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Collaborators (845): Astrid M. Vicente Free PMC article:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905/ Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI. info:eu-repo/semantics/publishedVersion

Kim et al. identify novel genes and disease pathways in the forebrain developmental disorder holoprosencephaly, and show that many cases involve oligogenic inheritance. The findings underline the roles of Sonic Hedgehog and primary cilia in forebrain development, and show that integrating clinical phenotyping into genetic studies can uncover relevant mutations.Holoprosencephaly is a pathology of forebrain development characterized by high phenotypic heterogeneity. The disease presents with various clinical manifestations at the cerebral or facial levels. Several genes have been implicated in holoprosencephaly but its genetic basis remains unclear: different transmission patterns have been described including autosomal dominant, recessive and digenic inheritance. Conventional molecular testing approaches result in a very low diagnostic yield and most cases remain unsolved. In our study, we address the possibility that genetically unsolved cases of holoprosencephaly present an oligogenic origin and result from combined inherited mutations in several genes. Twenty-six unrelated families, for whom no genetic cause of holoprosencephaly could be identified in clinical settings [whole exome sequencing and comparative genomic hybridization (CGH)-array analyses], were reanalysed under the hypothesis of oligogenic inheritance. Standard variant analysis was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis. Statistical validation was performed on 248 ancestrally similar control trios provided by the Genome of the Netherlands project and on 574 ancestrally matched controls provided by the French Exome Project. Variants of clinical interest were identified in 180 genes significantly associated with key pathways of forebrain development including sonic hedgehog (SHH) and primary cilia. Oligogenic events were observed in 10 families and involved both known and novel holoprosencephaly genes including recurrently mutated FAT1, NDST1, COL2A1 and SCUBE2. The incidence of oligogenic combinations was significantly higher in holoprosencephaly patients compared to two control populations (P

To identify new genetic risk factors for rheumatoid arthritis, we conducted a genome-wide association study meta-analysis of 5,539 autoantibody-positive individuals with rheumatoid arthritis (cases) and 20,169 controls of European descent, followed by replication in an independent set of 6,768 rheumatoid arthritis cases and 8,806 controls. Of 34 SNPs selected for replication, 7 new rheumatoid arthritis risk alleles were identified at genome-wide significance (P < 5 x 10(-8)) in an analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5 and PXK. We also refined associations at two established rheumatoid arthritis risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed rheumatoid arthritis risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P < 0.05, many of which are validated autoimmune risk alleles, suggesting that most represent genuine rheumatoid arthritis risk alleles.

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ~155,063 samples for independent replication. We identified 30 new blood pressure– or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention. N.P. has received financial support from several pharmaceutical companies that manufacture either blood pressure -lowering or lipid-lowering agents, or both, and consultancy fees. S.K. has received research grants from Merck, Bayer and Aegerion, is on the SAB of Catabasis, Regeneron Genetics Center, Merck and Celera, has equity in San Therapeutics and Catabasis, and performs consulting for Novartis, Aegerion, Bristol Myers Squibb, Sanofi, AstraZeneca and Alnylam. P. Sever has received research awards from Pfizer. A. Malarstig and M.U.-N. are full-time employees of Pfizer. D.F.R. and M. Hoek are full-time employees of Merck. M.J.C. is Chief Scientist for Genomics England, a UK government company. The authors declare no other competing financial interests.

