The risk of developing most cancers increases with age. The American Cancer Society ACS estimates that 9 out of 10 people with CLL are aged 50 years or older. Males are slightly more likely to have CLL than females.

ALL also occurs more often in males than females. The ACS notes that leukemia is more common in white Americans than in Black Americans. However, research suggests Black people may face poorer prognoses due to disparities in access to care, diagnosis, and treatment. How do racial disparities affect cancer rates and treatment for Black Americans?

The ACS states that the following inherited genetic syndromes may increase the risk of ALL:. Benzene is a chemical present in many products, including gasoline, glue, cleaning supplies, cigarettes, detergents, and dyes. According to the CDC Centers for Disease Control and Prevention , benzene is in the top 20 most produced chemicals in the United States.

What is the link between benzene and leukemia? In most cases, it is not clear why leukemia develops. However, being aware of the risk factors can help people take precautions.

In rare cases , people inherit genetic traits that increase their risk of leukemia, but it does not mean they will develop it. Scientists have found genetic links to various types of this disease. Most cases of leukemia do not have an obvious cause, but exposure to high levels of radiation and certain toxins can increase the risk.

It can also occur in people with a history of radiation therapy and chemotherapy and in those with certain genetic conditions, such as Down syndrome and Fanconi anemia. Genetic testing can show doctors which kind of genetic changes are present in cancer cells, and this can help identify the type of leukemia.

However, it cannot show if someone is likely to inherit or pass on the disease. In most cases, the genetic changes that occur with leukemia are not hereditary. Leukemia involves atypical cell development in the blood and bone marrow. It does not usually run in families, but people can inherit genetic features that increase their risk of developing it.

It is not always possible to prevent leukemia, but taking steps, such as avoiding smoking and exposure to certain toxins, may help. Leukemia and lymphoma are both types of blood cancer that affect white blood cells. Here, learn about the similarities and differences and the overall….

Read more…. Platelet counts can indicate the presence of leukemia. Learn how doctors can conduct blood platelet tests to help detect leukemia. Prolymphocytic leukemia PLL is a rare form of leukemia that mainly affects older adults and not children.

Common variants near MC4R are associated with fat mass, weight and risk of obesity. Qi L, Kraft P, Hunter DJ, Hu FB. The common obesity variant near MC4R gene is associated with higher intakes of total energy and dietary fat, weight change and diabetes risk in women.

Hum Mol Genet. Human genetics illuminates the paths to metabolic disease. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of , individuals reveal eighteen new loci associated with body mass index.

Heid IM, Jackson AU, Randall JC. Meta-analysis identifies 13 novel loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.

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Gene-environment interaction and obesity. Nutr Rev. Andreasen CH, Stender-Petersen KL, Mogensen MS, et al. Low physical activity accentuates the effect of the FTO rs polymorphism on body fat accumulation.

Rampersaud E, Mitchell BD, Pollin TI, et al. Physical activity and the association of common FTO gene variants with body mass index and obesity. Arch Intern Med. Ruiz JR, Labayen I, Ortega FB, et al. Attenuation of the effect of the FTO rs polymorphism on total and central body fat by physical activity in adolescents: the HELENA study.

Arch Pediatr Adolesc Med. Jonsson A, Renstrom F, Lyssenko V, et al. Assessing the effect of interaction between an FTO variant rs and physical activity on obesity in 15, Swedish and 2, Finnish adults.

KilpelinenTO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of , adults and 19, children.

PLoS Med. Epub Nov 1. Veerman JL. On the futility of screening for genes that make you fat. Qi, Q, Chu, AY, Kang, JH, Huang, J, Rose, LM, Jensen, MK, Liang, L, Curhan, GC, Pasquale, LR, Wiggs, JL, De Vivo, I, Chan, AT, Choi, HK, Tamimi, RM, Ridker, PM, Hunter, DJ, Willett, WC, Rimm, EB, Chasman, DI, Hu, FB, Qi, L.

We also extracted ClinVar-annotated 37 variants accessed in November and germline variants in BRCA1 and BRCA2 accessed in November annotated by the ENIGMA consortium We considered all genes part of the ACMG recommendations for reporting incidental findings in clinical exome and genome sequencing studies Due to statistical power considerations, we restricted our analysis so that at least 35 individuals had a positive burden, resulting in nine genes for both burden types Supplementary Table 7.

We included 30 genome-wide PGSs for traits of interest constructed from publicly available summary statistics Supplementary Table We selected PGSs for psychobehavioral traits for example cognitive ability and neuroticism , major chronic diseases for example coronary artery disease and depression and major risk factors for example LDL cholesterol and blood pressure to cover traits of interest with high-quality summary statistics available.

