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• W2997787910 abstract "•Mortality from age-related diseases is U-shaped with the nadir below reproductive age•Quantitative biomarkers of aging change continuously throughout life•Mutation burden causes early-life mortality and contributes to selection•Aging is best defined by damage rather than mortality and starts very early in life An increase in the probability of death has been a defining feature of aging, yet human perinatal mortality starts high and decreases with age. Previous evolutionary models suggested that organismal aging begins after the onset of reproduction. However, we find that mortality and incidence of diseases associated with aging follow a U-shaped curve with the minimum before puberty, whereas quantitative biomarkers of aging, including somatic mutations and DNA methylation, do not, revealing that aging starts early but is masked by early-life mortality. Moreover, our genetic analyses point to the contribution of damaging mutations to early mortality. We propose that mortality patterns are governed, in part, by negative selection against damaging mutations in early life, manifesting after the corresponding genes are first expressed. Deconvolution of mortality patterns suggests that deleterious changes rather than mortality are the defining characteristic of aging and that aging begins in very early life. An increase in the probability of death has been a defining feature of aging, yet human perinatal mortality starts high and decreases with age. Previous evolutionary models suggested that organismal aging begins after the onset of reproduction. However, we find that mortality and incidence of diseases associated with aging follow a U-shaped curve with the minimum before puberty, whereas quantitative biomarkers of aging, including somatic mutations and DNA methylation, do not, revealing that aging starts early but is masked by early-life mortality. Moreover, our genetic analyses point to the contribution of damaging mutations to early mortality. We propose that mortality patterns are governed, in part, by negative selection against damaging mutations in early life, manifesting after the corresponding genes are first expressed. Deconvolution of mortality patterns suggests that deleterious changes rather than mortality are the defining characteristic of aging and that aging begins in very early life. Aging involves the continuous accumulation of deleterious changes, consequential loss of function, development of age-related diseases, and ultimately death. Perhaps, the most characteristic, albeit not universal (Jones et al., 2014Jones O.R. Scheuerlein A. Salguero-Gómez R. Camarda C.G. Schaible R. Casper B.B. Dahlgren J.P. Ehrlén J. García M.B. Menges E.S. et al.Diversity of ageing across the tree of life.Nature. 2014; 505: 169-173Crossref PubMed Scopus (587) Google Scholar), feature of organismal aging has been the age-related increase in frailty and mortality, i.e., “something ages if it is more likely to fall apart tomorrow than today” (Gavrilov and Gavrilova, 2004Gavrilov L. Gavrilova N. Why we fall apart: engineering’s reliability theory explains human aging.IEEE Spectr. 2004; 41: 31-35Crossref Google Scholar). Mortality of all common model organisms used in aging studies (mice, zebrafish, flies, nematodes, and budding yeast) and many non-model organisms (Nussey et al., 2013Nussey D.H. Froy H. Lemaitre J.-F. Gaillard J.-M. Austad S.N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology.Ageing Res. Rev. 2013; 12: 214-225Crossref PubMed Scopus (434) Google Scholar) follows the pattern of age-dependent growth. If mortality indeed represents aging, it would then be tempting to extrapolate the relationship between these two processes to the entire lifespan and infer the age corresponding to the initiation of the aging process by identifying the moment when age-specific mortality starts to increase. Human aging closely follows this mortality pattern, which is thought to be a defining feature of aging. However, the early-life mortality rate in humans starts high and declines from the prenatal period to puberty, where it reaches minimum (Levitis, 2011Levitis D.A. Before senescence: the evolutionary demography of ontogenesis.Proc. Biol. Sci. 2011; 278: 801-809Crossref PubMed Scopus (47) Google Scholar); it subsequently starts to increase again, forming a U-shaped pattern. Some studies have reported that the nadir of mortality may lie even before reproductive age (Milne, 2006Milne E.M. When does human ageing begin?.Mech. Ageing Dev. 2006; 127: 290-297Crossref PubMed Scopus (18) Google Scholar). This was widely interpreted as a support for the idea that the aging process begins around that time (Rattan, 2006Rattan S.I. Theories of biological aging: genes, proteins, and free radicals.Free Radic. Res. 2006; 40: 1230-1238Crossref PubMed Scopus (271) Google Scholar); it is also thought to be consistent with evolutionary considerations, wherein the strength of natural selection remains constant during early life but declines once organisms reach the reproductive age (Kirkwood and Austad, 2000Kirkwood T.B. Austad S.N. Why do we age?.Nature. 2000; 408: 233-238Crossref PubMed Scopus (1273) Google Scholar). This concept originates in the work of Hamilton (Hamilton, 1966Hamilton W.D. The moulding of senescence by natural selection.J. Theor. Biol. 1966; 12: 12-45Crossref PubMed Scopus (1459) Google Scholar), who thought that development and aging are parts of the same phenomenon that defines the mortality pattern and therefore that aging starts at the onset of reproduction (Levitis and Martínez, 2013Levitis D.A. Martínez D.E. The two halves of U-shaped mortality.Front. Genet. 2013; 4: 31Crossref PubMed Scopus (12) Google Scholar). The basis of the decline of mortality in early life that is widespread among humans, animals, and even unicellular eukaryotes is poorly understood (Levitis, 2011Levitis D.A. Before senescence: the evolutionary demography of ontogenesis.Proc. Biol. Sci. 2011; 278: 801-809Crossref PubMed Scopus (47) Google Scholar). If one defines aging as a process that leads to an increase in the probability of death with age across the whole lifetime, the process of aging would have to start at the age of minimal mortality, and organisms in their early life would develop while becoming biologically younger until the age of minimal mortality. Thus, the period of development would be accompanied by reversed aging, when levels of damage inherited or accumulated early in life is decreased with age (e.g., by repair mechanisms or damage dilution due to extensive cell proliferation and an increase in body volume during embryogenesis and early childhood). However, the relationship between aging and mortality is not set in stone. In what follows, we define aging as the accumulation of damage such as mutations, methylation changes, protein oxidation, and other deleterious changes with age (Gladyshev, 2016Gladyshev V.N. Aging: progressive decline in fitness due to the rising deleteriome adjusted by genetic, environmental, and stochastic processes.Aging Cell. 2016; 15: 594-602Crossref PubMed Scopus (120) Google Scholar). Since the early research it has been thought that germline mutations drive aging (Medawar, 1946Medawar P.B. Old age and natural death.Mod Quart. 1946; 2: 30-49Google Scholar), and this concept was later applied in Hamilton’s seminal work (Hamilton, 1966Hamilton W.D. The moulding of senescence by natural selection.J. Theor. Biol. 1966; 12: 12-45Crossref PubMed Scopus (1459) Google Scholar) to support the conclusion that negative senescence cannot take place. However, this proposition appears to disagree with the discovery of species characterized by decreased mortality and increased fecundity over much of their lifespan (Vaupel et al., 2004Vaupel J.W. Baudisch A. Dölling M. Roach D.A. Gampe J. The case for negative senescence.Theor. Popul. Biol. 2004; 65: 339-351Crossref PubMed Scopus (245) Google Scholar). Another relevant concept is the notion of trade-off between fecundity and aging. Studies have shown that decreased reproduction and the delayed onset of puberty are associated with longer lifespan (Mostafavi et al., 2017Mostafavi H. Berisa T. Day F.R. Perry J.R.B. Przeworski M. Pickrell J.K. Identifying genetic variants that affect viability in large cohorts.PLoS Biol. 2017; 15: e2002458Crossref PubMed Scopus (44) Google Scholar) and exceptional longevity (Tabatabaie et al., 2011Tabatabaie V. Atzmon G. Rajpathak S.N. Freeman R. Barzilai N. Crandall J. Exceptional longevity is associated with decreased reproduction.Aging (Albany N.Y.). 