Age At Death

Age at death, often referred to as lifespan or longevity, represents the total duration of an individual’s life. It is a fundamental biological trait reflecting the cumulative impact of genetic predispositions, environmental factors, lifestyle choices, and the presence or absence of diseases throughout life. Understanding the factors that influence age at death is crucial for comprehending the biological processes of aging and for promoting healthy longevity.

The biological basis of age at death is complex and polygenic, meaning it is influenced by multiple genes working in concert with environmental factors. Genetic variants can influence an individual’s susceptibility to age-related diseases, the efficiency of cellular repair mechanisms, and overall physiological resilience. Research, including genome-wide association studies (GWAS), has identified specific genetic loci where certain alleles are associated with either an increased or decreased risk of mortality^[1]. These studies aim to uncover the genetic architecture underlying human longevity and vulnerability to age-related decline. For example, specific genetic variants have been linked to the age of onset for conditions like amyotrophic lateral sclerosis ^[2]and age-related macular degeneration^[3], both of which can significantly impact health and lifespan.

Clinically, understanding the genetic and environmental determinants of age at death can offer insights into personalized health management. Identifying individuals with genetic predispositions to shorter lifespans or specific age-related conditions could enable early interventions, targeted screenings, and lifestyle modifications to mitigate risks and potentially extend healthy years. This knowledge contributes to the development of strategies for preventing or delaying the onset of chronic diseases that commonly affect older populations.

From a societal perspective, age at death has profound implications for public health, demographics, and economic planning. As global populations age, understanding the factors that contribute to longevity and healthy aging becomes increasingly important for healthcare systems, social support structures, and workforce planning. Research into the genetic and environmental influences on age at death contributes to broader efforts to improve quality of life in later years and address the challenges of an aging society.

Limitations

Understanding the genetic factors influencing age at death is complex, and current research, while valuable, is subject to several limitations. These considerations are crucial for interpreting findings and guiding future studies.

Methodological and Statistical Considerations

Studies investigating age at death often face challenges related to statistical power and replication. While meta-analyses enhance the ability to detect genetic associations, individual cohorts or sub-analyses may have insufficient sample sizes, potentially leading to inflated effect sizes for initial discoveries and difficulties in subsequent validation^[4] The need for follow-up studies to identify additional loci and confirm initial findings underscores the dynamic nature of genetic discovery for complex traits ^[3] Furthermore, variations in genotyping platforms and imputation methodologies across different research efforts can introduce inconsistencies, affecting the comparability and synthesis of results ^[3]

Generalizability and Phenotypic Definition

A significant limitation in understanding the genetics of age at death concerns the generalizability of findings across diverse populations. Many genetic association studies are conducted within specific cohorts or populations, such as those focusing on particular ancestries, which may not adequately represent global genetic diversity^[5]This specificity can restrict the applicability of identified genetic variants to broader populations, as genetic architectures and environmental exposures vary significantly among different groups. Moreover, while age at death is a definitive endpoint, the complex interplay of factors contributing to mortality, or the nuanced definitions of “aging” used as a proxy, can introduce variability in how the phenotype is captured across studies, potentially impacting the interpretation of genetic associations^[1]

Environmental Influence and Unexplained Genetic Variance

The genetic basis of age at death is intricately intertwined with a myriad of environmental factors and lifestyle choices, presenting a significant challenge for comprehensive genetic studies. Most research identifies genetic loci without fully dissecting the complex gene–environment interactions that profoundly influence an individual’s lifespan, potentially confounding observed genetic associations. Consequently, a substantial portion of the heritability for age at death, akin to other complex human traits, often remains unexplained by currently identified genetic variants^[6] This “missing heritability” suggests that many genetic influences, including rare variants, structural variations, or complex epistatic effects, are yet to be discovered, indicating a need for more comprehensive genomic and environmental data integration to fully elucidate the genetic architecture of longevity.

