Nonpathogenic SIV and Pathogenic HIV Infections Associate with Disparate Innate Cytokine Signatures in Response to Mycobacterium bovis BCG.

Infections with mycobacteria, including Mycobacterium tuberculosis (Mtb) and Mycobacterium bovis (M. bovis) BCG, are a leading cause of morbidity and mortality for HIV-infected persons. In contrast to HIV, nonpathogenic SIV infections of sooty mangabeys are characterized by a lack of clinical disease including an absence of opportunistic infections. The goal of this study was to identify innate immune responses to M. bovis BCG maintained during nonpathogenic lentiviral infections through a comparison of functional responses during pathogenic HIV or nonpathogenic SIV infections. Monocytes were evaluated for their ability to express key anti-mycobacterial cytokines TNF-α and IL-12 following a six-hour ex vivo BCG exposure. While HIV-infection was associated with a decreased percentage of IL-12-producing monocytes, nonpathogenic SIV-infection was associated with an increased percentage of monocytes producing both cytokines. Gene expression analysis of PBMC following ex vivo BCG exposure identified differential expression of NK cell-related genes and several cytokines, including IFN-γ and IL-23, between HIV-infected and control subjects. In contrast, SIV-infected and uninfected-control mangabeys exhibited no significant differences in gene expression after BCG exposure. Finally, differential gene expression patterns were identified between species, with mangabeys exhibiting lower IL-6 and higher IL-17 in response to BCG when compared to humans. Overall, this comparison of immune responses to M. bovis BCG identified unique immune signatures (involving cytokines IL-12, TNF-α, IL-23, IL-17, and IL-6) that are altered during HIV, but maintained or increased during nonpathogenic SIV infections. These unique cytokine and transcriptome signatures provide insight into the differential immune responses to Mycobacteria during pathogenic HIV-infection that may be associated with an increased incidence of mycobacterial co-infections.

Genomic architecture of inflammatory bowel disease in five families with multiple affected individuals.

Currently, the best clinical predictor for inflammatory bowel disease (IBD) is family history. Over 163 sequence variants have been associated with IBD in genome-wide association studies, but they have weak effects and explain only a fraction of the observed heritability. It is expected that additional variants contribute to the genomic architecture of IBD, possibly including rare variants with effect sizes larger than the identified common variants. Here we applied a family study design and sequenced 38 individuals from five families, under the hypothesis that families with multiple IBD-affected individuals harbor one or more risk variants that (i) are shared among affected family members, (ii) are rare and (iii) have substantial effect on disease development. Our analysis revealed not only novel candidate risk variants but also high polygenic risk scores for common known risk variants in four out of the five families. Functional analysis of our top novel variant in the remaining family, a rare missense mutation in the ubiquitin ligase TRIM11, suggests that it leads to increased nuclear factor of kappa light chain enhancer in B-cells (NF-κB) signaling. We conclude that an accumulation of common weak-effect variants accounts for the high incidence of IBD in most, but not all families we analyzed and that a family study design can identify novel rare variants conferring risk for IBD with potentially large effect size, such as the TRIM11 p.H414Y mutation.

Applying genetics in inflammatory disease drug discovery.

Recent groundbreaking work in genetics has identified thousands of small-effect genetic variants throughout the genome that are associated with almost all major diseases. These genome-wide association studies (GWAS) are often proposed as a source of future medical breakthroughs. However, with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection.

Marker panels for genealogy-based mapping, breed demographics, and inference-of-ancestry in the dog.

Short tandem repeat polymorphisms (STRPs) are robust and informative markers for a range of genetic applications. STRPs are advantageous in experimental designs that derive power from sampling many individuals rather than many loci (e.g., pedigree-based studies, fine-scale mapping, and conservation genetics). STRPs have proven useful for vetting samples prior to costly high-density SNP analysis. Here we present validated STRPs (n = 1,012) spanning the canine genome (2.1 +/-1.4 Mb; 2.1 +/-2.1 cM). Standardized design, pre-multiplexing, M13-based dye-labeling, and selection for loci amenable to semi-automated allele-scoring minimize cost and facilitate efficient genotyping. The markers are leveraged from the canine linkage map, and thus are backed by genetic data useful for parametric multipoint analysis and assessment of empiric coverage. We demonstrate several applications with different marker subsets. The complete set provides a genome scan for linkage at ∼5 cM resolution. A subset of the markers measures molecular diversity between domestic and wild canid populations. Another subset reflects ancestry within breeds, uncovering hidden stratification and flagging genetic outliers prior to SNP genotyping. Thus, the markers described here add flexibility and cost effectiveness to several genetic applications in the dog that complement genome-wide SNP genotyping studies. Supplemental material is available for this article. Go to the publisher's online edition of Animal Biotechnology.

