Genetic Risk Factors of Depression

Gene By Environment Interaction ◽

Genome Wide ◽

Candidate Gene Studies

Research on the genetic underpinnings of depression has rapidly advanced in the past decade. This field of research provides a promising avenue toward improving the diagnosis of, prevention of, and treatment for this devastating disorder. The goal of this chapter is to review the main genetic and gene-by-environment interaction findings on depression. We first describe family and twin studies used to empirically study the familial aggregation of depression. Second, we provide a review of the genome-wide association studies (GWAS) published to date. Building on GWAS findings, we will discuss the use of polygenic risk scores in predicting depression. We also review the most robust candidate gene studies and gene-by-environment interaction studies. Finally, we discuss the clinical implications of the findings and promising strategies for making further progress within this field.

Critical Analysis of Genome-Wide Association Studies: Triple Negative Breast Cancer Quae Exempli Causa

International Journal of Molecular Sciences ◽

10.3390/ijms21165835 ◽

2020 ◽

Vol 21 (16) ◽

pp. 5835

Author(s):

Maria-Ancuta Jurj ◽

Mihail Buse ◽

Alina-Andreea Zimta ◽

Angelo Paradiso ◽

Schuyler S. Korban ◽

...

Keyword(s):

Breast Cancer ◽

Risk Factors ◽

Genetic Risk ◽

Triple Negative ◽

Cancer Susceptibility ◽

Association Studies ◽

Breast Cancer Susceptibility ◽

Risk Scores ◽

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.

Familial Chiari malformation: case series

Neurosurgical FOCUS ◽

10.3171/2011.6.focus11104 ◽

2011 ◽

Vol 31 (3) ◽

pp. E1 ◽

Cited By ~ 29

Author(s):

Benjamin D. Schanker ◽

Brian P. Walcott ◽

Brian V. Nahed ◽

Kristopher T. Kahle ◽

Yan Michael Li ◽

...

Keyword(s):

Chiari Malformation ◽

Familial Aggregation ◽

Association Studies ◽

Case Reports ◽

Case Series ◽

Twin Studies ◽

Type I ◽

Chiari Malformation Type I ◽

Chiari malformations (Types I–IV) are abnormalities of the posterior fossa that affect the cerebellum, brainstem, and the spinal cord with prevalence rates of 0.1%–0.5%. Case reports of familial aggregation of Chiari malformation, twin studies, cosegregation of Chiari malformation with known genetic conditions, and recent gene and genome-wide association studies provide strong evidence of the genetic underpinnings of familial Chiari malformation. The authors report on a series of 3 family pairs with Chiari malformation Type I: 2 mother-daughter pairs and 1 father-daughter pair. The specific genetic causes of familial Chiari malformation have yet to be fully elucidated. The authors review the literature and discuss several candidate genes. Recent advances in the understanding of the genetic influences and pathogenesis of familial Chiari malformation are expected to improve management of affected patients and monitoring of at-risk family members.

Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin

Thorax ◽

10.1136/thoraxjnl-2020-215624 ◽

2021 ◽

pp. thoraxjnl-2020-215624

Author(s):

Sinjini Sikdar ◽

Annah B Wyss ◽

Mi Kyeong Lee ◽

Thanh T Hoang ◽

Marie Richards ◽

...

Keyword(s):

Pulmonary Function ◽

Genetic Risk ◽

Association Studies ◽

European Ancestry ◽

Risk Scores ◽

P Value ◽

Inverse Association ◽

Genetic Risk Scores ◽

RationaleGenome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene–environment interactions, but studies are few.MethodsWe analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case–control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV1, FVC and FEV1/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10−9) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r2=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions.ResultsEach trait was highly significantly associated with its GRS (all three p values<8.9×10−8). The inverse association of the GRS with FEV1/FVC was stronger for current smokers (pinteraction=0.017) or former smokers (pinteraction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (pinteraction=0.053). No significant interactions were observed between any GRS and house dust endotoxin.ConclusionsEvaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.

A Brief Review of Genetic Approaches to the Study of Food Preferences: Current Knowledge and Future Directions

Nutrients ◽

10.3390/nu11081735 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1735 ◽

Cited By ~ 5

Author(s):

Robino ◽

Concas ◽

Catamo ◽

Gasparini

Keyword(s):

Current Knowledge ◽

Association Studies ◽

Food Selection ◽

Food Preferences ◽

Twin Studies ◽

Future Directions ◽

Genome Wide ◽

Assessment Approach ◽

Candidate Gene Studies

Genetic variation plays a crucial role in individual differences in food preferences which ultimately influence food selection and health. Our current understanding of this pathway has been informed through twin studies (to assess the heritability of food preferences), candidate gene studies, and genome-wide association studies (GWAS). However, most of this literature is mainly focused on genes previously identified as having taste or smell functions. New data suggests that genes not associated with taste or smell perception may be involved in food preferences and contribute to health outcomes. This review highlights these emerging findings and suggests a polygenic risk assessment approach to explore new relationships between food preferences and health risks.

