GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

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Genetic Determinants of Paget’s Disease of Bone

Current Osteoporosis Reports ◽

10.1007/s11914-021-00676-w ◽

2021 ◽

Author(s):

Navnit S. Makaram ◽

Stuart H. Ralston

Keyword(s):

Genetic Factors ◽

Association Studies ◽

Paget’S Disease ◽

Paget's Disease ◽

Paget’S Disease Of Bone ◽

Genome Wide Association Studies ◽

Paget's Disease Of Bone ◽

Genome Wide ◽

Family Based

Abstract Purpose of Review To provide an overview of the role of genes and loci that predispose to Paget’s disease of bone and related disorders. Recent Findings Studies over the past ten years have seen major advances in knowledge on the role of genetic factors in Paget’s disease of bone (PDB). Genome wide association studies have identified six loci that predispose to the disease whereas family based studies have identified a further eight genes that cause PDB. This brings the total number of genes and loci implicated in PDB to fourteen. Emerging evidence has shown that a number of these genes also predispose to multisystem proteinopathy syndromes where PDB is accompanied by neurodegeneration and myopathy due to the accumulation of abnormal protein aggregates, emphasising the importance of defects in autophagy in the pathogenesis of PDB. Summary Genetic factors play a key role in the pathogenesis of PDB and the studies in this area have identified several genes previously not suspected to play a role in bone metabolism. Genetic testing coupled to targeted therapeutic intervention is being explored as a way of halting disease progression and improving outcome before irreversible skeletal damage has occurred.

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A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Journal of Endocrinology ◽

10.1530/joe-12-0144 ◽

2012 ◽

Vol 215 (1) ◽

pp. 17-28 ◽

Cited By ~ 18

Author(s):

Georg Homuth ◽

Alexander Teumer ◽

Uwe Völker ◽

Matthias Nauck

Keyword(s):

Blood Cells ◽

Large Scale ◽

Genetic Factors ◽

Association Studies ◽

Transcriptional Profiling ◽

Genome Wide Association Studies ◽

Protein Levels ◽

Future Developments ◽

Genome Wide ◽

Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

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Germline Genetic Factors Influence Outcome of Interferon Alpha Therapy in Polycythemia Vera

Blood ◽

10.1182/blood.2020005792 ◽

2020 ◽

Author(s):

Roland Jäger ◽

Heinz Gisslinger ◽

Elisabeth Fuchs ◽

Edith Bogner ◽

Jelena D. Milosevic Feenstra ◽

...

Keyword(s):

Polycythemia Vera ◽

Interferon Alpha ◽

Genetic Factors ◽

Myeloproliferative Neoplasms ◽

Association Studies ◽

Study Cohort ◽

Molecular Response ◽

Genome Wide Association Studies ◽

Association Analyses ◽

Genome Wide

Interferon alpha (IFNα) based therapies can induce hematologic and molecular responses in polycythemia vera (PV); however, patients do not respond equally. Germline genetic factors have previously been implicated in differential drug response. We addressed the effect of common germline polymorphisms on hematologic and molecular response (HR/MR) in PV therapy within the PROUD-PV and CONTINUATION-PV studies including 122 patients with PV receiving ropeginterferon alfa-2b. Genome-wide association studies using longitudinal data on HR and MR over 36 months follow-up did not reveal any associations at genome-wide significance. Further, we performed targeted association analyses at the interferon lambda 4 (IFNL4) locus, well known for its role in hepatitis C viral clearance and recently reported to influence HR during therapy of myeloproliferative neoplasms. While we did not observe any association of IFNL4 polymorphisms with HR in our study cohort, we demonstrated a statistically significant effect of the functionally causative IFNL4 diplotype (haplotype pair including the protein-coding variants rs368234815/rs117648444) on MR (p=3.91x10-4; OR=10.80; 95%CI:[2.39-69.97]) as reflected in differential JAK2V617F mutational burden changes according to IFNL4 diplotype status. Stratification of PV patients based on IFNL4 functionality may allow for optimizing patient management during IFNα treatment.

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Genome-wide association studies for feedlot and growth traits in cattle1

Journal of Animal Science ◽

10.2527/jas.2010-3079 ◽

2011 ◽

Vol 89 (6) ◽

pp. 1684-1697 ◽

Cited By ~ 106

Author(s):

S. Bolormaa ◽

B. J. Hayes ◽

K. Savin ◽

R. Hawken ◽

W. Barendse ◽

...

