Genome-wide association (GWA) studies are the method of choice for discovering loci associated with common diseases. More than a thousand GWA studies have reported successful identification of statistically significant association signals in human genomes for a variety of complex diseases. In this review, I discuss some of the issues related to the future of GWA studies and their biomedical applications.
Genome, Human ; Genome-Wide Association Study ; Humans

Most psychiatric disorders are some kinds of complex genetic traits. Identifying the causal genes of psychiatric disorders has been challenging. Through recent revolutionary advances, such as the HapMap Project and the development of high-throughput genotyping chips, the genome-wide association study (GWAS) has recently become possible and is now in the spotlight in psychiatric genetics. In this article, we reviewed the concepts, rationale, designs and general steps of GWAS, and also introduced a few previous GWAS of several psychiatric disorders.
Genome-Wide Association Study ; HapMap Project

Most psychiatric disorders are some kinds of complex genetic traits. Identifying the causal genes of psychiatric disorders has been challenging. Through recent revolutionary advances, such as the HapMap Project and the development of high-throughput genotyping chips, the genome-wide association study (GWAS) has recently become possible and is now in the spotlight in psychiatric genetics. In this article, we reviewed the concepts, rationale, designs and general steps of GWAS, and also introduced a few previous GWAS of several psychiatric disorders.
Genome-Wide Association Study ; HapMap Project

Genome-Wide Association Study ; standards ; Humans

In the past few years, the bone field has witnessed great advances in genome-wide association studies (GWASs) of osteoporosis, with a number of promising genes identified. In particular, meta-analysis of GWASs, aimed at increasing the power of studies by combining the results from different study populations, have led to the identification of novel associations that would not otherwise have been identified in individual GWASs. Recently, the first whole genome sequencing study for osteoporosis and fractures was published, reporting a novel rare nonsense mutation. This review summarizes the important and representative findings published by December 2013. Comments are made on the notable findings and representative studies for their potential influence and implications on our present understanding of the genetics of osteoporosis. Potential limitations of GWASs and their meta-analyses are evaluated, with an emphasis on understanding the reasons for inconsistent results between different studies and clarification of misinterpretation of GWAS meta-analysis results. Implications and challenges of GWAS are also discussed, including the need for multi- and inter-disciplinary studies.
Codon, Nonsense ; Genetics ; Genome ; Genome-Wide Association Study* ; Osteoporosis*

Genome, Human ; Genome-Wide Association Study ; Humans ; Obesity ; genetics

Humans ; Genome-Wide Association Study ; Sleep Apnea, Obstructive/genetics*

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.
Artificial Intelligence ; Data Science ; Genome-Wide Association Study ; Genomics ; Technology

To analyze the research hotspots and trends of biomarkers for diseases based on genomics and thus provide basis for the future studies in this field. Based on the Web of Science,we analyzed the genomics-based biomarkers for diseases in literature published between 2006 and 2018 in terms of country and institutions,knowledge base,research hotspots,and trends by using bibliometric methods and CiteSpace software. A total of 998 articles were retrieved.The total number of articles has shown an upward trend and reached a peak of 112 in 2017 and 2018.Most articles(=477)were from the United States,follwed by China(=93).,,,,and are core journals in this field.Keywords co-occurrence analysis identified four research hotspots:disease research,research method and technology,research level,and application purpose. Research in functional genomics,cancer immunotherapy,genome-wide association and multi-omics techniques,personalized medicine,and precision medicine are research hotspots and frontiers in this field.
Bibliometrics ; Biomarkers ; China ; Genome-Wide Association Study ; Genomics ; United States

Bipolar Disorder ; genetics ; Genome-Wide Association Study ; Genomics ; methods ; Humans