@article {127, title = {Validation of a Genome-Wide Polygenic~Score for Coronary Artery~Disease in~South Asians.}, journal = {J Am Coll Cardiol}, volume = {76}, year = {2020}, month = {2020 08 11}, pages = {703-714}, abstract = {

BACKGROUND: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population.

OBJECTIVES: This analysis used summary statistics from a prior genome-wide association study to derive a new GPS for South Asians.

METHODS: This GPS was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPS reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPS reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India.

RESULTS: The GPS, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p~<~0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p~<~0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5\% of the GPS distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p~<~0.05 for each).

CONCLUSIONS: The new GPS has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.

}, keywords = {Adult, Aged, Bangladesh, Case-Control Studies, Coronary Artery Disease, Female, Genome-Wide Association Study, Humans, India, Male, Middle Aged, Multifactorial Inheritance}, issn = {1558-3597}, doi = {10.1016/j.jacc.2020.06.024}, author = {Wang, Minxian and Menon, Ramesh and Mishra, Sanghamitra and Patel, Aniruddh P and Chaffin, Mark and Tanneeru, Deepak and Deshmukh, Manjari and Mathew, Oshin and Apte, Sanika and Devanboo, Christina S and Sundaram, Sumathi and Lakshmipathy, Praveena and Murugan, Sakthivel and Sharma, Krishna Kumar and Rajendran, Karthikeyan and Santhosh, Sam and Thachathodiyl, Rajesh and Ahamed, Hisham and Balegadde, Aniketh Vijay and Alexander, Thomas and Swaminathan, Krishnan and Gupta, Rajeev and Mullasari, Ajit S and Sigamani, Alben and Kanchi, Muralidhar and Peterson, Andrew S and Butterworth, Adam S and Danesh, John and Di Angelantonio, Emanuele and Naheed, Aliya and Inouye, Michael and Chowdhury, Rajiv and Vedam, Ramprasad L and Kathiresan, Sekar and Gupta, Ravi and Khera, Amit V} } @article {139, title = {A variant-centric perspective on geographic patterns of human allele frequency variation.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 12 22}, abstract = {

A key challenge in human genetics is to understand the geographic distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, drawing inferences over many variants. Here, we introduce an alternative representation of genetic variation that reveals the relative abundance of different allele frequency patterns. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are found globally, regardless of whether the individuals are from the same region or different regions. Our variant-centric visualization clarifies the geographic patterns of human variation and can help address misconceptions about genetic differentiation among populations.

}, keywords = {Gene Frequency, Genetic Variation, Genetics, Population, Geography, Humans}, issn = {2050-084X}, doi = {10.7554/eLife.60107}, author = {Biddanda, Arjun and Rice, Daniel P and Novembre, John} } @article {103, title = {A vast resource of allelic expression data spanning human tissues.}, journal = {Genome Biol}, volume = {21}, year = {2020}, month = {2020 09 11}, pages = {234}, abstract = {

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

}, issn = {1474-760X}, doi = {10.1186/s13059-020-02122-z}, author = {Castel, Stephane E and Aguet, Fran{\c c}ois and Mohammadi, Pejman and Ardlie, Kristin G and Lappalainen, Tuuli} }