PGSs were only analyzed in FinnGen, as many of the original summary statistics included UKB. We used PRS-CS 75 for generating the PGSs using external LD reference panel Genomes Project Europeans.

We used the PRS-CS-auto algorithm, which learns the global scaling parameter ϕ from the data and performs well with large datasets. Scores for lifespan, educational attainment, cognitive performance and intelligence were reversed before analysis to make all scores on net deleterious in terms of DALYs.

We additionally formed a composite PGS of mortality using all the 30 PGSs Supplementary Table 10 emulating the approach by Meisner et al. We then extracted the linear predictor for all individuals, while setting sex to 0.

To estimate the HRs between all genetic exposure—disease pairs we used Cox proportional hazards regression via the coxph function in the survival package v. The model was additive for allele counts. Sex and the first ten PCs of population structure were included as covariates.

We used calendar age as the timescale and age at first record of the disease in the registries as time-to-event. Individuals were censored at death, emigration or end of registry-based follow-up 31 August in FinnGen and 30 April in UKB.

For the common variants, HRs were estimated both in FinnGen and UKB separately and combined using fixed effects inverse-variance-weighted meta-analysis.

A comparison of effect sizes between FinnGen and UKB is provided in Extended Data Fig. We did not account for left censoring or relatedness of the individuals due to computational limitations. As a sensitivity analysis in FinnGen, we examined whether accounting for relatedness would meaningfully change the standard errors of the log HRs.

For 2, common variant—disease pairs we estimated the log HRs using a survival model clustered by family indicator to generate robust standard errors. We generated a family indicator from genotype data using KING 77 v. Individuals up to a third degree of relatedness were included in the same family.

Compared to the main analysis estimates, robust standard errors were a median 1. Thus, accounting for relatedness would not meaningfully affect the CIs and P values. As a second sensitivity analysis, we explored whether the effect of the genetic exposures on the diseases was age-dependent by performing age-stratified survival analyses for a subset of genetic exposures.

Perhaps unsurprisingly 78 , we observed age-varying HRs for genetic exposures Extended Data Fig. For example, for the coronary artery disease PGS 46 , the HRs were 2.

We use prior information to apply a shrinkage procedure to the HRs for exposure—disease pairs to reduce the effect of sampling variation at the cost of being more conservative biasing total attributable DALYs toward zero.

We denote the mixture weight of the non-zero component by p e , which can be interpreted as the exposure-specific proportion of non-zero effects across the 80 diseases. The full probability model is:. In practice, which effects are shrunk to zero and which are retained as non-zero, does not vary considerably when these prior parameters are varied Extended Data Fig.

As a sensitivity analysis to explore the performance of the shrinkage method, we used hail v. The approach used genetic data from , individuals from UKB with European ancestry and has the advantage of using realistic variant frequencies and population structure as compared to simulated genetic data.

Only , independent HapMap 3 SNPs were considered in the analysis. Using the ldscsim. The phenotypes were consequently binarized based on the disease prevalence observed in FinGen using the function ldscsim. We considered four π values 0. We then ran a GWAS for each of the phenotypes across the four different π scenarios.

This allows us to obtain an observed effect size from the GWAS and an expected true underlying effect. For this selected group of variants, we applied the same shrinkage procedure as in the main analysis. Our procedure shrinks most of the variant—phenotype associations to 0, while maintaining others unshrunk.

Because we know the true underlying effect sizes, that is, which variants have effect size of 0 null model and effect sizes different from 0 alternative model , we can compare how well our procedure shrinks variants from the null model versus does not shrink those from the alternative model.

Overall, our approach results in area under the curve values of 0. Thus, the shrinkage approach can identify true causal variants in simulated GWAS data Extended Data Fig.

Similarly to the GBD 11 , we used the HRs and frequencies of the exposures to estimate attributable DALYs one disease at a time. We used the multilevel exposure formula 79 for the population-attributable fraction the fraction of cases of disease d caused by the exposure levels in the population deviating from counterfactual levels :.

Plugging these numbers into the AFp d formula produces the population-attributable fraction of disease cases from those carrying one versus zero copies of the allele the fraction of cases that would be prevented if all with one allele had zero instead , which is 0.

We then assumed that the estimated population-attributable fraction of disease cases can be interpreted as the population-attributable fraction of DALYs, which is true if all disease cases contribute on average the same amount of DALYs independent of whether they have the genetic exposure or not.

This assumption does not hold if, for example, deleterious BRCA1 mutation carriers develop breast cancer earlier and consequently accumulate more DALYs per case. where DALY d represents the population DALYs per year per , through disease d from GBD, Pi is the fraction of population for which the exposure is changed for example, fraction of those with one copy and L is included to convert yearly DALYs into lifetime estimates.

