Health issue in Japan
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Susceptibility of diabetes in Southeast Asia is influenced by mutations next to or within genes. Most of these genes have complex traits that interact and control multiple systems in the body. The genes are important, but they alone do not influence diabetes in this population. Westernization and globalization in Asia have led to changes in food supply and dietary patterns, coupled with risk alleles in genes of interest. [1] Foods with high glycemic index, such as rice, have contributed to an increased risk of type 2 diabetes. [1] These high glycemic foods, coupled with loss of gene function, have led to increase in blood glucose levels in Japanese populations. [1] Researchers have used Genome Wide Association Studies (GWAS) to identify several genetic variants of genes that are found in white and Asian populations; however, they found that there are interethnic differences in risk allele frequency of these genes and the location of those mutations. [1]
Age and gender are critical variables to track diabetes within a population overtime. Another very important factor of diabetes development is the environment in which that population lives. Factors like smoking or alcohol consumption has been seen to be prevalent in Japanese populations. [2] For example, smoking was very prominent in Japan in the 1980s where 40% of the population smoked. [3] As of 2020, smoking has declined to around 16% of the population. [3] The more resounding knowledge on the effect of smoking and the link to disease susceptibility has most likely driven this decline. [2] Smoking generally leads to problems because it leads to insulin resistance and/or decrease insulin secretion. [4]
Many diabetic studies have tried to deduce as to what and how many genes are involved in susceptibility to disease in Japanese populations. Researchers use tools such as GWAS and quantitative trait loci (QTLs), which can be very helpful in determining what mutations are associated with disease in a population. Most of these mutations are called single nucleotide polymorphisms (SNPs) leading to different variants of a gene, and these mutations can be different or the same across varying populations. If they are the same mutations, a population could have varied allelic frequencies of the gene or genes that lead to that disease. For example, a genetic variation of TCF7L2 in Japanese population influences one's susceptibility to diabetes; however, the at-risk allele frequency in Japanese population accounted for 4% of the population compared to 21% in European and European-origin population. [5]
Additional candidate genes with specific risk alleles have also been identified via GWAS that were persistent in European populations; however, there are considerable differences in allelic frequencies between these populations. [6] Genes such as CDKAL1, IGF2BP2, CDKN2A/CDKN2B, HHEX, SLC30A8, and KCNJ11 have shown risk alleles persistent to white populations; however, risk allele frequency has differed. [6] For example, the HHEX locus had an SNP denoted as rs1111875 that was found in 28.4% of the Japanese population compared to 56.1% in white populations. [6] Akyrin1 (ANK1) has also been identified as a novel gene for type 2 diabetes in Japanese populations. [7] SNP rs515071 located at an intron of this gene has been associated with European GWAS results. [7] Variants of ANK1 such as rs4737009 and rs6474359 were shown to influence HbA1c levels in European populations in non-diabetic adults. [7] To see if these SNPs were associated with rs515071 in Japanese populations, a linkage disequilibrium (LD) calculation was performed. [7] Linkage was determined be weak, which indicates that rs515071 operates independently of other SNPs that influence HbA1c levels in other populations. [7] So, HbA1c levels in Japanese populations seem to not be affected by these other SNPs that influence HbA1c levels in European populations. [7] This suggests that there's no association or interaction with these three SNPs in the Japanese population. [7] Thus, the mechanism by which this SNP rs515071 contributes to susceptibility of diabetes remains to be discovered. [7]
Other susceptibility genes common across different ethnicities have been found. Further GWAS studies of Japanese populations have identified seven novel loci CCDC85A, FAM60A, DMRTA1, ASB3, ATP8B2, MIR4686, INAFM2. [8] Among these loci, SNPs near or within genes such as FAM60A, DMRTA1, MIR4686, and INFAM2 showed common susceptibility loci for type 2 diabetes in different ethnicities. [8] Monoallelic gene expression (MAE) has also been identified in a couple of genes of interest in Japanese populations. [8] These genes are ADCY5, HNF1A, PRC1 that exhibited this monoallelic effect in Japanese populations and showed susceptibility to type 2 diabetes. [8] As of 2020, a new repertoire of genes has been reported to be a contributor to type 2 diabetes in Japanese populations. [9] These genes are MEF2C, TMEM161B, CEP120, PRDM6, STEAP1, ZNF804B, ZNRF3, PRIM1, IRF2BPL, LRRC74A respectively. [9] Linkage has been reported in these genes such as ME2FC/TMEM161B and the IRF2BPL/LRRC74A; however, the underlying causes or mechanisms that leads to diabetes do to this linkage is unknown at this time. [9]