Keyword(s):

Growth Traits ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Pathophysiology and genetic factors in moyamoya disease

Neurosurgical FOCUS ◽

10.3171/2009.1.focus08302 ◽

2009 ◽

Vol 26 (4) ◽

pp. E4 ◽

Cited By ~ 74

Author(s):

Achal S. Achrol ◽

Raphael Guzman ◽

Marco Lee ◽

Gary K. Steinberg

Keyword(s):

Moyamoya Disease ◽

Genetic Factors ◽

Association Studies ◽

Vascular Anomalies ◽

Future Research ◽

Incomplete Penetrance ◽

Genome Wide Association Studies ◽

Research Directions ◽

Genome Wide ◽

Future Research Directions

Moyamoya disease is an uncommon cerebrovascular condition characterized by progressive stenosis of the bilateral internal carotid arteries with compensatory formation of an abnormal network of perforating blood vessels providing collateral circulation. The etiology and pathogenesis of moyamoya disease remain unclear. Evidence from histological studies, proteomics, and endothelial progenitor cell analyses suggests new theories underlying the cause of vascular anomalies, including moyamoya disease. Familial moyamoya disease has been noted in as many as 15% of patients, indicating an autosomal dominant inheritance pattern with incomplete penetrance. Genetic analyses in familial moyamoya disease and genome-wide association studies represent promising strategies for elucidating the pathophysiology of this condition. In this review, the authors discuss recent studies that have investigated possible mechanisms underlying the etiology of moyamoya disease, including stem cell involvement and genetic factors. They also discuss future research directions that promise not only to offer new insights into the origin of moyamoya disease but to enhance our understanding of new vessel formation in the CNS as it relates to stroke, vascular anomalies, and tumor growth.

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Family history of venous thromboembolism as a risk factor and genetic research tool

Thrombosis and Haemostasis ◽

10.1160/th15-04-0306 ◽

2015 ◽

Vol 114 (11) ◽

pp. 890-900 ◽

Cited By ~ 15

Author(s):

Xinjun Li ◽

Henrik Ohlsson ◽

Jianguang Ji ◽

Jan Sundquist ◽

Kristina Sundquist ◽

...

Keyword(s):

Risk Factor ◽

Venous Thromboembolism ◽

Family History ◽

Genetic Factors ◽

Familial Aggregation ◽

Association Studies ◽

Genetic Research ◽

Research Tool ◽

Genome Wide Association Studies ◽

Venous Diseases

SummaryFamilial clustering of venous thromboembolism (VTE) was described as far back as 1905 by Briggs. Although Egeberg discovered inherited deficiency of antithrombin in 1965, it was not until Dahlback discovered resistance to activated protein C in 1993 that it became clear that genetic factors are common risk factors of VTE. Several genes have been linked to familial aggregation of VTE and genome-wide association studies have found several novel gene loci. Still, it has been estimated that much of the heritability for VTE remains to be discovered. Family history (FH) of VTE is therefore still important to determine whether a patient has an increased genetic risk of VTE. FH has the potential to represent the sum of effects and interactions between environmental and genetic factors. In this article the design, methodology, results, clinical and genetic implications of FH studies of VTE are reviewed. FH in first-degree relatives (siblings and/or parents) is associated with a 2–3 times increased familial relative risk (FRR). However, the FRR is dependent on age, number of affected relatives, and presentation of VTE (provoked/unprovoked). Especially high familial risks are observed in individuals with two or more affected siblings (FFR> 50). However, the familial risk for recurrent VTE is much lower or non-significant. Moreover, FH of VTE appears mainly to be important for venous diseases (i. e. VTE and varicose veins). The familial associations with other diseases are weaker. In conclusion, FH of VTE is an important research tool and a clinically potential useful risk factor for VTE.

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Mapping the genetics of neuropsychological traits to the molecular network of the human brain using a data integrative approach

10.1101/336776 ◽

2018 ◽

Cited By ~ 2

Author(s):

Afsheen Yousaf ◽

Eftichia Duketis ◽

Tomas Jarczok ◽

Michael Sachse ◽

Monica Biscaldi ◽

...