Deoxyribonucleic acid (DNA) is made up of four bases: adenine (A), cytosine (C), guanine (G), and thymine (T). Assembled in a strategic fashion, these bases code for the unique genomes of all walks of life, from viruses, to rodents, to primates. The human genome, mapped completely for the first time in April of 2003, is 3 billion bases long and contains approximately 20,000 genes. The genome of a randomly selected individual, checked base by base, will be approximately 99.9% identical to the reference map of the human genome. The other 0.1% (roughly 3 million bases total) will contain changes in the DNA that do not match to the canonical reference genome. These changes, or genetic variants, can have biological consequence, as they can impact the shape and function of the proteins for which genes code. Genetic variation accounts for much of the visible (phenotypic) diversity – such as eye color or height – that can be observed in humans today. Such variation also plays a role in susceptibility to disease in humans. In the last ten years, a major breakthrough in identifying the genetic changes that increase risk of disease has been genome-wide association studies (GWAS). In these studies, genetic variants (called single-nucleotide polymorphisms, or SNPs) observed in individuals with a particular trait or disorder (affected individuals) are compared to genetic variants observed in healthy (unaffected) individuals. SNPs that are observed more often in affected individuals than in unaffected individuals are associated to increased disease risk. They mark regions (called “loci”, singular “locus”) of the genome that can contain clues about a disease. Knowing which genes in a locus, when mutated, can increase risk of disease is a crucial first step in understanding why certain people are susceptible to disease while others are not. To date, GWAS have identified more than 7,000 loci that influence risk of disease. The primary goal of the work in this thesis is to identify genetic variants associated to risk of common disease. Finding such mutations can help identify disease-causing genes, potentially improving our ability to prevent, properly diagnose or treat illness in humans.

Most human traits, ranging from physical appearance to behavior and disease susceptibility, are in part inherited through genetic material. Whole-genome sequencing has enabled the complete characterization of human genetic variation. While most of common DNA sequence variation has been observed in genetic studies from worldwide populations, rare genetic variation is more geographically clustered and requires many more individuals from diverse populations to be studied. In this thesis I describe the genetic variation in 250 Dutch parent-offspring families from the Genome of the Netherlands (GoNL) Project obtained through whole-genome sequencing. A total of 20.4 million single nucleotide variants (SNVs), 1.2 million short insertions and deletions (indels) and 27.5 thousands structural variants (SVs) were discovered in these families. While most of the SNVs were known, the majority of the indels and almost all SVs are novel, partly due to the ability to identify mid-size deletions of size 30bp-500bp for the first time on a population scale. Taking advantage of the trio design, the SNVs and indels were phased into a highly accurate haplotype panel, which improves imputation accuracy especially for lower allele frequency alleles. In addition to describing the inherited DNA sequence variation in the Dutch population, I was also able to characterize de novo mutations at an unprecedented scale. Indeed, 11,020 de novo SNVs, 291 de novo indels and 41 de novo SVs were identified, from which a mutation rate of 1.15 x 10-8 SNVs/bp, 0.68 x 10-9 indels/bp and 0.16 SVs per generation can be estimated. Despite their much lower rate, de novo SVs affect 91 times more bases on average, including 52 times more protein coding bases, than de novo SNVs. This is in contrast with the relatively similar footprint of inherited SNVs and SVs, likely indicating much stronger selection pressure on SVs than on SNVs. Looking at the distribution of mutations across offspring, I confirmed the previously reported increase of de novo SNVs with paternal age and furthermore showed that paternal age also influences their chromosomal location. In particular, de novo SNVs in offspring of older fathers occur more frequently in early-replicating, genic regions than those in offspring of younger fathers. Although there was no significant association between father’s age and rates of de novo indels and SVs (possibly due to limited detection power), both of these variant types are also enriched in the paternal germline. De novo SNVs clustered significantly within individuals at distances up to 20kbp. Mutations in these clusters represent 1.5% of the SNVs and exhibit a unique mutational spectrum, which may point to a novel mutational mechanism. Local SNV mutation rates vary across genomes and are elevated in functional regions due to CpG dinucleotide context influences. Transcribed regions exhibit a pronounced strand asymmetry compatible with the action of transcription-coupled repair. On larger scales, I observed that SNV mutation and recombination rates independently associate with nucleotide diversity, and that variation in substitution rates based on species divergence is only partly explained by mutation rate heterogeneity. Overall, this thesis presents key results of large-scale sequencing data collected within a single population. The parent-offspring design has proved beneficial, allowing in-depth characterization of common, rare and de novo SNVs, indels and SVs. Analyses of these data have revealed novel insights into inherited and de novo variation in human genomes.