A complication of diabetes is retinopathy where the eye's blood vessels are dilated and damaged due to an increase in blood pressure. A recent study has specifically looked at susceptibility to diabetic retinopathy (DR) in Japanese patients with type 2 diabetes. [10] Using GWAS, they were able to identify an SNP locus showing genome-wide significant association DR. [10] This was the SNP rs12630354 near STT3B. [10] The authors also found lead SNPs of three loci that contributed to DR susceptibility in Japanese populations, which were HS6ST3A1/B1, KIAA0825, and NOX4. [10] However, the linkage disequilibrium is low to moderate suggesting that linkage is not important and that the genes are not associated with one another. [10] Silico chromatin interaction and eQTL analyses showed that rs12630354 upregulates STT3B expression. [10] This SNP causes significant STT3B expression in the adrenal gland. [10] STT3B has been suggested to participate in local synthesis and quality control of membrane involved in cholesterol and steroid metabolism in adrenocortical cells. [10] Therefore, rs12630354 might dysregulate cholesterol and steroidal synthesis leading to increase susceptibility to DR. [10]
In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome. Although certain definitions require the substitution to be present in a sufficiently large fraction of the population, many publications do not apply such a frequency threshold.
Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait more often than would be expected by chance occurrence.
In molecular biology, SNP array is a type of DNA microarray which is used to detect polymorphisms within a population. A single nucleotide polymorphism (SNP), a variation at a single site in DNA, is the most frequent type of variation in the genome. Around 335 million SNPs have been identified in the human genome, 15 million of which are present at frequencies of 1% or higher across different populations worldwide.
A tag SNP is a representative single nucleotide polymorphism (SNP) in a region of the genome with high linkage disequilibrium that represents a group of SNPs called a haplotype. It is possible to identify genetic variation and association to phenotypes without genotyping every SNP in a chromosomal region. This reduces the expense and time of mapping genome areas associated with disease, since it eliminates the need to study every individual SNP. Tag SNPs are useful in whole-genome SNP association studies in which hundreds of thousands of SNPs across the entire genome are genotyped.
The common disease-common variant hypothesis predicts that common disease-causing alleles, or variants, will be found in all human populations which manifest a given disease. Common variants are known to exist in coding and regulatory sequences of genes. According to the CD-CV hypothesis, some of those variants lead to susceptibility to complex polygenic diseases. Each variant at each gene influencing a complex disease will have a small additive or multiplicative effect on the disease phenotype. These diseases, or traits, are evolutionarily neutral in part because so many genes influence the traits. The hypothesis has held in the case of putative causal variants in apolipoprotein E, including APOE ε4, associated with Alzheimer's disease. IL23R has been found to be associated with Crohn's disease; the at-risk allele has a frequency of 93% in the general population.
In genomics, a genome-wide association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.
TOX high mobility group box family member 3, also known as TOX3, is a human gene.
CDKN2B-AS, also known as ANRIL is a long non-coding RNA consisting of 19 exons, spanning 126.3kb in the genome, and its spliced product is a 3834bp RNA. It is located within the p15/CDKN2B-p16/CDKN2A-p14/ARF gene cluster, in the antisense direction. Single nucleotide polymorphisms (SNPs) which alter the expression of CDKN2B-AS are associated with human healthy life expectancy, as well as with multiple diseases, including coronary artery disease, diabetes and many cancers. It binds to chromobox 7 (CBX7) within the polycomb repressive complex 1 and to SUZ12, a component of polycomb repression complex 2 and through these interactions is involved in transcriptional repression.
A gene is said to be polymorphic if more than one allele occupies that gene's locus within a population. In addition to having more than one allele at a specific locus, each allele must also occur in the population at a rate of at least 1% to generally be considered polymorphic.