2011; 3: 1202-1205Crossref PubMed Scopus (39) Google Scholar), whereas early puberty has been linked to the elevated risk of heart disease and diabetes (Day et al., 2015Day F.R. Elks C.E. Murray A. Ong K.K. Perry J.R. Puberty timing associated with diabetes, cardiovascular disease and also diverse health outcomes in men and women: the UK Biobank study.Sci. Rep. 2015; 5: 11208Crossref PubMed Scopus (275) Google Scholar). These findings were interpreted as supporting the antagonistic pleiotropy (Williams, 1957Williams G.C. Pleiotropy, natural selection, and the evolution of senescence.Evolution. 1957; 11: 398-411Crossref Google Scholar) and disposable soma (Kirkwood, 1977Kirkwood T.B.L. Evolution of ageing.Nature. 1977; 270: 301-304Crossref PubMed Scopus (1361) Google Scholar) theories of aging (Tabatabaie et al., 2011Tabatabaie V. Atzmon G. Rajpathak S.N. Freeman R. Barzilai N. Crandall J. Exceptional longevity is associated with decreased reproduction.Aging (Albany N.Y.). 2011; 3: 1202-1205Crossref PubMed Scopus (39) Google Scholar) and proving that puberty defines the start of aging. However, delayed puberty may prolong life by decelerating aging rather than by shifting its onset. Moreover, the opposite trend is observed in many species: honeybee queens live much longer than workers (Page and Peng, 2001Page Jr., R.E.J. Peng C.Y.S. Aging and development in social insects with emphasis on the honey bee, Apis mellifera L.Exp. Gerontol. 2001; 36: 695-711Crossref PubMed Scopus (256) Google Scholar), and in Ansell’s mole-rats, both female and male breeders live longer than non-breeders (Dammann and Burda, 2006Dammann P. Burda H. Sexual activity and reproduction delay ageing in a mammal.Curr. Biol. 2006; 16: R117-R118Abstract Full Text Full Text PDF PubMed Scopus (72) Google Scholar). The scientific community is currently divided over the question of when aging begins; existing opinions include conception, birth of the organism, nadir of mortality, puberty, completion of development, age of most transcriptional reversals, and perhaps other phases of human life (Allison et al., 2016Allison B.J. Kaandorp J.J. Kane A.D. Camm E.J. Lusby C. Cross C.M. Nevin-Dolan R. Thakor A.S. Derks J.B. Tarry-Adkins J.L. et al.Divergence of mechanistic pathways mediating cardiovascular aging and developmental programming of cardiovascular disease.FASEB J. 2016; 30: 1968-1975Crossref PubMed Scopus (45) Google Scholar). In this work, we sought to address this question quantitatively. The analysis we present points to very early life as the beginning of aging; this analysis has also led us to broader insights into the causes of early-life mortality, its role in negative selection against parental deleterious alleles, and persistence of populations in the face of high mutation rates. To evaluate the relationship between mortality and aging across the entire lifespan, we first examined overall patterns of mortality in human populations, where the most resolved data are available. Consistent with previous data (Milne, 2006Milne E.M. When does human ageing begin?.Mech. Ageing Dev. 2006; 127: 290-297Crossref PubMed Scopus (18) Google Scholar), the mortality rate is U-shaped, and the nadir of all-cause mortality in developed countries lies around the age of 9 years for both sexes (Figure 1A). Men show higher mortality rates in both arms of the U-shaped curve than women. As causes of death may be different between children and adults, we were particularly interested in the mortality from diseases typically associated with aging. Interestingly, mortality patterns from heart disease, infection, and sepsis are also U-shaped, with the nadir at the age of 9 years (Figure 1B). Moreover, mortality from most other causes behaves similarly (Figures 1B and S1A), with higher mortality for men than for women. In nearly every case, the nadir is observed long before the age of puberty. Mortality resulting from causes specific to childhood shows an age-related decrease, with the maximum at birth (Figure 1C). The pattern of higher early-life mortality and the timing of its decrease are preserved when injury-related mortality is subtracted (Figure S1B). As cancer incidence grows with age in adult life, we further analyzed its rate throughout the whole lifespan and found it to follow the same