Variants

The intricate genetic landscape influencing human longevity and age at death involves numerous genes and their variants, which collectively modulate cellular processes, metabolic pathways, and organ function. Among these, variants associated with regulatory non-coding RNAs and genes involved in fundamental cellular activities, such as metabolism and neural signaling, are of particular interest. Studies have consistently identified single nucleotide polymorphisms (SNPs) across the genome that are associated with age at death and other age-related phenotypes, highlighting the complex genetic architecture underlying the human lifespan^[7]

The OSBPL9P3 gene, also known as LINC02748, is a long non-coding RNA (lncRNA) pseudogene. LncRNAs are increasingly recognized for their vital regulatory roles in gene expression, chromatin organization, and various cellular processes, including inflammation and lipid metabolism. While pseudogenes were historically considered non-functional, many are now understood to act as lncRNAs or microRNA sponges, thereby influencing the expression of protein-coding genes. Variant rs1528753 , located in or near this pseudogene, could potentially affect the stability, expression, or regulatory capacity of OSBPL9P3. Such alterations may subtly impact cellular homeostasis and stress responses, which are critical determinants of healthy aging and overall longevity.

Another genomic region of interest involves the MTHFD2P5 and PCLO genes, where the variant rs2371208 is located. MTHFD2P5 is a pseudogene related to MTHFD2, an enzyme crucial for mitochondrial one-carbon metabolism, which is essential for nucleotide synthesis, cellular methylation, and maintaining mitochondrial health. Nearby, the PCLO gene (Piccolo) encodes a large scaffolding protein that plays a key role at the presynaptic active zones of neurons, modulating neurotransmitter release and synapse structure. Thers2371208 variant could potentially affect the regulation or function of MTHFD2P5, PCLO, or both. Genetic variations impacting mitochondrial function, cellular metabolism, or synaptic integrity can have significant consequences for neurodegeneration, cognitive function, and the overall pace of aging. Research exploring time to death and other aging metrics frequently uncovers genetic associations that shed light on these fundamental biological processes^[1]

Key Variants

RS ID	Gene	Related Traits
rs1528753	OSBPL9P3 - LINC02748	age at death vaginal microbiome measurement
rs2371208	MTHFD2P5 - PCLO	age at death vaginal microbiome measurement

Operational Definition and Ascertainment

Age at death is precisely defined as a continuous, quantitative survival trait, representing the duration of an individual’s life^[7]. Its accurate ascertainment involves a multi-faceted approach, including routine collection of death certificates, review of hospital and nursing home records, autopsy reports, and searches of obituaries or the National Death Index ^[7]. In instances of insufficient information, next of kin may be interviewed by senior investigators to ensure comprehensive data collection ^[7]. This meticulous process culminates in a review by an endpoint panel of three senior investigators, who adjudicate the date and cause of death, ensuring the reliability of this critical demographic and clinical variable ^[7].

While age at death itself is a dimensional measure, the underlying causes of death are systematically classified into distinct categories, such as coronary heart disease, stroke, other cardiovascular disease (CVD), cancer, other causes, or unknown cause^[7]. This nosological classification allows for the detailed analysis of mortality patterns and their genetic or environmental determinants^[7]. Furthermore, age at death is often studied in conjunction with other age-related phenotypes, including longevity, age at natural menopause, age at menarche, and the age of onset for various conditions like Alzheimer’s disease, amyotrophic lateral sclerosis, or age-related macular degeneration^[8]. These related traits provide a comprehensive conceptual framework for understanding the full spectrum of aging processes and their genetic correlates^[7].

Measurement Criteria and Analytical Frameworks

The scientific investigation of age at death relies on rigorous measurement criteria and sophisticated analytical frameworks. Survival analysis techniques, such as Cox proportional hazards models, are commonly employed to generate martingale residuals for its study^[7]. These models are typically sex-specific and adjusted for a range of influential covariates, including birth cohort, education level, current smoking status, obesity, hypertension, elevated cholesterol, and diabetes^[7]. Specific thresholds define these covariates, such as a body mass index ≥30 kg/m2 for obesity, blood pressure ≥140/90 mmHg or antihypertensive treatment for hypertension, and cholesterol >239 mg/dL for elevated cholesterol^[7]. Such precise operational definitions and adjustments are crucial for isolating genetic associations and understanding the complex interplay of factors influencing human lifespan ^[7].

