Exome sequencing of Finnish isolates enhances rare-variant association power

Clinical profiles and diagnostic challenges in 1158 children with rare hepatobiliary disorders

BACKGROUND

Diagnosis of rare diseases possesses a great challenge in pediatric hepatology because expert knowledge in the field is extremely insufficient. The study aims to explore new findings and collect diagnostic experience from pediatric rare liver diseases.

METHODS

The large-sample case analysis study included pediatric patients who had liver-involved rare diseases. All cases underwent liver biopsy and/or gene sequencing.

RESULTS

A total of 1158 pediatric patients were identified. Liver-based genetic diseases were most frequent (737 cases), followed by liver damages involved in extrahepatic or systemic disorders (151 cases) and cryptogenic hepatobilliary abnormalities (123 cases). Of note, diagnoses of 16 patients were re-evaluated according to genetic results combined with clinical pointers. In addition, 101 patients who underwent gene sequencing remained undiagnosed. Of them, 55 had negative genetic findings, 30 harbored mutations that failed to meet their typically pathogenic condition, and 16 had detected variants that were inconsistent with clinical pointers.

CONCLUSIONS

As a study involving known largest number of children with rare hepatobiliary disorders, it allows us to accumulate information (especially new findings) on the etiology and diagnosis of these disorders. The results can help to improve the diagnostic quality in the population.

IMPACT

Liver-based genetic diseases were most frequent in clinical profiles of pediatric rare liver diseases. Some novel variants in cases with genetic diseases (for example, two variants of c.3638G>T and c.1435G>C in a patient with progressive familial intrahepatic cholestasis type 2) were identified. As a study involving known largest number of pediatric cases with rare hepatobiliary disorders, it allows us to accumulate information on the etiology and diagnosis of these disorders. The study can help to optimize the diagnostic process and significantly improve the diagnostic quality in the field of pediatric hepatology. Given that clinical variability often exists within rare genetic disease entities and not all rare disorders are genetic, clinicians should not over-depend on the genetic results in the diagnosis.

Mendelian Randomization Study on Amino Acid Metabolism Suggests Tyrosine as Causal Trait for Type 2 Diabetes

Circulating levels of branched-chain amino acids, glycine, or aromatic amino acids have been associated with risk of type 2 diabetes. However, whether those associations reflect causal relationships or are rather driven by early processes of disease development is unclear. We selected diabetes-related amino acid ratios based on metabolic network structures and investigated causal effects of these ratios and single amino acids on the risk of type 2 diabetes in two-sample Mendelian randomization studies. Selection of genetic instruments for amino acid traits relied on genome-wide association studies in a representative sub-cohort (up to 2265 participants) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study and public data from genome-wide association studies on single amino acids. For the selected instruments, outcome associations were drawn from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis, 74,124 cases and 824,006 controls) consortium. Mendelian randomization results indicate an inverse association for a per standard deviation increase in ln-transformed tyrosine/methionine ratio with type 2 diabetes (OR = 0.87 (0.81-0.93)). Multivariable Mendelian randomization revealed inverse association for higher log10-transformed tyrosine levels with type 2 diabetes (OR = 0.19 (0.04-0.88)), independent of other amino acids. Tyrosine might be a causal trait for type 2 diabetes independent of other diabetes-associated amino acids.

Host genetics and infectious disease: new tools, insights and translational opportunities

Understanding how human genetics influence infectious disease susceptibility offers the opportunity for new insights into pathogenesis, potential drug targets, risk stratification, response to therapy and vaccination. As new infectious diseases continue to emerge, together with growing levels of antimicrobial resistance and an increasing awareness of substantial differences between populations in genetic associations, the need for such work is expanding. In this Review, we illustrate how our understanding of the host–pathogen relationship is advancing through holistic approaches, describing current strategies to investigate the role of host genetic variation in established and emerging infections, including COVID-19, the need for wider application to diverse global populations mirroring the burden of disease, the impact of pathogen and vector genetic diversity and a broad array of immune and inflammation phenotypes that can be mapped as traits in health and disease. Insights from study of inborn errors of immunity and multi-omics profiling together with developments in analytical methods are further advancing our knowledge of this important area.

Infectious diseases are an ever-present global threat. In this Review, Kwok, Mentzer and Knight discuss our latest understanding of how human genetics influence susceptibility to disease. Furthermore, they discuss emerging progress in the interplay between host and pathogen genetics, molecular responses to infection and vaccination, and opportunities to bring these aspects together for rapid responses to emerging diseases such as COVID-19.