U-shaped pattern in both men and women (Figure 1D). The number of doctor’s office visits, even after excluding visits associated with injury and preventive care, also shows a similar pattern, with the minimum during childhood, although the nadir cannot be reliably defined (Figure 1E). Overall, we observed the U-shaped pattern across many analyzed features, and its minimum is well below the age when humans start to reproduce. The rise of mortality in the period preceding reproductive age (i.e., starting at 9 years) as well as the corresponding rise in the incidence of disease represents a challenge to the evolutionary inference of the beginning of aging after the completion of development. To examine the nature of high early-life mortality and its relationship to the process of biological aging, we analyzed the behavior of quantitative biomarkers of aging, focusing on age-associated mutation accumulation in somatic tissues. We previously found that it can be assessed by following age-related mutations in cancers; i.e., somatic mutations in tumors that are additional relative to healthy tissues of the same patient (Podolskiy et al., 2016Podolskiy D.I. Lobanov A.V. Kryukov G.V. Gladyshev V.N. Analysis of cancer genomes reveals basic features of human aging and its role in cancer development.Nat. Commun. 2016; 7: 12157Crossref PubMed Scopus (52) Google Scholar). In contrast to mortality rates, mutations do not show U-shaped patterns. Instead, they increase with age throughout the whole lifetime for the analyzed cancer types (Figure 2A). As the majority of age-related somatic mutations are neutral or mildly deleterious, they may be viewed as proxies for molecular damage, suggesting that damage also accumulates with age, even at pre-reproductive age. Therefore, early-life mortality appears to be unrelated to aging if the latter is defined as damage accumulation and decreased fitness. Instead, the data suggest that the decreasing mortality in early life is a disparate effect, wherein all individuals begin to grow older already in very early life and continue aging throughout the lifespan, but some die early in life from non-aging related causes, even though phenotypically, these causes (e.g., heart disease, cancer, and infectious diseases) may appear similar at young and old ages. One of the strongest existing biomarkers of aging is represented by the age-dependent changes in DNA methylation. Epigenetic clocks were developed for humans and mice and allow for measurement of the biological age of tissues with high precision (Horvath, 2013Horvath S. DNA methylation age of human tissues and cell types.Genome Biol. 2013; 14: R115Crossref PubMed Scopus (3039) Google Scholar, Petkovich et al., 2017Petkovich D.A. Podolskiy D.I. Lobanov A.V. Lee S.-G. Miller R.A. Gladyshev V.N. Using DNA methylation profiling to evaluate biological age and longevity interventions.Cell Metab. 2017; 25: 954-960.e6Abstract Full Text Full Text PDF PubMed Scopus (173) Google Scholar, Stubbs et al., 2017Stubbs T.M. Bonder M.J. Stark A.-K. Krueger F. von Meyenn F. Stegle O. Reik W. BI Ageing Clock TeamMulti-tissue DNA methylation age predictor in mouse.Genome Biol. 2017; 18: 68Crossref PubMed Scopus (203) Google Scholar). The rates of epigenetic aging are associated with such parameters as sex, race, birthweight, and birth by caesarean section, as well as developmental characteristics and risk factors for aging-related diseases (Horvath et al., 2016Horvath S. Gurven M. Levine M.E. Trumble B.C. Kaplan H. Allayee H. Ritz B.R. Chen B. Lu A.T. Rickabaugh T.M. et al.An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease.Genome Biol. 2016; 17: 171Crossref PubMed Scopus (380) Google Scholar, Marioni et al., 2019Marioni R.E. Suderman M. Chen B.H. Horvath S. Bandinelli S. Morris T. Beck S. Ferrucci L. Pedersen N.L. Relton C.L. Tracking the epigenetic clock across the human life course: a meta-analysis of longitudinal cohort data.J. Gerontol. A Biol. Sci. Med. Sci. 2019; 74: 57-61Crossref PubMed Scopus (49) Google Scholar, Simpkin et al., 2016Simpkin A.J. Hemani G. Suderman M. Gaunt T.R. Lyttleton O. Mcardle W.L. Ring S.M. Sharp G.C. Tilling K. Horvath S. et al.Prenatal and early life influences on epigenetic age in children: a study of mother-offspring pairs from two cohort studies.Hum. Mol. Genet. 