In this study, we analyzed publicly available data to elucidate the genetic factors influencing the development of CRC. Significant disparities were noted in the population frequencies of CRC-associated SNPs. The GRS for the majority of EAS and Korean individuals exceeded that of EUR and AMR individuals.
The incidence rates of colon cancer exhibit a significant variation, approximately ninefold, among different global regions. The highest rates are observed in EUR regions, Australia/New Zealand, and Northern America, with Hungary and Norway showing the highest rates among males and females, respectively. A comparable regional distribution is also evident in rectal cancer incidence rates, with East Asia likewise ranking among the regions with elevated rates. Conversely, regions in Africa and South-Central Asia tend to display low rates for both colon and rectal cancer incidence.20 However, over the past few decades, there has been a steady increase in CRC incidence, particularly in East and Southeast Asian countries. Between 1993 and 2010, age-standardized CRC incidence rates consistently rose in various Asian countries, with South Korea experiencing the most significant increase.3 According to age-standardized incidence rates, CRC ranks among the top three cancer types in many Asian countries.21
The differing incidence rates of CRC among various racial groups can be attributed to environmental factors. For instance, dietary habits, including the consumption of red meat, represent a significant risk factor for CRC development and can impact incidence rates in both developed and developing nations. Moreover, early screening programs and prevention campaigns can exert an influence on the specific CRC incidence rates in individual countries.2,22 The upsurge in CRC incidence in Asian countries can also be attributed to shifts in socioeconomic status and lifestyle changes. These changes encompass factors such as obesity, tobacco usage, and the consumption of spicy food, alcohol, and meat.3 The dietary pattern in South America, characterized by high consumption of beef and fats and low intake of fibers, could be associated with an elevated risk of CRC.23 However, the rise in CRC incidence in South America has not matched the significant increase observed in East Asia.20 This implies that factors beyond dietary habits may also play a crucial role in the occurrence of CRC.
Genetic factors play a pivotal role in the initiation of CRC. A positive family history is associated with approximately 10–20% of CRC cases, with varying risk levels contingent on the number of affected relatives, their degree of relatedness, and the age of diagnosis.24 Heritability estimates for CRC span 12–35%, as determined by twin and family studies.25,26 Recent research has delved into the genetic factors impacting CRC. According to Lichtenstein et al.20, twin studies on patients with cancer have revealed an elevated risk for stomach, colorectal, lung, breast, and prostate cancer. Genetic factors make only a modest contribution to susceptibility in most tumor types, with distinct exceptions in certain cancers, such as CRC, where a relatively substantial genetic influence is evident.26
In this regard, recent studies have revealed the potential contribution of genetic factors to variations in CRC incidence among different racial groups. To date, GWASs focused on sporadic CRC have identified approximately 60 association signals across more than 50 loci.12,27 Within EUR populations, GWASs have pinpointed 43 SNPs associated with 40 risk loci. In Asian populations, 18 SNPs at 16 risk loci have been elucidated, with a few overlapping those identified in EUR populations. Among EAS individuals, 13 novel loci associated with CRC risk have been identified, and notably, 8 of these loci have not been observed in EUR populations.12 Recently, Xin et al.28, noting disparities in the genotype of CRC across diverse population groups, devised a polygenic risk score framework that incorporates genome-wide SNPs from both EAS and EUR lineages. This framework represents a contemporary effort to forecast genetic influences in a spectrum of cancers through GWAS. In this context, this study was conducted to investigate the impact of genetic backgrounds on CRC.29
Most GWASs on CRC primarily focus on EUR populations. In this study, we reviewed a total of 35 studies, with 23 pertaining to EUR populations. Our findings indicate that variants identified through GWAS are replicable across multiple ethnicities. For instance, Carlson et al.18 reported that a significant majority of variants identified through GWAS of various traits among individuals of EUR ancestry display allelic associations in the same direction when examined in non-EUR populations. Furthermore, GWASs of type 2 diabetes encompassing a variety of ancestral groups have consistently shown that most susceptibility loci associated with common variants are shared across ethnicities.16 In a similar vein, Wojcik et al.17 validated the replication of 8,979 SNPs associated with various traits listed in the GWAS catalog. Their analysis revealed that 1,444 of these associations were replicated at a significance threshold of p < 0.05. Although a few genetic factors may exert different effects within distinct populations, we posit that the majority of SNPs identified in EUR populations are likely to exhibit similar effects in non-EUR populations.
This study exhibits certain limitations. First, the inclusion of 1,722 individuals in the Korean reference genome constitutes a substantial sample size, especially when considering that the 1000 Genomes Project encompassed 26 populations, with their sample sizes ranging from 61 to 113. Therefore, the statistical significance of the findings pertaining to the Korean population may have been highly evaluated. Second, in the calculation of GRS, we did not consider the effect sizes of the SNPs. This was because some studies did not report effect size and variations in effect sizes for the same SNPs across different studies. Utilizing a more sophisticated model that incorporates effect sizes could potentially yield more precise and accurate results.
In summary, our study showed substantial variations in allele frequencies of CRC-associated SNPs across diverse populations. Furthermore, a correlation between GRS and CRC prevalence was discerned. Disparities in allele frequencies linked to CRC-associated SNPs were observed between EAS and other populations, suggesting that these disparities in disease prevalence among populations can, to some degree, be attributed to genotype.