Welcome back again RSG friends! The easter is over and we are going towards the summer, so lets take the advantage of these sunny german days to have a look to the May newsletter of the German Regional Student Group from the iSCB Student Council. We have decided for this month to select three papers related to population genomics and how this field can be useful for our society. In the first we will discuss about what would be the impact in considering gender and race/ethnicity in the health systems during the process of diagnosis. The second paper will talk about the contribution of genetic variants and negative selection in complex traits such as weight and obesity, finally we will see an example of how genomic variations impact the colors of our hairs. See you the next month, stay tuned! Yours:
RGS Germany (ISCB Student Council) – Tommaso Andreani, Ilkay Başak Uysal, Neetika Nath, Nikos Papadopoulos, Yvonne Gladbach
Racial and Ethnic Disparities in Cancer Survival: The Contribution of Tumor, Sociodemographic, Institutional, and Neighborhood Characteristics
Health is wealth! We all have heard this term some point of time in our life. So far this is achieved independently by different countries with continuous improvement in the medical care system. However, we find ourselves asking this important question: Is our current understanding of medicine good enough for treatment of different race/ethnicity or gender? A study conducted by The California Cancer Registry (CCR) of Ellis et al. in 2017, used data about almost 900.000 patients collected between 2000 and 2013 to study the influence of race/ethnicity, gender, and socioeconomic status on cancer survival. Among other factors considered for the study, there were 1) tumour characteristics at onset, 2) disease management, 3) treatments, 4) factors that relate to the health care institute where the treatment was performed and 5) the sociodemographic and neighbourhood characteristics in which the patients were living.
By applying mediation analysis, the authors quantify the contribution of patient and tumour characteristics to racial/ethnic survival disparities and rather than race being the reason for better or worse survival rates, they hypothesized that other circumstances, closely related to race, might explain the phenomenon better. The particular question asked here would be whether disease management and treatments are possible mediators for cancer mortality in different race/ethnicity or gender.
Agreeing with conventional wisdom, the study found that early tumour detection is the most effective treatment strategy, irrespective of the race/ethnicity or gender. The tumour stage at the time of diagnosis had the largest effect on race/ethnicity disparities in survival for breast, prostate, and colorectal cancers. This, in turn, reflects other factors that include socioeconomic status, health insurance and access to health care. Race/ethnicity disparities in breast cancer survival vary considerably according to tumour subtypes, which was not limited to simply earlier detection of cancer. In a single case example, the hormone receptor status was important to investigate survival disparities in breast cancer, especially for black women. Without doubt, further detailed information regarding clinical treatments that consider race and ethnicity as important variables, could provide important insight into the resolution of the differences in cancer survival among single individuals. These studies are very important not only for the understanding but to improve our healthcare system and towards personalized medicine.
Ellis, Libby, et al. “Racial and Ethnic Disparities in Cancer Survival: The Contribution of Tumor, Sociodemographic, Institutional, and Neighborhood Characteristics.” Journal of Clinical Oncology 36.1 (2017): 25-33.
Signatures of negative selection in the genetic architecture of human complex traits
Understand the genetic basis of complex molecular traits such as cancer, obesity or alzheimer has attract a lot of attentions in “modern” genetic. The improvement of genomics analysis in terms of cost and time has allowed scientists to test the millions of genetic variants of the human genome to specific polygenic traits. It is becoming clear that understanding the role of single nucleotide polymorphisms to a particular phenotype is far more complex than what explained by the mendelian genetic in which a mutation in one locus is responsible of a given phenotype.
Natural selection on genetic variants, is one of the driving force of when and how an allele is selected or not in the next gene pool for a particular phenotype (positive and negative selection respectively). This concept has attracted a lot of attention in the last few years in cohort of genome wide association studies. Indeed, it has been proposed that natural selection is likely to act simultaneously on many trait-associated variants that have pleiotropic effects on fitness. But, what does this exactly mean? In practice, according to the author, specific genetic variants along the genome are more prone to undergo to negative selection (selective sweep model). Example of those variants are the ones directly involved in a particular disease and that negative selection is likely to select against (eliminate) in most gene pool individuals. However, others variants with small effects that are typical of complex traits are difficult to estimate whether they are under negative or positive selection.
In order to detect the contribution of negative and positive selection on those variants with small effects on complex traits, the authors developed a Bayesian mixed linear model (MLM) method that can simultaneously estimate SNP-based heritability, polygenicity, and the joint distribution of effect size and minor allele frequency (MAF) in conventionally unrelated individuals. What they found was that out of the 28 traits analyzed, 23 of them are associated with strong selection. More important, they were able to report that most of these traits related variants were undergoing to negative selection (for example for height and body mass index).
In conclusion, future GWAS based on whole genome sequencing, or imputed data with large sample sizes, are expected to discover an increasing number of rare variants in which the sum of them will have a large effect in the phenotypic outcome of a given population.
Zeng, Jian, et al. “Signatures of negative selection in the genetic architecture of human complex traits.” Nature genetics (2018): 1.
Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability
This study provides insight to melanin pigment metabolism in humans and how it is explained by single-nucleotide polymorphisms associated with hair color.
The results might be useful to better understand molecular human pigmentation, particularly for DNA-based predictions with forensic and anthropological applications, and to understand and potentially develop treatment strategies for diseases that result from biological impairment of pigmentation.
In this large-scale study, the authors analysed 300.00 people’s genomes. By comparing their genomic profile, they discovered 124 genes significantly associated with hair colour; one of which was on the X chromosome and only 13 of them were novel.
A meta-analysis of two GWAS was carried out in two large discovery cohort studies: 157,653 research participants from the 23andMe, Inc. customer base18 and 133,238 individuals from the UK Biobank (UKBB). The participants self-reported their natural hair colour in adulthood. A numerical category system was used for this purpose and each hair color category collected (black, dark brown, light brown, red, and blond) was assigned to a numerical values ranging from lowest (blond) to highest (black). These codes were used as the outcome variable in linear regression-based GWAS analyses. To minimize population admixture and stratification, the analyses were restricted to individuals of European ancestry and adjusted for the first ten principal components of the genotype matrix, as well as for age and sex.
Hair colour associated single-nucleotide polymorphisms were found to be responsible for 34.6% of red hair, 25% of blond hair, and 26% of black hair heritability in the group of people studied. These results confirm the polygenic nature of complex phenotypes as known before.
The study findings also provide insights into disease and sex-specific pigmentation. Woman in the study are more likely to report their hair blond or red than any other color and three to five times less likely to report black hair compared to men. This result shows that sex is associated with hair colour independent of socially driven self-reporting bias which is in line with previous findings that are based on objective measurement of hair colour. Although hair pigmentation spans a spectrum from very bright (blonde) to very dark (black), the genetic mechanisms do not always follow this linear scale, as red hair color often has unique predisposing genetic factors. However, this study explains higher portions of heritability than before.
As expected, genes showing the strongest association in the meta-analysis were significantly enriched for several Gene Ontology entries, especially pigmentation and melanin biosynthetic and metabolic processes. Moreover, this method outperformed one of the pioneer studies that used the HIrisPlex model for predicting eye and hair colour from DNA (The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA, Walsh et al. 2013).
The authors used their results to predict black and red hair colours with a high degree of accuracy using a separate set of genetic data in which hair colour was known. Although predictions for brown and blond hair colour were less accurate, this can potentially help in predicting hair colour based on DNA evidence in forensic investigations.
Pirro G. Hysi, et al. “Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability” Nature genetics(2018).