Causes of Age at Death

The age at which an individual dies is a complex trait influenced by a multifaceted interplay of genetic predispositions, environmental exposures, developmental timings, and the onset of various age-related conditions. Understanding these causal factors requires an integrated approach, drawing from various fields of research.

Genetic Architecture of Lifespan

Longevity and the age at death are significantly influenced by an individual’s genetic makeup. Research, including genome-wide association studies (GWAS), has identified numerous genetic correlates of longevity, with specific variants impacting the risk of mortality^[7]. For instance, certain coded alleles have been linked to either an increased or decreased risk of mortality^[1]. Beyond overall lifespan, genetics modulate the age of onset for serious conditions such as amyotrophic lateral sclerosis, with a locus on 1p34.1 implicated in this modulation ^[9]. Furthermore, polygenic risk, stemming from the cumulative effect of many genetic variants, contributes to susceptibility to age-related phenotypes like macular degeneration^[3], and influences key developmental timings such as age at menarche ^[4] and age at natural menopause ^[6], all of which can correlate with overall lifespan.

Environmental and Lifestyle Influences on Health and Longevity

Environmental factors and lifestyle choices play a crucial role in shaping an individual’s health trajectory and, consequently, their age at death. Longitudinal genome-wide association studies highlight the impact of various factors on cardiovascular disease risk^[10]. These cardiovascular risk factors are well-known to be influenced by diet, physical activity, and broader socioeconomic conditions, all of which contribute to the development of chronic diseases that can significantly reduce lifespan. Such environmental and lifestyle elements interact with an individual’s biological predispositions, influencing the onset and progression of conditions that ultimately impact longevity.

The timing of developmental milestones and the accumulation of age-related changes are significant contributors to the age at death. Genetic studies have identified loci influencing the age at menarche^[4] and age at natural menopause ^[6], both of which are developmental markers that can be associated with broader health and aging processes. The progressive biological changes inherent to aging itself lead to increased vulnerability to disease and mortality^[1]. The presence of comorbidities, such as age-related macular degeneration^[3] or amyotrophic lateral sclerosis ^[9], significantly impacts an individual’s overall health and can accelerate decline, thereby reducing age at death. These age-related changes and disease processes are influenced by a complex interplay of genetic predispositions and environmental factors throughout an individual’s life.

Cellular and Molecular Foundations of Aging

The duration of life, or age at death, is fundamentally influenced by intricate cellular and molecular processes that govern the body’s maintenance and repair mechanisms. Key among these are the efficiency of mitochondrial function, which is critical for cellular energy production, and the robustness of DNA repair pathways, essential for maintaining genomic integrity. Disruptions in mitochondrial function, for instance, have been observed in age-related changes within specific cell types, highlighting their role in the aging process^[11]. Similarly, the effectiveness of DNA repair mechanisms is directly linked to the accumulation of cellular damage over time, a hallmark of aging. Furthermore, the immune system’s function, including its ability to identify and eliminate compromised cells, plays a crucial role; age-related decline in immune responses can contribute to increased susceptibility to diseases and overall physiological deterioration.

These molecular pathways are interconnected, forming a complex regulatory network that dictates cellular resilience and longevity. Signaling pathways respond to cellular stress, coordinating DNA repair, modulating metabolic processes, and influencing immune cell activity. The balance within these networks, maintained by various key biomolecules such as enzymes, receptors, and transcription factors, determines how well cells can cope with environmental insults and intrinsic damage. When these systems become overwhelmed or dysregulated, such as through persistent oxidative stress or inflammation, cellular functions decline, accelerating the aging process and increasing vulnerability to mortality.