2016; 25: 191-201Crossref PubMed Scopus (126) Google Scholar, Simpkin et al., 2017Simpkin A.J. Howe L.D. Tilling K. Gaunt T.R. Lyttleton O. McArdle W.L. Ring S.M. Horvath S. Smith G.D. Relton C.L. The epigenetic clock and physical development during childhood and adolescence: longitudinal analysis from a UK birth cohort.Int. J. Epidemiol. 2017; 46: 549-558PubMed Google Scholar, Slieker et al., 2016Slieker R.C. van Iterson M. Luijk R. Beekman M. Zhernakova D.V. Moed M.H. Mei H. van Galen M. Deelen P. Bonder M.J. et al.BIOS consortiumAge-related accrual of methylomic variability is linked to fundamental ageing mechanisms.Genome Biol. 2016; 17: 191Crossref PubMed Scopus (88) Google Scholar). We analyzed the pace of multi-tissue epigenetic clocks in humans and mice during the period spanning the entire lifespan of these organisms. Similar to the process of cancer mutation accumulation, average DNA methylation changed monotonically in humans (Figure 2B). Consistent with Horvath, 2013Horvath S. DNA methylation age of human tissues and cell types.Genome Biol. 2013; 14: R115Crossref PubMed Scopus (3039) Google Scholar, the changes in weighted average DNA methylation proceeded faster during early life and slowed down during adulthood, with no evidence of U-shaped patterns. Age-dependent patterns of DNA methylation in mice appeared to mimic those in humans (Figure 2C). We further tested whether standard deviation of DNA methylation age in samples from the same age cohort for young mice and humans is larger than for adults. We found that it also grows monotonically with chronological age (Figure S2C). Taken together with the patterns of mutations, the DNA methylation data further point to continuous aging of organisms and damage accumulation in them from early development rather than from reproductive age. If organisms continuously and monotonically accumulate deleterious changes during early life, then there must be other mechanisms explaining the puzzling pattern of initially high and declining mortality during development. As many gene knockouts are known to lead to early-life mortality, we investigated the patterns of knockout lethality in detail. The International Mouse Phenotyping Consortium (IMPC) collected data on 2,808 individual gene knockouts in mice, 34% of which are associated with abnormal survival (Koscielny et al., 2014Koscielny G. Yaikhom G. Iyer V. Meehan T.F. Morgan H. Atienza-Herrero J. Blake A. Chen C.-K. Easty R. Di Fenza A. et al.The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data.Nucleic Acids Res. 2014; 42: D802-D809Crossref PubMed Scopus (192) Google Scholar). Using lethality windows established for 242 knockouts from the IMPC dataset (Dickinson et al., 2016Dickinson M.E. Flenniken A.M. Ji X. Teboul L. Wong M.D. White J.K. Meehan T.F. Weninger W.J. Westerberg H. Adissu H. et al.International Mouse Phenotyping ConsortiumJackson LaboratoryInfrastructure Nationale PHENOMIN, Institut Clinique de la Souris (ICS)Charles River LaboratoriesMRC HarwellToronto Centre for PhenogenomicsWellcome Trust Sanger InstituteRIKEN BioResource CenterHigh-throughput discovery of novel developmental phenotypes.Nature. 2016; 537: 508-514Crossref PubMed Scopus (639) Google Scholar), we calculated the age-related patterns of lethality associated with gene knockouts (Figure 3A). Most lethal-knockout mice are characterized by abnormal survival before and at mid-gestation; lethality is reduced dramatically afterward, including the period after birth. As the analyzed knockout models represent particular genotypes that are viable in the parent’s heterozygous state but lethal in the homozygous offspring, the data suggest that mortality during development is explained, in part, by parental genotype (Dickinson et al., 2016Dickinson M.E. Flenniken A.M. Ji X. Teboul L. Wong M.D. White J.K. Meehan T.F. Weninger W.J. Westerberg H. Adissu H. et al.International Mouse Phenotyping ConsortiumJackson LaboratoryInfrastructure Nationale PHENOMIN, Institut Clinique de la Souris (ICS)Charles River LaboratoriesMRC HarwellToronto Centre for PhenogenomicsWellcome Trust Sanger InstituteRIKEN BioResource CenterHigh-throughput discovery of novel developmental phenotypes.Nature. 2016; 537: 508-514Crossref PubMed Scopus (639) Google Scholar, Meehan et al., 2017Meehan T.F. Conte N. West D.B. Jacobsen J.O. Mason J. Warren J. Chen C.