Genome-wide by environment interaction studies (GWEIS) of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

10.1101/479691 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aleix Arnau-Soler ◽

Erin Macdonald-Dunlop ◽

Mark J. Adams ◽

Toni-Kim Clarke ◽

Donald J. MacIntyre ◽

...

Keyword(s):

Depressive Symptoms ◽

Association Studies ◽

Joint Effect ◽

Risk Scores ◽

Environment Interaction ◽

Uk Biobank ◽

Interaction Studies ◽

Genome Wide ◽

Generation Scotland

ABSTRACTStress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77×10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53kb downstream PIWIL4; p = 4.95×10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46×10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00×10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77×10-6). Polygenic risk scores (PRS) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91×10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.

Multiple Metabolic Genetic Risk Scores and Type 2 Diabetes Risk in Three Racial/Ethnic Groups

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2014-1818 ◽

2014 ◽

Vol 99 (9) ◽

pp. E1814-E1818 ◽

Cited By ~ 13

Author(s):

Yann C. Klimentidis ◽

Nathan E. Wineinger ◽

Ana I. Vazquez ◽

Gustavo de los Campos

Keyword(s):

Genetic Risk ◽

Association Studies ◽

Risk Scores ◽

Racial Groups ◽

European Americans ◽

Genome Wide ◽

Meta Analyses ◽

Racial Ethnic

Context/Rationale: Meta-analyses of genome-wide association studies have identified many single-nucleotide polymorphisms associated with various metabolic and cardiovascular traits, offering us the opportunity to learn about and capitalize on the links between cardiometabolic traits and type 2 diabetes (T2D). Design: In multiple datasets comprising over 30 000 individuals and 3 ethnic/racial groups, we calculated 17 genetic risk scores (GRSs) for glycemic, anthropometric, lipid, hemodynamic, and other traits, based on the results of recent trait-specific meta-analyses of genome-wide association studies, and examined associations with T2D risk. Using a training-testing procedure, we evaluated whether additional GRSs could contribute to risk prediction. Results: In European Americans, we find that GRSs for T2D, fasting glucose, fasting insulin, and body mass index are associated with T2D risk. In African Americans, GRSs for T2D, fasting insulin, and waist-to-hip ratio are associated with T2D. In Hispanic Americans, GRSs for T2D and body mass index are associated with T2D. We observed a trend among European Americans suggesting that genetic risk for hyperlipidemia is inversely associated with T2D risk. The use of additional GRSs resulted in only small changes in prediction accuracy in multiple independent validation datasets. Conclusions: The analysis of multiple GRSs can shed light on T2D etiology and how it varies across ethnic/racial groups. Our findings using multiple GRSs are consistent with what is known about the differences in T2D pathogenesis across racial/ethnic groups. However, further work is needed to understand the putative inverse correlation of genetic risk for hyperlipidemia and T2D risk and to develop ethnic-specific GRSs.

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores

10.1101/2020.01.15.907808 ◽

2020 ◽

Author(s):

Brooks Paige ◽

James Bell ◽

Aurélien Bellet ◽

Adrià Gascón ◽

Daphne Ezer

Keyword(s):

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

Risk Scores ◽

Genome Database ◽

Genomic Databases ◽

Aggregate Information ◽

Genome Wide ◽

The Uk

AbstractSome organisations like 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies (GWAS). Even research studies that compile smaller genomic databases often utilise these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication. Here we show that under some circumstances, these GRS models can be used to recover the genetic variants of individuals in these genomic databases—a reconstruction attack. In particular, if two GRS models are trained using a largely overlapping set of participants, then it is often possible to determine the genotype for each of the individuals who were used to train one GRS model, but not the other. We demonstrate this theoretically and experimentally by analysing the Cornell Dog Genome database. The accuracy of our reconstruction attack depends on how accurately we can estimate the rate of co-occurrence of pairs of SNPs within the private database, so if this aggregate information is ever released, it would drastically reduce the security of a private genomic database. Caution should be applied when using the same database for multiple analysis, especially when a small number of individuals are included or excluded from one part of the study.

Genetic determinants of glucose levels in pregnancy: genetic risk scores analysis and GWAS in the Norwegian STORK cohort

Acta Endocrinologica ◽

10.1530/eje-18-0478 ◽

2018 ◽

Vol 179 (6) ◽

pp. 363-372 ◽

Cited By ~ 2

Author(s):

Gunn-Helen Moen ◽

Marissa LeBlanc ◽

Christine Sommer ◽

Rashmi B Prasad ◽

Tove Lekva ◽

...