Associations of the serum metabolite profile with a healthy Nordic diet and risk of coronary artery disease

BACKGROUND & AIM

A healthy Nordic diet (HND) rich in wholegrain cereals, berries, vegetables, and fish, has been associated with a lower risk of cardiovascular disease, but the molecular links remain unclear. Here, we present the application of nontargeted metabolic profiling based on liquid chromatography with tandem mass spectrometry (LC-MS/MS) to identify metabolites that would potentially reflect the adherence to HND and their relationship with the risk of coronary artery disease (CAD).

METHODS

From a Finnish population-based prospective cohort (Kuopio Ischaemic Heart Disease Risk Factor Study; KIHD), we collected 364 baseline serum samples in 4 groups: 1) 94 participants with high adherence to HND who developed CAD during the follow-up of 20.4 ± 7.6 years (cases), 2) 88 participants with high adherence who did not develop CAD during follow-up (controls), 3) 93 CAD cases with low adherence, and 4) 89 controls with low adherence.

RESULTS

Indolepropionic acid, proline betaine, vitamin E derivatives, and medium-chain acylcarnitines were associated with adherence to HND after adjustments for age, waist-to-hip ratio (WHR), physical activity, and total cholesterol. These metabolites also correlated negatively with blood lipid profiles, BMI, insulin, inflammation marker high-sensitivity C reactive protein (hsCRP), smoking, and alcohol consumption, as well as positively with physical activity. Predictors of CAD risk included several lipid molecules, which also indicated lower adherence to HND. But, only the associations with the plasmalogens PC(O-16:0/18:2) and PC(O-16:1/18:2) remained significant after adjusting for age, smoking, systolic blood pressure, LDL cholesterol, and WHR. These plasmalogens did not correlate with any investigated risk factors of CAD at baseline, which may highlight their potential as novel predictors of CAD risk. Interestingly, the metabolic profile predicting CAD risk differed based on the adherence to HND. Also, HND adherence was more distinct within CAD cases than controls, which may emphasize the interaction between HND adherence and CAD risk.

CONCLUSIONS

The association between higher adherence to HND and a lower risk of CAD likely involves a complex interaction of various endogenous, plant-, and microbial-derived metabolites.

Association of Body Mass Index with Fecal Microbial Diversity and Metabolites in the Northern Finland Birth Cohort

BACKGROUND

Obesity is an established risk factor for multiple cancer types. Lower microbial richness has been linked to obesity, but human studies are inconsistent, and associations of early-life body mass index (BMI) with the fecal microbiome and metabolome are unknown.

METHODS

We characterized the fecal microbiome (n = 563) and metabolome (n = 340) in the Northern Finland Birth Cohort 1966 using 16S rRNA gene sequencing and untargeted metabolomics. We estimated associations of adult BMI and BMI history with microbial features and metabolites using linear regression and Spearman correlations (rs ) and computed correlations between bacterial sequence variants and metabolites overall and by BMI category.

RESULTS

Microbial richness, including the number of sequence variants (rs = -0.21, P < 0.0001), decreased with increasing adult BMI but was not independently associated with BMI history. Adult BMI was associated with 56 metabolites but no bacterial genera. Significant correlations were observed between microbes in 5 bacterial phyla, including 18 bacterial genera, and metabolites in 49 of the 62 metabolic pathways evaluated. The genera with the strongest correlations with relative metabolite levels (positively and negatively) were Blautia, Oscillospira, and Ruminococcus in the Firmicutes phylum, but associations varied by adult BMI category.

CONCLUSIONS

BMI is strongly related to fecal metabolite levels, and numerous associations between fecal microbial features and metabolite levels underscore the dynamic role of the gut microbiota in metabolism.

IMPACT

Characterizing the associations between the fecal microbiome, the fecal metabolome, and BMI, both recent and early-life exposures, provides critical background information for future research on cancer prevention and etiology.

Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification

The integration of transcriptomic studies and genome-wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) can lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.

Characterization of Exome Variants and Their Metabolic Impact in 6,716 American Indians from the Southwest US

Applying exome sequencing to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from a Southwestern American Indian (SWAI) population with well-characterized metabolic traits. We found that the SWAI population has distinct allelic architecture compared to populations of European and East Asian ancestry, and there were many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in the SWAI population. We used pLOF and nonsynonymous variants in the SWAI population to evaluate gene-burden associations of candidate genes from European genome-wide association studies (GWASs) for type 2 diabetes, body mass index, and four major plasma lipids. We found 19 significant gene-burden associations for 11 genes, providing additional evidence for prioritizing candidate effector genes of GWAS signals. Interestingly, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in the SWAI population. Particularly, we found four pLOF or nonsynonymous variants in APOB, APOE, PCSK9, and TM6SF2 that are private or enriched in the SWAI population and associated with low-density lipoprotein (LDL) cholesterol levels. Their large estimated effects on LDL cholesterol levels suggest strong impacts on protein function and potential clinical implications of these variants in cardiovascular health. In summary, our study illustrates the utility and potential of exome sequencing in genetically unique populations, such as the SWAI population, to prioritize candidate effector genes within GWAS loci and to find additional variants in known disease genes with potential clinical impact.