These individual DALYs are interpreted as the expected loss of healthy life years for an individual caused by having the genetic exposure at birth. Finally, both population-attributable DALYs and individual DALYs can be summed up across the 80 diseases to arrive at the total impact of the genetic exposure.

Note that the attributable DALYs or the population-attributable fractions for multiple exposures cannot be added together to estimate the effect of jointly intervening on multiple exposures Thus, summing the attributable DALYs for multiple genetic exposures does not prove a correct estimate for the counterfactual joint intervention on multiple exposures for example, summing attributable DALYs of two variants.

Also see Witte et al. Assuming that there is no uncertainty in the DALY estimates from GBD and the estimated population prevalence of the exposures for example, allele frequencies , for a single disease both attributable individual and population DALYs are a deterministic function of the HRs between the exposure and the diseases.

Therefore, CIs for the effect of a genetic exposure on attributable DALYs through one disease was estimated using the delta method.

Estimating the total attributable DALYs through the 80 examined diseases is less straightforward, as the HRs for different diseases are not independent for example, ischemic heart disease and lower extremity peripheral artery disease are comorbid, so risk variants tend to increase risk for both.

Bootstrapping was not computationally feasible, so we estimated the uncertainty via resampling the multivariate normal distribution of the log HR estimates.

The coefficients follow a multivariate normal distribution:. We estimate β e and σ e from the 80 Cox models for each disease for common variants we use the meta-analysis estimates from FinnGen and UKB. We restricted the correlation estimation to unshrunk variants to to make the coefficients reflect sampling variability, not true effects.

to emulate the sampling distribution of the vector of log HRs across all diseases that accounts for dependence in log HRs between diseases. We then use the 2. Patients and control participants in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act.

Alternatively, separate research cohorts, collected before the Finnish Biobank Act came into effect in September and start of FinnGen August , were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea Finnish Medicines Agency , the National Supervisory Authority for Welfare and Health.

Recruitment protocols followed the biobank protocols approved by Fimea. The FinnGen study is approved by the Finnish Institute for Health and Welfare permit nos.

UKB obtained ethics approval from the North West Multicentre Research Ethics Committee, which covers the United Kingdom approval no. Our analyses were conducted under the UKB application no. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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Public Health 40 , —

Recent advances in research on polygenic scores and their application to risk stratification have created renewed interest in the use of genetic information in prevention of common diseases. The debate thus far has been largely polarised, with strong proponents and critics about the utility of such information.

In our report we look specifically at polygenic scores and cardiovascular disease to explore their potential for prevention. The report outlines the science that underlies polygenic scores, and their potential applications.

It also provides an overview of the current state of cardiovascular disease prevention, and considers the evidence for the implementation. We conclude that, whilst there is indeed potential in the field, it remains under development. Initial indications are that polygenic scores for coronary artery disease CAD can improve population stratification, and hence potentially support more effective prevention.

Stratification can be an important mechanism for informing prevention, by allowing interventions to be targeted towards those at greatest risk of disease, and is established practice in cardiovascular disease prevention efforts.

Exposure to radiation, chemicals, infections, and other environmental factors contribute to genetic changes that result in atypical DNA. However, in most cases , doctors do not know why leukemia occurs. Learn more about AML genetic mutations. People can inherit genetic risk factors, or their genes can change because of environmental triggers.

The authors of a study found that certain gene mutations, specifically FLT3-ITD and NRAS mutations, frequently appear in people with AML-M5, a type of AML that forms in immature white blood cells.

However, some subtypes of the following leukemias may be due to inherited genetic features:. There may be another inherited condition in the family with the same genetic change, such as a platelet deficiency or immune condition.

One form of leukemia, ALL, is more common among children and teenagers than adults. The risk of developing ALL is higher during childhood but falls as people enter their 20s. It rises again after the age of 50 years. The risk of developing most cancers increases with age. The American Cancer Society ACS estimates that 9 out of 10 people with CLL are aged 50 years or older.

Males are slightly more likely to have CLL than females. ALL also occurs more often in males than females. The ACS notes that leukemia is more common in white Americans than in Black Americans. However, research suggests Black people may face poorer prognoses due to disparities in access to care, diagnosis, and treatment.

How do racial disparities affect cancer rates and treatment for Black Americans? The ACS states that the following inherited genetic syndromes may increase the risk of ALL:. Benzene is a chemical present in many products, including gasoline, glue, cleaning supplies, cigarettes, detergents, and dyes.

According to the CDC Centers for Disease Control and Prevention , benzene is in the top 20 most produced chemicals in the United States.

What is the link between benzene and leukemia? In most cases, it is not clear why leukemia develops. However, being aware of the risk factors can help people take precautions. In rare cases , people inherit genetic traits that increase their risk of leukemia, but it does not mean they will develop it.