Keyword(s):

Pregnant Women ◽

Genetic Risk ◽

Association Studies ◽

Risk Scores ◽

Oral Glucose ◽

Shape Information ◽

Genetic Risk Scores ◽

Genome Wide ◽

In Pregnancy

Objective Hyperglycaemia during pregnancy increases the risk of adverse health outcomes in mother and child, but the genetic aetiology is scarcely studied. Our aims were to (1) assess the overlapping genetic aetiology between the pregnant and non-pregnant population and (2) assess the importance of genome-wide polygenic contributions to glucose traits during pregnancy, by exploring whether genetic risk scores (GRSs) for fasting glucose (FG), 2-h glucose (2hG), type 2 diabetes (T2D) and BMI in non-pregnant individuals were associated with glucose measures in pregnant women. Methods We genotyped 529 Norwegian pregnant women and constructed GRS from known genome-wide significant variants and SNPs weakly associated (p > 5 × 10−8) with FG, 2hG, BMI and T2D from external genome-wide association studies (GWAS) and examined the association between these scores and glucose measures at gestational weeks 14–16 and 30–32. We also performed GWAS of FG, 2hG and shape information from the glucose curve during an oral glucose tolerance test (OGTT). Results GRSFG explained similar variance during pregnancy as in the non-pregnant population (~5%). GRSBMI and GRST2D explained up to 1.3% of the variation in the glucose traits in pregnancy. If we included variants more weakly associated with these traits, GRS2hG and GRST2D explained up to 2.4% of the variation in the glucose traits in pregnancy, highlighting the importance of polygenic contributions. Conclusions Our results suggest overlap in the genetic aetiology of FG in pregnant and non-pregnant individuals. This was less apparent with 2hG, suggesting potential differences in postprandial glucose metabolism inside and outside of pregnancy.

Association analysis of juvenile idiopathic arthritis genetic susceptibility factors in Estonian patients

Clinical Rheumatology ◽

10.1007/s10067-021-05756-x ◽

2021 ◽

Author(s):

Tiit Nikopensius ◽

Priit Niibo ◽

Toomas Haller ◽

Triin Jagomägi ◽

Ülle Voog-Oras ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Juvenile Idiopathic Arthritis ◽

Autoimmune Diseases ◽

Genetic Risk ◽

Association Studies ◽

Control Sample ◽

Case Control ◽

Genome Wide Association ◽

Abstract Background Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic condition of childhood. Genetic association studies have revealed several JIA susceptibility loci with the strongest effect size observed in the human leukocyte antigen (HLA) region. Genome-wide association studies have augmented the number of JIA-associated loci, particularly for non-HLA genes. The aim of this study was to identify new associations at non-HLA loci predisposing to the risk of JIA development in Estonian patients. Methods We performed genome-wide association analyses in an entire JIA case–control sample (All-JIA) and in a case–control sample for oligoarticular JIA, the most prevalent JIA subtype. The entire cohort was genotyped using the Illumina HumanOmniExpress BeadChip arrays. After imputation, 16,583,468 variants were analyzed in 263 cases and 6956 controls. Results We demonstrated nominal evidence of association for 12 novel non-HLA loci not previously implicated in JIA predisposition. We replicated known JIA associations in CLEC16A and VCTN1 regions in the oligoarticular JIA sample. The strongest associations in the All-JIA analysis were identified at PRKG1 (P = 2,54 × 10−6), LTBP1 (P = 9,45 × 10−6), and ELMO1 (P = 1,05 × 10−5). In the oligoarticular JIA analysis, the strongest associations were identified at NFIA (P = 5,05 × 10−6), LTBP1 (P = 9,95 × 10−6), MX1 (P = 1,65 × 10−5), and CD200R1 (P = 2,59 × 10−5). Conclusion This study increases the number of known JIA risk loci and provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis. The reported loci are involved in molecular pathways of immunological relevance and likely represent genomic regions that confer susceptibility to JIA in Estonian patients. Key Points• Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease with heterogeneous presentation and genetic predisposition.• Present genome-wide association study for Estonian JIA patients is first of its kind in Northern and Northeastern Europe.• The results of the present study increase the knowledge about JIA risk loci replicating some previously described associations, so adding weight to their relevance and describing novel loci.• The study provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis.

Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

Human Heredity ◽

10.1159/000513303 ◽

2021 ◽

pp. 1-11

Author(s):

Valentina Escott-Price ◽

Karl Michael Schmidt

Keyword(s):

Linkage Disequilibrium ◽

Association Studies ◽

Statistical Significance ◽

Ordinary Least Squares ◽

Effect Sizes ◽

Risk Scores ◽

Single Nucleotide ◽

Genome Wide ◽

Tikhonov Regularisation

Background: Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. Objectives: We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning. Methods: We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. Results: Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. Conclusions: There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.