Evidence of Polygenic Adaptation in Sardinia at Height-Associated Loci Ascertained from the Biobank Japan

Adult height is one of the earliest putative examples of polygenic adaptation in humans. However, this conclusion was recently challenged because residual uncorrected stratification from large-scale consortium studies was considered responsible for the previously noted genetic difference. It thus remains an open question whether height loci exhibit signals of polygenic adaptation in any human population. We re-examined this question, focusing on one of the shortest European populations, the Sardinians, in addition to mainland European populations. We utilized height-associated loci from the Biobank Japan (BBJ) dataset to further alleviate concerns of biased ascertainment of GWAS loci and showed that the Sardinians remain significantly shorter than expected under neutrality (∼0.22 standard deviation shorter than Utah residents with ancestry from northern and western Europe [CEU] on the basis of polygenic height scores, p = 3.89 × 10-4). We also found the trajectory of polygenic height scores between the Sardinian and the British populations diverged over at least the last 10,000 years (p = 0.0082), consistent with a signature of polygenic adaptation driven primarily by the Sardinian population. Although the polygenic score-based analysis showed a much subtler signature in mainland European populations, we found a clear and robust adaptive signature in the UK population by using a haplotype-based statistic, the trait singleton density score (tSDS), driven by the height-increasing alleles (p = 9.1 × 10-4). In summary, by ascertaining height loci in a distant East Asian population, we further supported the evidence of polygenic adaptation at height-associated loci among the Sardinians. In mainland Europeans, the adaptive signature was detected in haplotype-based analysis but not in polygenic score-based analysis.

Genetic Associations of Chronotype in the Finnish General Population

Individuals with a later chronotype (evening types) tend to have unhealthier behaviors and increased morbidity and mortality as compared with those with an earlier chronotype (morning types). However, the role of genetics in explaining evening types' adverse health and health behavior is unclear. Our aim was to study genetic associations of chronotype among 8433 Finns from the cross-sectional National FINRISK 2007 and 2012 studies. First, we studied associations between chronotype and 20 key clock genes with a candidate-gene approach and then performed a full genome-wide association study (GWAS) of chronotype. We also developed a genetic risk score (GRS) for chronotype based on 313 single nucleotide polymorphisms (SNPs) that have previously been associated with chronotype. Chronotype was assessed with a shortened version of Horne and Östberg's Morningness-Eveningness Questionnaire (sMEQ), and for comparison, we also used the single self-evaluation question on chronotype from the questionnaire. Linear and logistic regression was used for statistical analysis assuming additive effects. The clock gene analysis revealed 1 independent association signal within NR1D2 (lead SNP rs4131403) that was associated with chronotype (p < 0.05; as based on both chronotype assessment methods). The GWAS analysis did not yield any genome-wide significant associations (p > 5 × 10^-8). However, higher GRS was associated with evening chronotype (p < 0.001; as based on both chronotype assessment methods). In conclusion, our findings indicated novel genetic associations between chronotype and the NR1D2 clock gene, which has previously been associated with carbohydrate and lipid metabolism. Furthermore, the GRS was able to capture the genetic aspect of chronotype in our study population. These findings expand our knowledge of the genetic basis of chronotype.

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets.

Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort

The HLA genes, the most polymorphic genes in the human genome, constitute the strongest single genetic susceptibility factor for autoimmune diseases, transplantation alloimmunity and infections. HLA imputation via statistical inference of alleles based on single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with alleles is a powerful first-step screening tool. Due to different LD structures between populations, the accuracy of HLA imputation may benefit from matching the imputation reference with the study population. To evaluate the potential advantage of using population-specific reference in HLA imputation, we constructed an HLA reference panel consisting of 1150 Finns with 5365 major histocompatibility complex region SNPs consistent between genome builds. We evaluated the accuracy of the panel against a European panel in an independent test set of 213 Finnish subjects. We show that the Finnish panel yields a lower imputation error rate (1.24% versus 1.79%). More than 30% of imputation errors occurred in haplotypes enriched in Finland. The frequencies of imputed HLA alleles were highly correlated with clinical-grade HLA allele frequencies and allowed accurate replication of established HLA–disease associations in ∼102 000 biobank participants. The results show that a population-specific reference increases imputation accuracy in a relatively isolated population within Europe and can be successfully applied to biobank-scale genome data collections.