-K. Tudose I. Relac M. Matthews P. et al.International Mouse Phenotyping ConsortiumDisease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium.Nat. Genet. 2017; 49: 1231-1238Crossref PubMed Scopus (152) Google Scholar). Enrichment analysis of genes with lethality at different stages of development shows that mortality during early gestation is associated with regulation of transcription and translation, chromosomal organization, and regulation of the cell cycle and with regulation of organogenesis at mid-gestation (Figure S3A). Similar to the evolutionary costs of complete gene knockout, there is an active selection against heterozygous loss of gene function, which can be assessed by the distribution of selection coefficients for heterozygous protein-truncating variants (Cassa et al., 2017Cassa C.A. Weghorn D. Balick D.J. Jordan D.M. Nusinow D. Samocha K.E. O’Donnell-Luria A. MacArthur D.G. Daly M.J. Beier D.R. Sunyaev S.R. Estimating the selective effects of heterozygous protein-truncating variants from human exome data.Nat. Genet. 2017; 49: 806-810Crossref PubMed Scopus (71) Google Scholar). Moreover, lower heterozygous selection coefficients are associated with decreased severity of associated diseases and later age of their onset. The frequencies of deleterious alleles change according to the theory of evolution of dominance (Fisher, 1931Fisher R.A. The evolution of dominance.Biol. Rev. Camb. Philos. Soc. 1931; 6: 345-368Crossref Scopus (228) Google Scholar, Wright, 1931Wright S. Evolution in Mendelian populations.Genetics. 1931; 16: 97-159Crossref PubMed Google Scholar). When the effect of the heterozygous loss of function on fitness is stronger than the rate of genetic drift, these alleles are selected against in the population. This makes homozygotes for this allele occur extremely rarely, and selection happens almost entirely through heterozygotes. We found that selection against heterozygous knockouts of human genes orthologous to lethal mouse homozygous knockouts is such that it remains high in the prenatal stage and decreases afterward, being lowest for the knockouts that are viable in mice or humans (Figure 3B). This observation and the well-established contribution of consanguinity to early-life mortality in human populations further point to the role of parental genotype in selection against deleterious alleles during development. The decreasing lethality during development is consistent with the decrease in the number of essential genes that are expressed for the first time during development (Figure S3B). Thus, it appears that the damaging alleles start causing deleterious changes that lead to lethality when they are first expressed. Indeed, we calculated selection coefficients for genes expressed for the first time at each stage of development and found that the coefficients are higher during the early stages and decrease afterward (Figure 3C). When genes expressed during embryonic development were clustered, the main clusters corresponded to the genes first expressed very early during embryogenesis and at mid-gestation (Figure S3C). Similar to the mortality patterns of homozygous gene knockouts in mice (Figure 3A), early-life mortality is highest during the prenatal stages in humans (Figure 3D), with congenital malformations being the lead cause (Figures 1C and S1A). It is also well established that early-life mortality is associated with aneuploidy and other chromosomal abnormalities. It is instructive to understand what the relative contributions of chromosomal abnormalities and organismal genotype to early-life mortality are. While the two cannot be fully separated (e.g., genotype may influence the aneuploidy rate), the contribution of parental genotypes to the offspring genotype is fixed at conception and does not change with age, whereas chromosomal abnormalities are expected to increase with maternal age. Indeed, we found that maternal age is correlated with an increased spontaneous abortion rate (Figure 3E), and the fraction of abnormally karyotyped abortions is also increased with age (Figure 3F) (primary data from Hassold and Chiu, 1985Hassold T. Chiu D. Maternal age-specific rates of numerical chromosome abnormalities with special reference to trisomy.Hum. Genet. 