Abstract: De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains 〈 1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability ( h 2 ), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Abstract: The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

Abstract: Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

Abstract: We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Abstract: When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer’s disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.

Abstract: Alzheimer’s disease (AD) is a leading cause of mortality among the elderly. We performed a whole-genome sequencing study of AD in the Chinese population. In addition to the variants identified in or around the APOE locus (sentinel variant rs73052335, P = 1.44 × 10 −14 ), two common variants, GCH1 (rs72713460, P = 4.36 × 10 −5 ) and KCNJ15 (rs928771, P = 3.60 × 10 −6 ), were identified and further verified for their possible risk effects for AD in three small non-Asian AD cohorts. Genotype–phenotype analysis showed that KCNJ15 variant rs928771 affects the onset age of AD, with earlier disease onset in minor allele carriers. In addition, altered expression level of the KCNJ15 transcript can be observed in the blood of AD subjects. Moreover, the risk variants of GCH1 and KCNJ15 are associated with changes in their transcript levels in specific tissues, as well as changes of plasma biomarkers levels in AD subjects. Importantly, network analysis of hippocampus and blood transcriptome datasets suggests that the risk variants in the APOE , GCH1 , and KCNJ15 loci might exert their functions through their regulatory effects on immune-related pathways. Taking these data together, we identified common variants of GCH1 and KCNJ15 in the Chinese population that contribute to AD risk. These variants may exert their functional effects through the immune system.

Abstract: We report a whole-genome shotgun assembly (called WGSA) of the human genome generated at Celera in 2001. The Celera-generated shotgun data set consisted of 27 million sequencing reads organized in pairs by virtue of end-sequencing 2-kbp, 10-kbp, and 50-kbp inserts from shotgun clone libraries. The quality-trimmed reads covered the genome 5.3 times, and the inserts from which pairs of reads were obtained covered the genome 39 times. With the nearly complete human DNA sequence [National Center for Biotechnology Information (NCBI) Build 34] now available, it is possible to directly assess the quality, accuracy, and completeness of WGSA and of the first reconstructions of the human genome reported in two landmark papers in February 2001 [Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) Science 291, 1304–1351; International Human Genome Sequencing Consortium (2001) Nature 409, 860–921]. The analysis of WGSA shows 97% order and orientation agreement with NCBI Build 34, where most of the 3% of sequence out of order is due to scaffold placement problems as opposed to assembly errors within the scaffolds themselves. In addition, WGSA fills some of the remaining gaps in NCBI Build 34. The early genome sequences all covered about the same amount of the genome, but they did so in different ways. The Celera results provide more order and orientation, and the consortium sequence provides better coverage of exact and nearly exact repeats.

Abstract: To address claims of human exceptionalism, we determine where humans fit within the greater mammalian distribution of reproductive inequality. We show that humans exhibit lower reproductive skew (i.e., inequality in the number of surviving offspring) among males and smaller sex differences in reproductive skew than most other mammals, while nevertheless falling within the mammalian range. Additionally, female reproductive skew is higher in polygynous human populations than in polygynous nonhumans mammals on average. This patterning of skew can be attributed in part to the prevalence of monogamy in humans compared to the predominance of polygyny in nonhuman mammals, to the limited degree of polygyny in the human societies that practice it, and to the importance of unequally held rival resources to women’s fitness. The muted reproductive inequality observed in humans appears to be linked to several unusual characteristics of our species—including high levels of cooperation among males, high dependence on unequally held rival resources, complementarities between maternal and paternal investment, as well as social and legal institutions that enforce monogamous norms.