Most cases of type 2 diabetes involved many genes contributing small amount to the overall condition. As of 2011 more than 36 genes have been found that contribute to the risk of type 2 diabetes. All of these genes together still only account for 10% of the total genetic component of the disease.
CDKAL1 is a gene in the methylthiotransferase family. The complete physiological function and implications of this have not been fully determined. CDKAL1 is known to code for CDK5, a regulatory subunit-associated protein 1. This protein CDK5 regulatory subunit-associated protein 1 is found broadly across tissue types including neuronal tissues and pancreatic beta cells. CDKAL1 is suspected to be involved in the CDK5/p35 pathway, in which p35 is the activator for CDK5 which regulates several neuronal functions.
In recent years it has become apparent that the environment and underlying mechanisms affect gene expression and the genome outside of the central dogma of biology. It has been found that many epigenetic mechanisms are involved in the regulation and expression of genes such as DNA methylation and chromatin remodeling. These epigenetic mechanisms are believed to be a contributing factor to pathological diseases such as type 2 diabetes. An understanding of the epigenome of diabetes patients may help to elucidate otherwise hidden causes of this disease.
Project MinE is an independent large scale whole genome research project that was initiated by 2 patients with amyotrophic lateral sclerosis and started on World ALS Day, June 21, 2013.
GWAS in allergy is a study of a meta-analysis of genome-wide association study in which allergy is associated with different susceptibility loci. The three allergic phenotypes studied were to cat, dust mites and pollen, for which found patients presenting allergic symptoms.
Insulin-dependent diabetes mellitus (IDDM) is a genetic heterogenouse autoimmune disorder, which is triggered by genetic predisposition and environmental factors. The prevalence of insulin-dependent diabetes mellitus (IDDM) among children and young adult from Europe is approximately 0.4%. Insulin-dependent diabetes mellitus (IDDM) is characterized by acute onset and insulin deficiency. Patients with insulin-dependent diabetes mellitus (IDDM) are found with gradual loss of the pancreatic islet beta cells and therefore not able to produce insulin. As a result, they usually need exogenous insulin to maintain their life.
Predictive genomics is at the intersection of multiple disciplines: predictive medicine, personal genomics and translational bioinformatics. Specifically, predictive genomics deals with the future phenotypic outcomes via prediction in areas such as complex multifactorial diseases in humans. To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in genome-wide association (GWA) studies. The identification of associated single-nucleotide polymorphisms underpin GWA studies in complex diseases that have ranged from Type 2 Diabetes (T2D), Age-related macular degeneration (AMD) and Crohn's disease.
In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score or genetic risk score. The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated alleles.
Complex traits are phenotypes that are controlled by two or more genes and do not follow Mendel's Law of Dominance. They may have a range of expression which is typically continuous. Both environmental and genetic factors often impact the variation in expression. Human height is a continuous trait meaning that there is a wide range of heights. There are an estimated 50 genes that affect the height of a human. Environmental factors, like nutrition, also play a role in a human's height. Other examples of complex traits include: crop yield, plant color, and many diseases including diabetes and Parkinson's disease. One major goal of genetic research today is to better understand the molecular mechanisms through which genetic variants act to influence complex traits. Complex traits are also known as polygenic traits and multigenic traits.
Adolescent idiopathic scoliosis (AIS) is a disorder in which the spine starts abnormally curving sideways (scoliosis) between the ages of 10–18 years old. Generally, AIS occurs during the growth spurt associated with adolescence. In some teens, the curvature is progressive, meaning that it gets worse over time, however, AIS more commonly manifests only as a mild curvature.
Alzheimer's disease (AD) is a complex neurodegenerative disease that affects millions of people across the globe. It is also a topic of interest in the East Asian population, especially as the burden of disease increases due to aging and population growth. The pathogenesis of AD between ethnic groups is different. However, prior studies in AD pathology have focused primarily on populations of European ancestry and may not give adequate insight on the genetic, clinical, and biological differences found in East Asians with AD. Gaps in knowledge regarding Alzheimer's disease in the East Asian population introduce serious barriers to screening, early prevention, diagnosis, treatment, and timely intervention.