Genetic and Epigenetic Regulation of Longevity

Genetic mechanisms exert a significant influence on an individual’s age at death, with numerous genes and regulatory elements contributing to variations in lifespan. Genome-wide association studies (GWAS) have identified specific genetic loci associated with overall aging and longevity, indicating a heritable component to how long individuals live^[1]. These studies explore how variations in DNA sequences, particularly single nucleotide polymorphisms (SNPs), can either increase or decrease the risk of mortality. Beyond direct effects on lifespan, genetic factors also influence age-related phenotypes, such as the timing of reproductive milestones like age at menarche and natural menopause^[4]. For example, a substantial portion of the variation in age at natural menopause is attributed to genetic factors, underscoring the genetic underpinning of specific aging processes^[6].

The function of these identified genes often relates to fundamental biological processes such as DNA repair and immune responses, pathways directly implicated in cellular aging^[11]. Regulatory elements within the genome control gene expression patterns, determining when and where genes are activated or suppressed. Epigenetic modifications, which are changes in gene activity that do not involve alterations to the underlying DNA sequence, also play a role in modulating gene expression over a lifetime. These modifications can be influenced by environmental factors and change with age, potentially altering how genes related to longevity and disease susceptibility are expressed, thereby contributing to the overall trajectory of aging and ultimately, age at death.

Systemic Homeostasis and Pathophysiological Decline

The maintenance of systemic homeostasis is critical for prolonged life, with its disruption serving as a primary driver of pathophysiological processes that contribute to age at death. As individuals age, the body’s ability to maintain stable internal conditions diminishes, leading to a cascade of functional declines across various organ systems. Disease mechanisms, often with an age-related onset, represent major threats to homeostasis. For instance, genetic loci have been identified that modulate the age of onset for neurodegenerative conditions like amyotrophic lateral sclerosis (ALS)^[2], and several loci are associated with age-related macular degeneration (AMD)^[3]. These age-related diseases exemplify how specific pathologies can arise from accumulated cellular damage and impaired repair mechanisms, ultimately impacting an individual’s health trajectory and lifespan.

The progression of these diseases and the general decline in homeostatic control involve a complex interplay of biomolecules, including critical proteins, enzymes, and hormones that regulate physiological functions. When these components malfunction or their regulatory networks are disrupted, compensatory responses may initially attempt to restore balance. However, with advancing age, these compensatory capacities often become exhausted, leading to chronic inflammation, metabolic dysfunction, and organ failure. These systemic consequences, rather than isolated events, represent the culmination of various pathophysiological processes that collectively increase the risk of mortality.

Organ-Specific Aging and Inter-Tissue Communication

Aging is not uniform across all tissues and organs; rather, it manifests with distinct organ-specific effects that collectively contribute to an individual’s age at death. Different organs and cell types exhibit varying susceptibilities to age-related changes, often due to their unique metabolic demands, regenerative capacities, and exposure to environmental stressors. For example, research has highlighted age-related changes in specific tissues, such as alterations in mouse oocytes, which include significant changes in mitochondrial function, directly impacting reproductive aging^[11]. Similarly, the eye is susceptible to age-related macular degeneration, a condition influenced by specific genetic variants and cellular processes within the retina^[3].

These localized aging processes do not occur in isolation but are intricately linked through extensive inter-tissue communication and systemic consequences. Hormones, cytokines, and metabolic byproducts circulating throughout the body can transmit signals of aging or dysfunction from one organ to another, creating a systemic environment that either promotes health or accelerates decline. For instance, chronic inflammation originating from one tissue can have detrimental effects on distant organs, contributing to a broader physiological frailty. Ultimately, age at death is a reflection of the cumulative impact of these organ-specific declines and their coordinated failure to maintain the complex network of physiological functions essential for life.

Age at death is a complex trait influenced by a multitude of interacting biological pathways and mechanisms that collectively determine an individual’s rate of aging and susceptibility to age-related diseases. While specific molecular pathways directly dictating the exact age at death are still being elucidated, research into various aging-related phenotypes, such as age at menopause, has identified fundamental cellular and molecular processes critical to biological longevity. These pathways involve maintaining cellular health, regulating metabolic efficiency, and orchestrating robust immune responses.