Clusters of adaptive evolution in the human genome.

Considerable work has been devoted to identifying regions of the human genome that have been subjected to recent positive selection. Although detailed follow-up studies of putatively selected regions are critical for a deeper understanding of human evolutionary history, such studies have received comparably less attention. Recently, we have shown that ALMS1 has been the target of recent positive selection acting on standing variation in Eurasian populations. Here, we describe a careful follow-up analysis of genetic variation across the ALMS1 region, which unexpectedly revealed a cluster of substrates of positive selection. Specifically, through the analysis of SNP data from the HapMap and Human Genome Diversity Project-Centre d'Etude du Polymorphisme Humain samples as well sequence data from the region, we find compelling evidence for three independent and distinct signals of recent positive selection across this 3 Mb region surrounding ALMS1. Moreover, we analyzed the HapMap data to identify other putative clusters of independent selective events and conservatively discovered 19 additional clusters of adaptive evolution. This work has important implications for the interpretation of genome-scans for positive selection in humans and more broadly contributes to a better understanding of how recent positive selection has shaped genetic variation across the human genome.

Genome-wide insights into the patterns and determinants of fine-scale population structure in humans.

Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.

Population genomic analysis of ALMS1 in humans reveals a surprisingly complex evolutionary history.

Mutations in the human gene ALMS1 result in Alström Syndrome, which presents with early childhood obesity and insulin resistance leading to Type 2 diabetes. Previous genomewide scans for selection in the HapMap data based on linkage disequilibrium and population structure suggest that ALMS1 was subject to recent positive selection. Through a detailed population genomic analysis of existing genomewide data sets and new resequencing data obtained in geographically diverse populations, we find that the signature of selection at ALMS1 is considerably more complex than what would be expected for an idealized model of a selective sweep acting on a newly arisen advantageous mutation. Specifically, we observed three highly divergent and globally dispersed haplogroups, two of which carry a set of seven derived nonsynonymous single nucleotide polymorphisms that are nearly fixed in Asian populations. Our data suggest that the interaction of human demographic history and positive selection on standing variation in Eurasian populations approximately 15 thousand years ago parsimoniously explains the spectrum of extant ALMS1 variation. These results provide new insights into the evolutionary history of ALMS1 in humans and suggest that selective events identified in genomewide scans may be more complex than currently appreciated.

Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis.

BACKGROUND: The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, most studies have searched for eQTL by analyzing gene expression traits one at a time. As thousands of expression traits are typically analyzed, this can reduce power because of the need to correct for the number of hypothesis tests performed. In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually. RESULTS: To address these issues, we applied two different multivariate dimension reduction techniques, the Singular Value Decomposition (SVD) and Independent Component Analysis (ICA) to gene expression traits derived from a cross between two strains of Saccharomyces cerevisiae. Both methods decompose the data into a set of meta-traits, which are linear combinations of all the expression traits. The meta-traits were enriched for several Gene Ontology categories including metabolic pathways, stress response, RNA processing, ion transport, retro-transposition and telomeric maintenance. Genome-wide linkage analysis was performed on the top 20 meta-traits from both techniques. In total, 21 eQTL were found, of which 11 are novel. Interestingly, both cis and trans-linkages to the meta-traits were observed. CONCLUSION: These results demonstrate that dimension reduction methods are a useful and complementary approach for probing the genetic architecture of gene expression variation.

Genomic insights into positive selection.

The traditional way of identifying targets of adaptive evolution has been to study a few loci that one hypothesizes a priori to have been under selection. This approach is complicated because of the confounding effects that population demographic history and selection have on patterns of DNA sequence variation. In principle, multilocus analyses can facilitate robust inferences of selection at individual loci. The deluge of large-scale catalogs of genetic variation has stimulated many genome-wide scans for positive selection in several species. Here, we review some of the salient observations of these studies, identify important challenges ahead, consider the limitations of genome-wide scans for selection and discuss the potential significance of a comprehensive understanding of genomic patterns of selection for disease-related research.