Scientists have found genetic links to various types of this disease. Most cases of leukemia do not have an obvious cause, but exposure to high levels of radiation and certain toxins can increase the risk.

It can also occur in people with a history of radiation therapy and chemotherapy and in those with certain genetic conditions, such as Down syndrome and Fanconi anemia. Genetic testing can show doctors which kind of genetic changes are present in cancer cells, and this can help identify the type of leukemia.

However, it cannot show if someone is likely to inherit or pass on the disease. Volunteer Be an Advocate Volunteer Opportunities for Organizations. Fundraising Events Relay For Life Making Strides Against Breast Cancer Walk Endurance Events Galas, Balls, and Parties Golf Tournaments.

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All About Cancer Cancer Types Breast Cancer Breast Cancer Risk and Prevention. Breast Cancer About Breast Cancer. Download Section as PDF. Breast Cancer Risk Factors You Cannot Change. On this page.

Being born female Getting older Inheriting certain gene changes Having a family history of breast cancer Having a personal history of breast cancer Race and ethnicity Being taller Having dense breast tissue Having certain benign breast conditions Starting menstrual periods early Going through menopause later Having radiation to your chest Exposure to diethylstilbestrol DES.

For information on other known and possible breast cancer risk factors, see: Lifestyle-related Breast Cancer Risk Factors Factors with Unclear Effects on Breast Cancer Risk Disproven or Controversial Breast Cancer Risk Factors Being born female This is the main risk factor for breast cancer.

Getting older As you get older, your risk of breast cancer goes up. If you have inherited a mutated copy of either gene from a parent, you have a higher risk of breast cancer.

On average, a woman with a BRCA1 or BRCA2 gene mutation has up to a 7 in 10 chance of getting breast cancer by age This risk is also affected by how many other family members have had breast cancer. It goes up if more family members are affected. Women with one of these mutations are more likely to be diagnosed with breast cancer at a younger age, as well as to have cancer in both breasts.

Women with one of these gene changes also have a higher risk of developing ovarian cancer and some other cancers. Men who inherit one of these gene changes also have a higher risk of breast and some other cancers. In the United States, BRCA mutations are more common in Jewish people of Ashkenazi Eastern Europe origin than in other racial and ethnic groups, but anyone can have them.

ATM : The ATM gene normally helps repair damaged DNA or helps kill the cell if the damaged can't be fixed. Inheriting 2 abnormal copies of this gene causes the disease ataxia-telangiectasia.

Inheriting one abnormal copy of this gene has been linked to a high rate of breast cancer in some families. PALB2 : The PALB2 gene makes a protein that interacts with the protein made by the BRCA2 gene. Mutations in this gene can lead to a higher risk of breast cancer. TP53 : The TP53 gene helps stop the growth of cells with damaged DNA.

Inherited mutations of this gene cause Li-Fraumeni syndrome. People with this syndrome have an increased risk of breast cancer, as well as some other cancers such as leukemia, brain tumors, and sarcomas cancers of bones or connective tissue.

This mutation is a rare cause of breast cancer. CHEK2 : The CHEK2 gene is another gene that normally helps with DNA repair. A CHEK2 mutation increases breast cancer risk. PTEN : The PTEN gene normally helps regulate cell growth.

Inherited mutations in this gene can cause Cowden syndrome , a rare disorder that puts people at higher risk for both cancer and benign non-cancer tumors in the breasts, as well as growths in the digestive tract, thyroid, uterus, and ovaries.

CDH1 : Inherited mutations in this gene cause hereditary diffuse gastric cancer , a syndrome in which people develop a rare type of stomach cancer. Women with mutations in this gene also have an increased risk of invasive lobular breast cancer. STK11 : Defects in this gene can lead to Peutz-Jeghers syndrome.

People affected with this disorder have pigmented spots on their lips and in their mouths, polyps abnormal growths in the urinary and digestive tracts, and a higher risk of many types of cancer, including breast cancer. Having 2 first-degree relatives increases her risk by about 3-fold.

Women with a father or brother who has had breast cancer also have a higher risk of breast cancer. Having a personal history of breast cancer A woman with cancer in one breast has a higher risk of developing a new cancer in the other breast or in another part of the same breast.

Race and ethnicity Overall, White women are slightly more likely to develop breast cancer than African American women, although the gap between them has been closing in recent years. Being taller Many studies have found that taller women have a higher risk of breast cancer than shorter women.

Having dense breast tissue Breasts are made up of fatty tissue, fibrous tissue, and glandular tissue. Having certain benign breast conditions Women diagnosed with certain types of benign non-cancer breast conditions may have a higher risk of breast cancer.