Genomic Integrity and DNA Repair

The faithful preservation of an organism’s genetic material is paramount for cellular function and survival. DNA repair pathways represent a critical set of regulatory mechanisms that actively counteract DNA damage, which can arise from both intrinsic cellular processes and external environmental factors. The efficiency of these pathways, encompassing various protein modifications and enzymatic activities, directly impacts the accumulation of mutations and genomic instability over time. Impaired DNA repair is a hallmark of accelerated aging, contributing to cellular senescence and dysfunction across tissues, thereby influencing the overall biological age and, consequently, an individual’s age at death^[11].

Mitochondrial Bioenergetics

Mitochondria are central organelles responsible for energy metabolism, converting nutrients into usable energy through a series of metabolic pathways, including oxidative phosphorylation. The functional integrity of mitochondria is crucial for sustaining cellular activities, and their decline is a well-established feature of aging. Changes in mitochondrial function can involve alterations in energy metabolism, a decrease in ATP biosynthesis, and an increase in harmful byproducts, leading to cellular damage and reduced resilience. Such dysregulation in mitochondrial bioenergetics impacts overall physiological health and is implicated in the progression of aging, which in turn influences the age at death^[11].

Immune System Modulation

The immune system plays a multifaceted role in maintaining health and responding to threats, involving complex signaling pathways and regulatory mechanisms. It is responsible for identifying and eliminating pathogens, clearing cellular debris, and surveilling for senescent or cancerous cells. With advancing age, the immune system undergoes a process known as immunosenescence, characterized by a decline in its adaptive capacity and an increase in chronic low-grade inflammation. This dysregulation of immune pathways can compromise the body’s ability to maintain homeostasis, increasing susceptibility to infections and chronic diseases, which are significant determinants of lifespan ^[11].

Systems-Level Regulation of Aging

The intricate interplay between genomic integrity, mitochondrial function, and immune system modulation highlights a broader systems-level integration of aging processes. These core pathways are not isolated but exhibit extensive crosstalk and network interactions, forming a hierarchical regulatory system that governs the overall biological state of an organism. The emergent properties of these interconnected networks determine an individual’s resilience to age-related stressors and contribute to the variability observed in human longevity^[1]. Understanding how these pathways are coordinated and regulated at a systems level is essential for unraveling the fundamental mechanisms of aging and identifying potential targets for interventions aimed at extending healthy lifespan.

Population Studies

Understanding the factors influencing age at death is a central goal in population studies, often approached through the examination of aging processes, longevity, and the age of onset of major diseases. Large-scale epidemiological research employs diverse methodologies to uncover the genetic and environmental determinants impacting lifespan across various populations.

Longitudinal Cohort Studies and Temporal Patterns

Major population cohorts are instrumental in investigating the complex interplay of factors contributing to aging and longevity over time. Studies like the Framingham Study have been pivotal in identifying genetic correlates of longevity and other age-related phenotypes, employing robust statistical methods such as Cox proportional hazards models for survival traits^[7]. Similarly, the Bogalusa Heart Study utilizes a longitudinal genome-wide association approach to track cardiovascular disease risk factors, providing insights into temporal patterns of health indicators that influence life expectancy^[10]. These extensive cohorts, often involving collaborations across numerous institutions like the National Institute on Aging and various university departments, enable researchers to observe health trajectories and identify critical determinants of aging across decades^[1].

Epidemiological studies delve into the prevalence and incidence patterns of age-related conditions and their demographic correlates, which collectively shape the age at death. Research has explored the genetic underpinnings of age-related diseases that significantly impact lifespan. For instance, genome-wide association studies have investigated the age of onset for severe neurodegenerative conditions such as amyotrophic lateral sclerosis (ALS)^[9]and Alzheimer’s disease^[12]. These studies often analyze large patient cohorts, like those from the University of Pittsburgh ADRC and Mayo, to understand how genetic variations influence when these diseases manifest, thereby affecting an individual’s potential lifespan. Beyond disease, studies also examine broader age-related phenotypes, such as age at menarche and menopause, leveraging meta-analyses of genome-wide association data from diverse research centers globally to identify influential genetic loci^[4], ^[11], ^[6], ^[13].