Electronic health records and polygenic risk scores for predicting disease risk

Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.

Disparities in glioblastoma survival by case volume: a nationwide observational study

Introduction

High hospital case volumes are associated with improved treatment outcomes for numerous diseases. We assessed the association between academic non-profit hospital case volume and survival of adult glioblastoma patients.

Methods

From the nationwide Finnish Cancer Registry, we identified all adult (≥ 18 years) patients with histopathological diagnoses of glioblastoma from 2000 to 2013. Five university hospitals (treating all glioblastoma patients in Finland) were classified as high-volume (one hospital), middle-volume (one hospital), and low-volume (three hospitals) based on their annual numbers of cases. We estimated one-year survival rates, estimated median overall survival times, and compared relative excess risk (RER) of death between high, middle, and low-volume hospitals.

Results

A total of 2,045 patients were included. The mean numbers of annually treated patients were 54, 40, and 17 in the high, middle, and low-volume hospitals, respectively. One-year survival rates and median survival times were higher and longer in the high-volume (39%, 9.3 months) and medium-volume (38%, 8.9 months) hospitals than in the low-volume (32%, 7.8 months) hospitals. RER of death was higher in the low-volume hospitals than in the high-volume hospital (RER = 1.19, 95% CI 1.07–1.32, p = 0.002). There was no difference in RER of death between the high-volume and medium-volume hospitals (p = 0.690).

Conclusion

Higher glioblastoma case volumes were associated with improved survival. Future studies should assess whether this association is due to differences in patient-specific factors or treatment quality.

Electronic supplementary material

The online version of this article (10.1007/s11060-020-03428-5) contains supplementary material, which is available to authorized users.

Two novel mutations of COL1A1 in fetal genetic skeletal dysplasia of Chinese

Background

Skeletal disorders, which have great genotypic and phenotypic varieties, are a considerable challenge to differentiate these diseases and provide a definitive prenatal diagnosis or pre‐implantation. The present study aims to identify the causative mutation in two unrelated outbred Han–Chinese families.

Method

Two short‐limb fetuses were referred to our hospital. Genomic DNA was extracted from the amniotic fluid of the short‐limb fetuses and from peripheral blood of their parents. To identify the causative gene, next‐generation‐based target capture sequencing was performed on these two fetuses, followed by Sanger Sequencing in unrelated healthy controls. Segregation analysis of the candidate variant was performed in parents by using Sanger sequencing. The mutations were analyzed by SIFT, PolyPhen and Provean.

Results

We found that fetal genetic skeletal dysplasia was confirmed according to the correlations between genetic mutations and phenotypes in two Chinese families. Targeted next generation sequencing was performed to screen causative mutations in patients. Two novel heterozygous mutations COL1A1 c.1706 G > C (p. G569A) and c.3307 G > A (p. G1103S) were respectively identified. The results suggested that COL1A1 novel mutations were in highly conserved glycine residues present in the Gly‐X‐Y sequence repeats of the triple helical region of the collagen type I α chain, which was responsible for Osteogenesis Imperfecta. The presence of the missense mutation was also confirmed with the Sanger sequence. These two mutations were predicted to be pathogenic by SIFT, PolyPhen and Provean.

Conclusion

Our findings showed that the mutations of COL1A1 may play important roles in fetal genetic skeletal dysplasia in Chinese patients. Exome sequencing enhances the accurate diagnosis in utero then provides appropriate genetic counseling.

Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease

BACKGROUND AND AIMS

Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts.

METHODS

We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles.

RESULTS

The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour.

CONCLUSIONS

These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment.

The GenomeAsia 100K Project enables genetic discoveries across Asia

The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world’s population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide.

Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide.

Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions

The excessive hospital mortality associated with acute respiratory distress syndrome (ARDS) in adults mandates an urgent need for developing new therapies and tools for the early risk assessment of these patients. ARDS is a heterogeneous syndrome with multiple different pathogenetic processes contributing differently in different patients depending on clinical as well as genetic factors. Identifying genetic-based biomarkers holds the promise for establishing effective predictive and prognostic stratification methods and for targeting new therapies to improve ARDS outcomes. Here we provide an updated review of the available evidence supporting the presence of genetic factors that are predictive of ARDS development and of fatal outcomes in adult critically ill patients and that have been identified by applying different genomic and genetic approaches. We also introduce other incipient genomics approximations, such as admixture mapping, metagenomics and genome sequencing, among others, that will allow to boost this knowledge and likely reveal new genetic predictors of ARDS susceptibility and prognosis among critically ill patients.