Abstract: The global precipitation EOF1 shows a more complex spatial response than the global temperature EOF1, whereas the initial increase in the associated PC1 significantly lags the initial increase in the global temperature PC1 and exhibits greater millennial-scale structure than seen in the global temperature PC1 ( Fig. P1 ). Insofar as precipitation increases should accompany a warming planet, the approximately 2-ky lag between the initial increase in temperature and precipitation may reflect one or more mechanisms that affect low-latitude hydrology, including the impact of Oldest Dryas cooling, a nonlinear response to Northern Hemisphere forcing by insolation and glacial boundary conditions, or interhemispheric latent heat transports. This response may then have been modulated by subsequent millennial-scale changes in the AMOC and its attendant effects on African and Asian monsoon systems and the position of the Intertropical Convergence Zone and North American storm tracks. In contrast, the global temperature PC2 is remarkably similar to a North Atlantic Pa/Th record ( r 2  = 0.86) ( Fig. P1 ) that is interpreted as a kinematic proxy for the strength of the AMOC ( 3 ). Similar millennial-scale variability is identified in several other proxies of intermediate- and deep-ocean circulation, identifying a strong coupling between SSTs and ocean circulation. The large reduction in the AMOC during the Oldest Dryas can be explained as a response to the freshwater forcing associated with the 19-ka meltwater pulse from Northern Hemisphere ice sheets, Heinrich event 1, and routing events along the southern Laurentide Ice Sheet margin, whereas the reduction during the Younger Dryas was likely caused by freshwater routing through the St. Lawrence River and Heinrich event 0. The sustained strength of the AMOC following meltwater pulse 1a supports arguments for a large contribution of this event from Antarctica . With EOF2 accounting for only 13% of deglacial global climate variability, we conclude that the direct global impact of AMOC variations was small in comparison to other processes operating during the last deglaciation. Our analysis indicates that the superposition of two orthogonal modes explains much of the variability (64–100%) in regional and global climate during the last deglaciation ( Fig. P1 ). The nearly uniform spatial pattern of the global temperature EOF1 and the large magnitude of the temperature principal component 1 (PC1) variance indicate that this mode reflects the global warming of the last deglaciation. Given the large global forcing of greenhouse gases (GHGs) ( 2 ), the strong correlation between PC1 and the combined GHG forcing ( r 2  = 0.97) ( Fig. P1 ) supports arguments that GHGs were a major driver of global warming. Fig. P1. ( A ) Comparison of the global temperature PC1 (blue line, with confidence intervals showing results of jackknifing procedure for 68% and 95% of records removed) with record of atmospheric CO 2 from European Project for Ice Coring in Antarctica Dome C ice core (red line with age uncertainty) ( 4 ) on revised timescale from ref.  5 . ( B ) Comparison of the global temperature PC2 (blue line, with confidence intervals showing results of jackknifing procedure for 68% and 95% of records removed) with Pa/Th record (a proxy for Atlantic meridional overturning circulation) ( 3 ) (green and purple symbols). Also shown are freshwater fluxes from ice-sheet meltwater, Heinrich events, and routing events. ( C ) Comparison of the global precipitation PC1 (blue line) with record of methane (green line) and radiative forcing from greenhouse gases (red line). OD, Oldest Dryas; BA, Bølling—Allerød; YD, Younger Dryas; MWP, meltwater pulse. We used empirical orthogonal functions (EOFs) to provide an objective characterization of the temporal and spatial patterns of the leading modes of global surface climate variability for the 20- to 11-ka interval as derived from 166 published proxy records. In addition to characterizing sea surface temperature (SST) variability, we also characterize variability in regional and global continental temperature and precipitation, as well as derive a composite of global temperature variability. The low concentrations of atmospheric CO 2 during the LGM are thought to have been caused by greater storage of carbon in the deep ocean through stratification of the Southern Ocean ( 1 ). Release of the sequestered carbon may have occurred due to deep Southern Ocean overturning induced by enhanced wind-driven upwelling and sea-ice retreat associated with times of Antarctic warming, coincident with the Oldest and Younger Dryas cold events in the north. Several proxies identify large changes in the volume and circulation of the major water masses that fill the deep ocean. During the LGM, there was a marked division in the Atlantic, Indian, and Pacific oceans separating shallower, nutrient-poor intermediate water from more nutrient-rich deep water. In the North Atlantic, Antarctic Bottom Water expanded northward and upward at the expense of North Atlantic Deep Water (NADW), while both water masses maintained a vigorous circulation. In the southwest Pacific and the Arabian Sea, there was an increased influence of Antarctic Intermediate Water (AAIW). During the subsequent deglaciation, there was a net decrease of the Atlantic meridional overturning circulation (AMOC) below LGM strength during the Oldest Dryas, renewed production of NADW at the start of the Bølling–Allerød, followed by a subsequent decrease during the Younger Dryas. In the southwest Pacific and the Arabian Sea, the influence of AAIW further increased during the Oldest Dryas, decreased again during the Bølling–Allerød, and subsequently increased during the Younger Dryas. In contrast, intermediate-depth sites in the southeast Pacific suggest greatest expansion of AAIW during the LGM, followed by stepwise reduction between 17 and 11 ka. Deciphering the evolution of global climate from the end of the Last Glacial Maximum (LGM) approximately 19 ka to the early Holocene 11 ka presents an outstanding opportunity for understanding the transient response of Earth’s climate system to external and internal forcings. During this interval of global warming, virtually every component of the climate system underwent large-scale change, sometimes at extraordinary rates, as the world emerged from the grips of the last ice age. This dramatic time of global change was triggered by changes in insolation, with associated changes in ice sheets, greenhouse gas concentrations, and other amplifying feedbacks that produced distinctive regional and global responses. In addition, there were several episodes of large and rapid sea-level rise and abrupt climate change that produced regional climate signals superposed on those associated with global warming. Considerable ice-sheet melting and sea-level rise occurred after 11 ka, but otherwise the world had entered the current interglaciation with near-pre-Industrial greenhouse gas concentrations and relatively stable climates. Here we summarize a major effort by the paleoclimate research community to characterize these changes through the development of well-dated, high-resolution records of the deep and intermediate ocean as well as surface climate.

Abstract: Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.