Population-Specific Effects and Ancestry Variations

Cross-population comparisons are crucial for understanding how genetic and environmental factors interact to influence age-related traits across different ancestries and geographic regions. Studies have specifically highlighted ancestry differences, such as a genome-wide association study focusing on age at menarche in African-American women ^[14]. Such research underscores the importance of studying diverse ethnic groups to identify population-specific effects and ensure that findings are generalizable. The broad geographic distribution of collaborating institutions, including centers in the Netherlands, Sweden, Finland, the United Kingdom, and various parts of the United States, further indicates an effort to capture global variations in age-related health outcomes and their genetic determinants ^[1], ^[4], ^[11]. This international collaboration helps to reveal how genetic predispositions and environmental factors may vary in their impact on aging and longevity across different human populations.

Methodological Frameworks in Aging Research

The study of age at death and related phenotypes relies heavily on advanced methodological frameworks, particularly Genome-Wide Association Studies (GWAS) and meta-analyses. These designs allow for the systematic scanning of the entire genome for genetic variants associated with traits like longevity or age of disease onset^[1], ^[7]. Large-scale meta-analyses, combining data from multiple individual GWAS, enhance statistical power and improve the ability to detect subtle genetic effects ^[4], ^[11], ^[6], ^[13]. Researchers employ a range of statistical models, including Cox proportional hazards for survival traits, logistic regression for dichotomous outcomes, and linear regression for quantitative traits, to analyze complex epidemiological data ^[7]. The representativeness of study samples and the generalizability of findings are critical considerations, often addressed by assembling large, diverse cohorts and conducting multi-center collaborations to ensure broad applicability of the results.

Frequently Asked Questions About Age At Death

These questions address the most important and specific aspects of age at death based on current genetic research.

1. My grandparents lived really long. Does that mean I will too?

Having long-lived grandparents can indicate a genetic predisposition for longevity in your family. Your genes influence things like cellular repair and how susceptible you are to age-related diseases. However, your own lifestyle, environment, and health choices also play a significant role, so it’s not a guaranteed outcome.

2. Can a healthy lifestyle really beat bad family genetics?

Yes, absolutely. While your genes influence your susceptibility to age-related conditions, lifestyle choices like diet and exercise are incredibly powerful. Early interventions, targeted screenings, and smart lifestyle modifications can significantly mitigate genetic risks and potentially extend your healthy years, even with a family history of shorter lifespans.

3. My sibling eats junk food and is fine, but I’m always worried. Why are we so different?

It’s very common for siblings to have different health outcomes, even with shared family genetics. Your individual genetic variants influence your susceptibility to diseases and how your body responds to lifestyle factors. Plus, unique environmental exposures and lifestyle choices, even subtle ones, can accumulate over time, leading to different health trajectories.

4. Is getting a DNA test useful to know my own lifespan risk?

A DNA test can offer insights into your genetic predispositions for certain age-related conditions, like specific eye or neurological disorders. This information could guide early interventions or lifestyle changes. However, current research doesn’t fully explain all genetic influences on lifespan, and findings aren’t always generalizable across all populations, so it’s one piece of a complex picture.

5. I’m from a certain background; does that change my risk for living longer?

Yes, your ethnic background can play a role in how genetic factors influence your lifespan. Many genetic association studies are conducted in specific populations, and the genetic architectures impacting longevity can vary across different ancestries. This means certain identified genetic risk factors might apply differently, or new ones might exist for your specific background.

6. Does eating healthy and exercising really make a big difference, or is it mostly my genes?

Both genes and lifestyle are crucial. While your genetic makeup influences your baseline susceptibility to age-related diseases and cellular repair efficiency, a healthy diet and regular exercise are powerful environmental factors. They can significantly reduce risks, improve physiological resilience, and help you extend your healthy years, even if you have some genetic predispositions.

