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Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits. The Finnish population is enriched for genetic variants which are rare in other populations. Here, the authors find new genetic loci associated with 1391 circulating metabolites in 6136 Finnish men, demonstrating that metabolite genetic associations can help elucidate disease mechanisms.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data.

Analysis of whole-genome sequencing data from 163 vervet monkeys from Africa and the Caribbean shows high diversity among taxa and identifies signatures of selection. Selection signals affect viral processes, and genes that show response to SIV in vervets but not macaques have elevated selection scores. Vervet monkeys are among the most widely distributed nonhuman primates, show considerable phenotypic diversity, and have long been an important biomedical model for a variety of human diseases and in vaccine research. Using whole-genome sequencing data from 163 vervets sampled from across Africa and the Caribbean, we find high diversity within and between taxa and clear evidence that taxonomic divergence was reticulate rather than following a simple branching pattern. A scan for diversifying selection across taxa identifies strong and highly polygenic selection signals affecting viral processes. Furthermore, selection scores are elevated in genes whose human orthologs interact with HIV and in genes that show a response to experimental simian immunodeficiency virus (SIV) infection in vervet monkeys but not in rhesus macaques, suggesting that part of the signal reflects taxon-specific adaptation to SIV.

Nelson Freimer and colleagues report the first genome-wide association study of a longitudinal birth cohort (the Northern Finland Birth Cohort 1966). The results include new associations for nine quantitative metabolic traits. Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 ( LXRA ), low-density lipoprotein with AR and FADS1 - FADS2 , glucose with MTNR1B , and insulin with PANK1 . Two of these new associations emerged after adjustment of results for body mass index. Gene–environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has devastated health infrastructure around the world. Both ACE2 (an entry receptor) and TMPRSS2 (used by the virus for spike protein priming) are key proteins to SARS-CoV-2 cell entry, enabling progression to COVID-19 in humans. Comparative genomic research into critical ACE2 binding sites, associated with the spike receptor binding domain, has suggested that African and Asian primates may also be susceptible to disease from SARS-CoV-2 infection. Savanna monkeys (Chlorocebus spp.) are a widespread non-human primate with well-established potential as a bi-directional zoonotic/anthroponotic agent due to high levels of human interaction throughout their range in sub-Saharan Africa and the Caribbean. To characterize potential functional variation in savanna monkey ACE2 and TMPRSS2, we inspected recently published genomic data from 245 savanna monkeys, including 163 wild monkeys from Africa and the Caribbean and 82 captive monkeys from the Vervet Research Colony (VRC). We found several missense variants. One missense variant in ACE2 (X:14,077,550; Asp30Gly), common in Ch. sabaeus, causes a change in amino acid residue that has been inferred to reduce binding efficiency of SARS-CoV-2, suggesting potentially reduced susceptibility. The remaining populations appear as susceptible as humans, based on these criteria for receptor usage. All missense variants observed in wild Ch. sabaeus populations are also present in the VRC, along with two splice acceptor variants (at X:14,065,076) not observed in the wild sample that are potentially disruptive to ACE2 function. The presence of these variants in the VRC suggests a promising model for SARS-CoV-2 infection and vaccine and therapy development. In keeping with a One Health approach, characterizing actual susceptibility and potential for bi-directional zoonotic/anthroponotic transfer in savanna monkey populations may be an important consideration for controlling COVID-19 epidemics in communities with frequent human/non-human primate interactions that, in many cases, may have limited health infrastructure.

Abnormalities in sleep and circadian rhythms are central features of bipolar disorder (BP), often persisting between episodes. We report here, to our knowledge, the first systematic analysis of circadian rhythm activity in pedigrees segregating severe BP (BP-I). By analyzing actigraphy data obtained from members of 26 Costa Rican and Colombian pedigrees [136 euthymic (i.e., interepisode) BP-I individuals and 422 non–BP-I relatives], we delineated 73 phenotypes, of which 49 demonstrated significant heritability and 13 showed significant trait-like association with BP-I. All BP-I–associated traits related to activity level, with BP-I individuals consistently demonstrating lower activity levels than their non–BP-I relatives. We analyzed all 49 heritable phenotypes using genetic linkage analysis, with special emphasis on phenotypes judged to have the strongest impact on the biology underlying BP. We identified a locus for interdaily stability of activity, at a threshold exceeding genome-wide significance, on chromosome 12pter, a region that also showed pleiotropic linkage to two additional activity phenotypes.

What constitutes replication of a genotype–phenotype association, and how best can it be achieved? Gene association pitfalls Reviews of the many genetic association studies published recently give pause for thought: there are many false positives and questionable genotype–phenotype associations in the literature. A working group set up by the National Cancer Institute and National Human Genome Research Institute has been tackling the thorny question of what constitutes replication of a genotype–phenotype association, and the initial results are published this week. Guidelines on best practice for reporting initial and replication studies are presented. But it's clear that a series of studies is sometimes necessary to confirm critical genotype–phenotype associations.

The large and diverse population of Latin America is potentially a powerful resource for elucidating the genetic basis of complex traits through admixture mapping. However, no genome-wide characterization of admixture across Latin America has yet been attempted. Here, we report an analysis of admixture in thirteen Mestizo populations (i.e. in regions of mainly European and Native settlement) from seven countries in Latin America based on data for 678 autosomal and 29 X-chromosome microsatellites. We found extensive variation in Native American and European ancestry (and generally low levels of African ancestry) among populations and individuals, and evidence that admixture across Latin America has often involved predominantly European men and both Native and African women. An admixture analysis allowing for Native American population subdivision revealed a differentiation of the Native American ancestry amongst Mestizos. This observation is consistent with the genetic structure of pre-Columbian populations and with admixture having involved Natives from the area where the Mestizo examined are located. Our findings agree with available information on the demographic history of Latin America and have a number of implications for the design of association studies in population from the region.

Familial combined hyperlipidemia (FCH) is a complex and common familial dyslipidemia characterized by elevated total cholesterol and/or triglyceride levels with over five-fold risk of coronary heart disease. The genetic architecture and contribution of rare Mendelian and common variants to FCH susceptibility is unknown. In 53 Finnish FCH families, we genotyped and imputed nine million variants in 715 family members with DNA available. We studied the enrichment of variants previously implicated with monogenic dyslipidemias and/or lipid levels in the general population by comparing allele frequencies between the FCH families and population samples. We also constructed weighted polygenic scores using 212 lipid-associated SNPs and estimated the relative contributions of Mendelian variants and polygenic scores to the risk of FCH in the families. We identified, across the whole allele frequency spectrum, an enrichment of variants known to elevate, and a deficiency of variants known to lower LDL-C and/or TG levels among both probands and affected FCH individuals. The score based on TG associated SNPs was particularly high among affected individuals compared to non-affected family members. Out of 234 affected FCH individuals across the families, seven (3%) carried Mendelian variants and 83 (35%) showed high accumulation of either known LDL-C or TG elevating variants by having either polygenic score over the 90th percentile in the population. The positive predictive value of high score was much higher for affected FCH individuals than for similar sporadic cases in the population. FCH is highly polygenic, supporting the hypothesis that variants across the whole allele frequency spectrum contribute to this complex familial trait. Polygenic SNP panels improve identification of individuals affected with FCH, but their clinical utility remains to be defined.