Genome-wide association studies (GWASs) and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem. Computer simulations of polygenic late-onset diseases (LODs) in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores (PRSs) becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes. The incidence rate for LODs grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for GWASs overrepresent older individuals with lower PRSs, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and GWASs. It also explains the relatively constant-with-age heritability found for LODs of lower prevalence, exemplified by cancers.

Gene therapy techniques and genetic knowledge may sufficiently advance, within the next few decades, to support prophylactic gene therapy for the prevention of polygenic late-onset diseases. The risk of these diseases may, hypothetically, be lowered by correcting the effects of a subset of common low effect gene variants. In this paper, simulations show that if such gene therapy were to become technically possible; and if the incidences of the treated diseases follow the proportional hazards model with a multiplicative genetic architecture composed of a sufficient number of common small effect gene variants, then: (a) late-onset diseases with the highest familial heritability will have the largest number of variants available for editing; (b) diseases that currently have the highest lifetime risk, particularly those with the highest incidence rate continuing into older ages, will prove the most challenging cases in lowering lifetime risk and delaying the age of onset at a population-wide level; (c) diseases that are characterized by the lowest lifetime risk will show the strongest and longest-lasting response to such therapies; and (d) longer life expectancy is associated with a higher lifetime risk of these diseases, and this tendency, while delayed, will continue after therapy.

For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display the risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer's disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. Computer simulations have determined that genome-wide association studies of the late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display a risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. In this research, computer simulations have demonstrated that genome-wide association studies of late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

Background: Genome-wide association studies and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called "missing heritability" problem. Methods:Computer simulations of polygenic late-onset diseases in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer's disease, coronary artery disease, cerebral stroke, and type 2 diabetes. Results:The incidence rate for late-onset diseases grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for genomewide association studies overrepresent older individuals with lower polygenic risk scores, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and genome-wide association studies. It also explains the relatively constant-with-age heritability found for late-onset diseases of lower prevalence, exemplified by cancers. Conclusions:For late-onset polygenic diseases showing high cumulative incidence together with high initial heritability, rather than using relatively old agematched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genomewide association studies.Algorithm 1: Sampling individuals diagnosed with a disease proportionately to their polygenic odds ratio and incidence rate. for age = 1 to M axAge do number I ll T hisYear = I(age)· N // N is unaffected population for i = 1 to number I ll T hisYear do HRsum = 0 // will recalculate sum of all HRs for u = 1 to N do HRsum = HRsum + ORt oHR(G u ) // calculate the HR total LOOKU P(add, HRsum, u) // add u th individual to the lookup table r and = RandomN umber(0, HRsum) // pick a random number ill = LOOKU P( f ind, r and, N ) // found newly diagnosed N = N − 1 // decrement in number of healthy individuals P r ocessAndAnal yze(ill) Note: an individual makes a sampling target proportionate to the hazard ratio (HR) in the LOOKU P() table.Odds ratios (ORs) are converted to HRs, similar to the approach taken by . An individual with an HR of 15 will be 150 times more likely to be sampled than an individual with an HR of 0.1. P r ocessAndAnal yze() moves newly diagnosed individual from the healthy to the ill population pool, accounts for allele distribution, case/control ORs, etc.Descriptively, the algorithm works as follows. In this prospective simulation, each next individual to be diagnosed with an LOD is chosen propo...

In a few decades, gene therapy techniques and genetic knowledge may sufficiently advance to support prophylactic gene therapy to prevent late-onset diseases (LODs). Polygenic risk scores and risk allele distribution of diagnosed individuals change with LOD diagnosis age, which may complicate statistical risk estimates. Population simulation naturally accounted for this effect. It quantified the correlation between aging process, polygenic score and hazard ratio of LODs at all ages based on clinical incidence rate and familial heritability for eight highly prevalent diseases. Simulations confirmed that the hypothetical gene therapy would be very beneficial in delaying the onset age and lowering lifetime risk of LODs. Longevity counterbalances the gains, with type 2 diabetes, stroke, and coronary artery disease regaining the pre-treatment baseline with 10-15 years of longer life. Alzheimer's disease proves most resistant, surpassing the baseline lifetime risk within 5 years longevity improvement. Characteristics of cancers may bring longer lasting benefits.

With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early- to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk; however, the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If the preventive heritable gene therapies were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.

DNA plasmids are an essential tool for delivery and expression of RNAs and proteins in cell culture experiments. The preparation of plasmids typically involves a laborious process of bacterial cloning, validation, and purification. While the expression plasmids can be designed and ordered from the contract manufacturers, the cost may be prohibitive when a large number of plasmids is required. We have developed an efficient fully synthetic method and protocol that enables the production of circularized DNA containing expression elements ready for transfection in as little as 3 hours, thereby eliminating the bacterial cloning steps. The protocol describes how to take a linear double-stranded DNA fragment and efficiently circularize and purify this DNA fragment with minimal hands-on time. As proof of the principle, we applied Circular Vector expressing engineered prime editing guide RNA (epegRNA) in cell culture, and demonstrated matching and even exceeding performance of this method as compared to guides expressed by plasmids. The method’s speed of preparation, low cost, and ease of use will make it a useful tool in applications requiring the expression of short RNAs and proteins.