7. Does my stressful job actually shorten my life, or is that just an old wives’ tale?

While the article doesn’t detail specific genes for stress, chronic stress is a significant environmental factor that can impact your overall health. It can increase susceptibility to various diseases, which in turn can influence your lifespan. Managing stress through lifestyle choices is a key component of promoting healthy longevity.

8. Why do some people seem to age faster or get sick earlier than others?

This variation stems from a complex interplay of genetic and environmental factors. Genetic variants influence your susceptibility to age-related diseases and the efficiency of your cellular repair mechanisms. These inherited differences, combined with unique environmental exposures and lifestyle choices, contribute to when and how age-related decline or specific conditions manifest.

9. If my family has a history of a serious illness, can I do anything to avoid it or delay it?

Yes, definitely. If you know you have a family history of specific illnesses, like certain neurological (e.g., ALS) or eye conditions (e.g., age-related macular degeneration), this knowledge is valuable. Early interventions, targeted screenings, and specific lifestyle modifications, often with medical guidance, can help mitigate your genetic risks and potentially delay the onset or reduce the severity of these conditions, helping you live healthier.

10. Why do doctors sometimes not have clear answers about why some people live longer?

The genetic basis of age at death is incredibly complex, and there’s still a lot we don’t fully understand. While many genetic influences have been identified, a significant portion of the heritability for lifespan, often called “missing heritability,” remains unexplained. This means many rare variants or complex genetic interactions are yet to be discovered, making definitive answers challenging.

This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

[1] Walter S, et al. “A genome-wide association study of aging.”Neurobiol Aging, vol. 33, no. 11, Nov. 2012, pp. 2707-17. PubMed, PMID: 21782286.

[2] Ahmeti KB, et al. “Age of onset of amyotrophic lateral sclerosis is modulated by a locus on 1p34.1.” Neurobiol Aging, vol. 35, no. 1, Jan. 2014, pp. 240.e1-5. PubMed, PMID: 22959728.

[3] Fritsche LG, et al. “Seven new loci associated with age-related macular degeneration.”Nat Genet, vol. 45, no. 4, Apr. 2013, pp. 433-39. PubMed, PMID: 23455636.

[4] Elks, C. E. et al. “Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.” Nat Genet, vol. 43, no. 2, 2011, pp. 150-5.

[5] Comuzzie, A. G., et al. “Novel Genetic Loci Identified for the Pathophysiology of Childhood Obesity in the Hispanic Population.”PLoS One, vol. 7, no. 12, 2012, p. e51954.

[6] He, C. et al. Genome-wide association studies identify loci associated with age at menarche and age at natural menopause. Nat Genet, 2010.

[7] Lunetta, K. L. “Genetic Correlates of Longevity and Selected Age-Related Phenotypes: A Genome-Wide Association Study in the Framingham Study.” BMC Med Genet, 2007.

[8] Kamboh, M. I. et al. “Genome-wide association analysis of age-at-onset in Alzheimer’s disease.”Mol Psychiatry, vol. 18, no. 2, 2013, pp. 248-55.

[9] Ahmeti, K. B. “Age of Onset of Amyotrophic Lateral Sclerosis Is Modulated by a Locus on 1p34.1.” Neurobiol Aging, 2012.

[10] Smith, E. N. “Longitudinal Genome-Wide Association of Cardiovascular Disease Risk Factors in the Bogalusa Heart Study.”PLoS Genet, 2010.

[11] Stolk, L et al. “Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways.”Nat Genet, 2012.

[12] Kamboh, M. I. “Genome-Wide Association Analysis of Age-At-Onset in Alzheimer’s Disease.”Mol Psychiatry, 2011.

[13] Perry, JR et al. “A genome-wide association study of early menopause and the combined impact of identified variants.” Hum Mol Genet, 2013.

[14] Demerath, E. W. et al. “Genome-Wide Association Study of Age at Menarche in African-American Women.” Hum Mol Genet, 2013.