Recent advances in genetic knowledge, bioinformatics and technology have transformed host genetic research and allowed comprehensive studies of the human genome. Genome-wide association studies based on genotyping arrays have been considered the approach of choice to search for host factors involved in the pathogenesis of chronic viral infections, and to assess their relative contribution. The first part of the thesis describes GWAS of various phenotypes related to HIV infection. Studies of spontaneous viral control affirmed the key role of the HLA region, in modulating viral load and disease progression. We also redefined the known associations between CCR5Δ32 - a 32 base pair deletion in CCR5 - and viral control (in heterozygous individuals), and with HIV resistance (in homozygous individuals). GWAS of HIV acquisition did not identify new associated variant, implying that common genetic polymorphisms with moderate to high effect size do not appreciably impact HIV susceptibility. An additional GWAS looked at T cell responses to an experimental HIV vaccine, showing that the same HLA alleles are implicated in vaccine induced cellular immunity and in natural immune control. Finally, we used variation in the viral genome as phenotype in multiple parallel GWAS. This method allowed the establishment of the first global map of human-HIV genomic interactions, by identifying the areas of the human genome that put pressure on HIV, and the regions of the virus that serve to escape human control. The second part of the thesis presents GWAS performed in patients treated for chronic HCV infection, and investigating the efficacy and toxicity of peg-interferon alpha and ribavirin treatment. Interferon-lambda 3 (encoded by IL28B) was identified as the key determinant of treatment response and ITPA deficiency was shown to play a major protective role against drug-induced anemia. These discoveries were considered major breakthroughs in virology and hepatology: by highlighting host factors that are responsible for differences in pathogenesis, they facilitated the development of diagnostic and therapeutic strategies. The studies presented here provide a detailed description of how common variation influences control of HIV and HCV infections. Still, most of the inter-individual variability remains to be explained. Sequencing strategies will prove essential to better appreciate the global contribution of the human genome to phenotypic outcomes. The ongoing historical transition into the genomic era brings new hopes that host genetics will contribute to a better understanding of viral infections, to the identification of new targets for drug and vaccine development, and to the creation of predictive tools that will make personalized medicine a reality in infectious diseases.

In a genome-wide association study (GWAS), a large number of SNPs are genotyped in a large number of cases (with disease) and controls (without disease) using commercially available high-throughput genotyping platforms. In a GWAS, the genotype data collected in cases and unaffected, population-matched controls are compared. Other than identifying individuals with the disease, there is no requirement for prior biological knowledge of the trait under investigation. This analysis approach has discovered several thousand genetic loci reliably associated to a range of complex phenotypes and diseases. Psychiatric diseases are characterized by affective, behavioral, and cognitive abnormalities. The majority of psychiatric diseases share a common feature, namely that family history of disease is a major risk factor, which is suggestive of a strong genetic component to the underlying disease risk. Epidemiological studies have estimated high heritability values of up to 80% for many common psychiatric diseases, such as schizophrenia and bipolar disorder. Psychiatric research to date has uncovered little about the underlying biology of disease. Commonly used tools in biomedical research, such as suitable in vitro and in vivo model systems, are essentially absent. This makes GWAS the perfect tool for gaining more fundamental insight into the etiology of psychiatric diseases. Moreover, large-scale genotyping or whole-genome sequencing has become more affordable by today’s standards. Indeed it is now feasible to analyze tens of thousands of individuals. This is a crucial requirement because large data sets are necessary for a successful GWAS of complex diseases as the apparent effect sizes are small. In this thesis I have described initial results from GWAS for a range of psychiatric diseases. I have identified here which components have been critical for its success and discuss how we learned from both failures and successes. By far, it has become clear that the most important step to scientific progress in human genetics of complex traits is collaboration and exchange of data. In a world where data (in this case genotype data) becomes available in enormous amounts, and where computer power increases significantly via computer clusters and cloud computing, it has been essential to bring everybody together to produce robust findings. In the first half of this thesis (chapter 2-6) I presented GWAS for three single psychiatric diseases: major depressive disorder, bipolar disorder and schizophrenia. The most recent GWAS for schizophrenia, presented in Chapter 6 builds the central piece of this thesis. Here we could demonstrate the remarkable success via extensive collaboration. The second half (chapters 7-9) concentrated on endophenotypes and cross-phenotypes with common variant analysis, where we attempted to dissect commonalities and differences between the classic psychiatric nosologies. In summary the analyses of common variants in psychiatric genetics is the main contributor to a huge scientific success that is in the process of changing the field with many robust genetic findings. It will continue to be a valuable source of information to the biology in psychiatry for the decades to come.