1985; 70: 11-17Crossref PubMed Scopus (378) Google Scholar). Abnormal karyotypes account for up 75% of spontaneous abortions in older mothers but only 35% in young mothers. These data suggest that a substantial fraction of early-life mortality is unrelated to chromosomal abnormalities or other factors dependent on maternal age, further pointing to the substantial role of damaging mutations. We should point out, however, that using these data, we cannot determine the exact contribution of each factor or distinguish between maternal and paternal effects, since maternal and paternal ages correlate. To examine patterns of transmission of parental damaging mutations to offspring, we considered transmission of ultrarare gene-disruptive mutations in families with at least two siblings, among which one is affected by autism and another one is not (Iossifov et al., 2015Iossifov I. Levy D. Allen J. Ye K. Ronemus M. Lee Y.H. Yamrom B. Wigler M. Low load for disruptive mutations in autism genes and their biased transmission.Proc. Natl. Acad. Sci. USA. 2015; 112: E5600-E5607Crossref PubMed Scopus (94) Google Scholar). This approach that applied to the autism dataset was used as a proxy for the transmission of mutations that lead to early-life mortality. This analysis pointed to increased transmission of damaging mutations (relative to that of parents), most notably mutations in essential and long genes, to autistic children, which is a known effect (Iossifov et al., 2015Iossifov I. Levy D. Allen J. Ye K. Ronemus M. Lee Y.H. Yamrom B. Wigler M. Low load for disruptive mutations in autism genes and their biased transmission.Proc. Natl. Acad. Sci. USA. 2015; 112: E5600-E5607Crossref PubMed Scopus (94) Google Scholar), but also to a possible undertransmission of certain parental ultrarare damaging mutations to healthy siblings (Table 1).Table 1Transmission of Parental Damaging Mutations to Autistic Children and Their Healthy SiblingsGenesTo SiblingNot to SiblingTo Autistic ChildNot to Autistic ChildOdds RatioFisher’s Exact Test p Value for Undertransmission to SiblingAll genes10,0049,59210,1189,484––Genes from mother7678238437470.920.03Long genes7468118397180.900.02Essential genes1141441381200.840.03The table shows transmission of ultrarare gene-disruptive mutations in indicated types of genes in families with at least two siblings, one of which is affected by autism and the other is not. Previously reported data (Iossifov et al., 2015Iossifov I. Levy D. Allen J. Ye K. Ronemus M. Lee Y.H. Yamrom B. Wigler M. Low load for disruptive mutations in autism genes and their biased transmission.Proc. Natl. Acad. Sci. USA. 2015; 112: E5600-E5607Crossref PubMed Scopus (94) Google Scholar) were used for the assessment of transmission of mutations. Open table in a new tab The table shows transmission of ultrarare gene-disruptive mutations in indicated types of genes in families with at least two siblings, one of which is affected by autism and the other is not. Previously reported data (Iossifov et al., 2015Iossifov I. Levy D. Allen J. Ye K. Ronemus M. Lee Y.H. Yamrom B. Wigler M. Low load for disruptive mutations in autism genes and their biased transmission.Proc. Natl. Acad. Sci. USA. 2015; 112: E5600-E5607Crossref PubMed Scopus (94) Google Scholar) were used for the assessment of transmission of mutations. Thus, parental damaging mutations are unequally inherited by the viable offspring due to the early loss of embryos with highly damaging mutations or their combinations. The offspring has a greater risk of death after the time the genes carrying these mutations are first expressed. This contributes to the high early-life mortality, which decreases as organisms develop. In turn, the data show that the overall mortality is the sum of the initially high and decreasing developmental mortality and the ini" @default.
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• W2997787910 date "2019-12-01" @default.
• W2997787910 modified "2023-10-16" @default.
• W2997787910 title "Patterns of Aging Biomarkers, Mortality, and Damaging Mutations Illuminate the Beginning of Aging and Causes of Early-Life Mortality" @default.
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• W2997787910 doi "https://doi.org/10.1016/j.celrep.2019.11.091" @default.
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