Keyword(s):

Genetic Variants ◽

Genetic Factors ◽

Association Studies ◽

Brain Regions ◽

Autism Spectrum ◽

Integrative Approach ◽

Phenotypic Trait ◽

Genome Wide Association Studies ◽

Transcriptome Data ◽

Brain Areas

AbstractMotivationComplex neuropsychiatric conditions including autism spectrum disorders are among the most heritable neurodevelopmental disorders with distinct profiles of neuropsychological traits. A variety of genetic factors modulate these traits (phenotypes) underlying clinical diagnoses. To explore the associations between genetic factors and phenotypes, genome-wide association studies are broadly applied. Stringent quality checks and thorough downstream analyses for in-depth interpretation of the associations are an indispensable prerequisite. However, in the area of neuropsychology there is no framework existing, which besides performing association studies also affiliates genetic variants at the brain and gene network level within a single framework.ResultsWe present a novel bioinformatics approach in the field of neuropsychology that integrates current state-of-the-art tools, algorithms and brain transcriptome data to elaborate the association of phenotype and genotype data. The integration of transcriptome data gives an advantage over the existing pipelines by directly translating genetic associations to brain regions and developmental patterns. Based on our data integrative approach, we identify genetic variants associated with Intelligence Quotient (IQ) in an autism cohort and found their respective genes to be expressed in specific brain areas.ConclusionOur data integrative approach revealed that IQ is related to early down-regulated and late up-regulated gene modules implicated in frontal cortex and striatum, respectively. Besides identifying new gene associations with IQ we also provide a proof of concept, as several of the identified genes in our analysis are candidate genes related to intelligence in autism, intellectual disability, and Alzheimer’s disease. The framework provides a complete extensive analysis starting from a phenotypic trait data to its association at specific brain areas at vulnerable time points within a timespan of four days.Availability and ImplementationOur framework is implemented in R and Python. It is available as an in-house script, which can be provided on [email protected]

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MixMir: microRNA motif discovery from gene expression data using mixed linear models

10.1101/004010 ◽

2014 ◽

Author(s):

LIYANG Diao ◽

Antoine Marcais ◽

Scott Norton ◽

Kevin C. Chen

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Motif Discovery ◽

Linear Models ◽

Developmental Stages ◽

Sequence Similarity ◽

Association Studies ◽

Genome Wide Association Studies ◽

Expression Data ◽

Mixed Linear Models

MicroRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. MiRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3' UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miREDUCE, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in Dicer knockout CD4+ T-cells, as well as protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

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A systematic review and meta-analysis of host genetic factors associated with influenza severity

BMC Genomics ◽

10.1186/s12864-021-08240-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nina Van Goethem ◽

Célestin Danwang ◽

Nathalie Bossuyt ◽

Herman Van Oyen ◽

Nancy H. C. Roosens ◽

...

Keyword(s):

Systematic Review ◽

Genetic Factors ◽

Association Studies ◽

Influenza Infection ◽

Meta Analysis ◽

Genetic Association Studies ◽

Genome Wide Association Studies ◽

Severe Influenza ◽

Host Genetic ◽

Host Genetic Factors

Abstract Background The severity of influenza disease can range from mild symptoms to severe respiratory failure and can partly be explained by host genetic factors that predisposes the host to severe influenza. Here, we aimed to summarize the current state of evidence that host genetic variants play a role in the susceptibility to severe influenza infection by conducting a systematic review and performing a meta-analysis for all markers with at least three or more data entries. Results A total of 34 primary human genetic association studies were identified that investigated a total of 20 different genes. The only significant pooled ORs were retrieved for the rs12252 polymorphism: an overall OR of 1.52 (95% CI [1.06–2.17]) for the rs12252-C allele compared to the rs12252-T allele. A stratified analysis by ethnicity revealed opposite effects in different populations. Conclusion With exception for the rs12252 polymorphism, we could not identify specific genetic polymorphisms to be associated with severe influenza infection in a pooled meta-analysis. This advocates for the use of large, hypothesis-free, genome-wide association studies that account for the polygenic nature and the interactions with other host, pathogen and environmental factors.

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Bench Research Informed by GWAS Results

Cells ◽

10.3390/cells10113184 ◽

2021 ◽

Vol 10 (11) ◽

pp. 3184

Author(s):

Nikolay V. Kondratyev ◽

Margarita V. Alfimova ◽

Arkadiy K. Golov ◽

Vera E. Golimbet

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Reverse Genetics ◽

Genetic Factors ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually ‘highly polygenic’. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise ‘wet biologists’ with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

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